# CtrlB CtrlB is an observability control plane that unifies ingestion, routing, retention, and query for logs, metrics, and traces so platform teams can operate OpenTelemetry-backed telemetry reliably with operator-focused workflows and faster paths from incident to evidence. ## Metadata - **Canonical HTML**: https://ctrlb.ai/ - **Last updated**: 2026-05-01 ## Product pillars | Area | What CtrlB delivers | |------|---------------------| | Telemetry | Ingestion, routing, and retention for logs, metrics, and traces | | Query | Fast exploration with SQL / PromQL-friendly workflows | | Platform | Centralized collectors, policies, and integrations | ## Explore further - **Blog**: [Latest articles](https://ctrlb.ai/blogs) - **Pricing**: [Plans](https://ctrlb.ai/pricing) - **Docs**: [docs.ctrlb.ai](https://docs.ctrlb.ai/) - **Markdown mirror**: Fetch `/index.md` for this page as plain Markdown --- # CtrlB Blog The CtrlB Blog publishes editorial and technical articles on observability strategy, OpenTelemetry instrumentation, telemetry pipelines, retention economics, and production operations lessons aimed at engineers who care about reliability, cost, and data quality. ## Metadata - **Canonical HTML**: https://ctrlb.ai/blogs - **Last updated**: 2026-05-01 ## Browse | Goal | Path | |------|------| | All posts | HTML listing at `/blogs` | | RSS | [`/rss.xml`](https://ctrlb.ai/rss.xml) | | Tags | [`/blogs/tag/`](https://ctrlb.ai/blogs/tag/kubernetes) | | Authors | [`/blogs/author/`](https://ctrlb.ai/blogs/author/adarsh-srivastava) | ## Example: inspect the RSS feed ```bash curl -fsSL "https://ctrlb.ai/rss.xml" | head -n 40 ``` ## Markdown mirrors Each article has `/blogs/.md`; tag and author hubs expose sibling Markdown (.md) twins as well. --- # Pricing CtrlB Pricing explains the commercial model for teams evaluating, growing, or standardizing on the observability control plane, including a free evaluation tier, usage-based Pro pricing tied to retained gigabytes, and Enterprise options with volume discounts and dedicated support. ## Metadata - **Canonical HTML**: https://ctrlb.ai/pricing - **Last updated**: 2026-05-01 ## Plans (summary) | Plan | Highlights | |------|------------| | Free | Logs, traces, metrics; starter retention limits | | Pro | Strong default retention; Slack/Teams support; usage-based GB pricing | | Enterprise | Custom retention, discounts, migration assistance | ## Next steps - [Book a demo](https://ctrlb.ai/booking) - [Contact sales](https://ctrlb.ai/contact) Canonical HTML remains authoritative for legal numeric pricing. --- # About CtrlB About CtrlB introduces the team and mission behind the observability control plane, emphasizing centralized collector management, faster query ergonomics, and reliability practices for organizations ingesting high-cardinality telemetry across hybrid environments. ## Metadata - **Canonical HTML**: https://ctrlb.ai/about-us - **Last updated**: 2026-05-01 ## What we believe | Principle | Detail | |-----------|--------| | Standards-first | OpenTelemetry and portable telemetry formats | | Operator UX | Fast paths from incident → evidence → fix | | Scale | Built for high-cardinality and large ingest | ## Links - [Careers via LinkedIn](https://www.linkedin.com/company/ctrlb-hq/jobs/) - [Blog](https://ctrlb.ai/blogs) --- # Contact CtrlB The Contact page is the structured entry point for demo requests, architecture reviews, customer support escalations, partnership conversations, careers outreach, and other operational discussions with CtrlB via the web form and linked social channels. ## Metadata - **Canonical HTML**: https://ctrlb.ai/contact - **Last updated**: 2026-05-01 ## Channels | Channel | Best for | |---------|-----------| | Web form | Structured requests via the Contact page | | [LinkedIn](https://www.linkedin.com/company/ctrlb-hq) | Company updates | | [GitHub](https://github.com/ctrlb-hq) | Open-source components | ## Related - [Book a demo](https://ctrlb.ai/booking) - [Documentation](https://docs.ctrlb.ai/) --- # Live debugger The CtrlB Live Debugger connects VS Code to production workloads so engineers can inspect variables and execution paths while traffic keeps flowing, capturing tightly scoped snapshots instead of stopping threads or attaching heavyweight interactive debuggers. ## Metadata - **Canonical HTML**: https://ctrlb.ai/live-debugger - **Last updated**: 2026-05-01 ## Workflow | Step | Detail | |------|--------| | Install | VS Code marketplace extension | | Instrument | Follow onboarding in docs.ctrlb.ai | | Capture | Pull live snapshots while traffic flows | ## Example: fetch this page as Markdown ```bash curl -fsSL "https://ctrlb.ai/live-debugger.md" | head -n 60 ``` ## Links - [VS Code Marketplace — CtrlB](https://marketplace.visualstudio.com/items?itemName=CtrlB.CtrlB) - [Documentation](https://docs.ctrlb.ai/) --- # Observability control plane The CtrlB observability control plane provides centralized orchestration for OpenTelemetry collectors so teams can roll out routing rules, health monitoring, and policy-driven forwarding consistently across fleets instead of editing YAML independently on every host. ## Metadata - **Canonical HTML**: https://ctrlb.ai/control-plane - **Last updated**: 2026-05-01 ## Capabilities | Capability | Notes | |------------|-------| | Collector orchestration | Push configs, observe fleet health | | Routing | Fan-out / filtering policies | | OSS alignment | ctrlb-control-plane on GitHub | ## Example: OTLP-first collector skeleton ```yaml receivers: otlp: protocols: grpc: http: exporters: otlphttp: endpoint: https://ingest.example.com/v1/logs service: pipelines: logs: receivers: [otlp] exporters: [otlphttp] ``` ## Links - Repository: [ctrlb-control-plane](https://github.com/ctrlb-hq/ctrlb-control-plane) - [Documentation](https://docs.ctrlb.ai/) --- # Sign up The Sign up page collects requests for CtrlB Flow early access and related beta programs so product and customer teams can prioritize onboarding steps, communicate feature availability, and align enablement timelines with each organization’s rollout plan. ## Metadata - **Canonical HTML**: https://ctrlb.ai/sign-up - **Last updated**: 2026-05-01 ## Expectations | Step | Detail | |------|--------| | Submit | Form on the Sign up page | | Review | Team follows up with onboarding steps | | Enable | Feature flags vary by program | ## Related - [Get started meeting](https://ctrlb.ai/get-started) - [Documentation](https://docs.ctrlb.ai/) --- # Get started Get started is the meeting-oriented path for stakeholders to align on telemetry sources, ingestion architecture, retention targets, alerting needs, and phased adoption of the CtrlB control plane before deeper implementation work begins in production environments. ## Metadata - **Canonical HTML**: https://ctrlb.ai/get-started - **Last updated**: 2026-05-01 ## Agenda ideas | Topic | Why it matters | |-------|----------------| | Data paths | Logs/metrics/traces sources | | Storage | Retention vs cost | | Query | Teams’ dashboards & alerts | ## Related - [Book a demo](https://ctrlb.ai/booking) - [Pricing](https://ctrlb.ai/pricing) --- # Book a demo Book a demo lets teams schedule a guided CtrlB walkthrough with space to discuss ingest volumes, existing agents and collectors, backend constraints, and concrete goals around latency, retention, compliance, or infrastructure spend. ## Metadata - **Canonical HTML**: https://ctrlb.ai/booking - **Last updated**: 2026-05-01 ## What to prepare | Material | Detail | |----------|--------| | Volume | Rough ingest TB/day | | Stack | Agents, collectors, backends | | Goals | Latency, retention, compliance | ## Related - [Contact](https://ctrlb.ai/contact) - [Pricing](https://ctrlb.ai/pricing) --- # Privacy policy (summary mirror) This Markdown mirror summarizes themes commonly covered in CtrlB’s privacy policy for rapid agent ingestion, while the canonical HTML privacy page remains legally authoritative for collection practices, retention periods, processor relationships, and regional privacy rights. ## Metadata - **Canonical HTML**: https://ctrlb.ai/privacy - **Last updated**: 2026-05-01 ## Topics typically covered | Topic | Notes | |-------|-------| | Collection | Forms, analytics, product telemetry | | Retention | Policy-driven storage durations | | Rights | Access/deletion where applicable | Open **Canonical HTML** for definitive wording. --- # Terms of service (summary mirror) This Markdown mirror sketches the scope of CtrlB’s terms of service for tooling workflows, but the HTML terms page remains binding for acceptable use, warranties, liability caps, indemnities, and how contractual updates are communicated to customers. ## Metadata - **Canonical HTML**: https://ctrlb.ai/terms - **Last updated**: 2026-05-01 ## Scope | Area | Notes | |------|-------| | Acceptable use | Product-specific constraints | | Liability | Caps and disclaimers | | Changes | Notification mechanics | Consult lawyers using the HTML page. --- # CtrlB Decompose CtrlB Decompose is a browser and command-line toolkit that compresses noisy logs into reusable structural templates, sketch-backed statistics, and correlation hints using an interactive WASM demo plus automation-friendly CLI workflows on developer machines and CI runners. ## Metadata - **Canonical HTML**: https://ctrlb.ai/decompose - **Last updated**: 2026-05-01 ## Interfaces | Surface | Detail | |---------|--------| | WASM demo | Hosted under `/decompose` | | CLI | Install via releases / package managers | | Docs | [`/decompose/docs`](https://ctrlb.ai/decompose/docs) | ## Repository [github.com/ctrlb-hq/ctrlb-decompose](https://github.com/ctrlb-hq/ctrlb-decompose) --- # CtrlB Decompose documentation This documentation describes CtrlB Decompose’s multi-stage pipeline from raw noisy logs through timestamp normalization, CLP encoding, Drain3 clustering, typed variables such as IPv4 and UUIDs, DDSketch quantiles, HyperLogLog cardinality sketches, and anomaly-oriented cues. ## Metadata - **Canonical HTML**: https://ctrlb.ai/decompose/docs - **Last updated**: 2026-05-01 ## Stage overview | Stage | Purpose | |-------|---------| | Parsing | Normalize timestamps & tokens | | Encoding | CLP compression prep | | Clustering | Drain3 grouping | | Stats | Quantiles & cardinality sketches | ## Example: CLI invocation ```bash ctrlb-decompose --help cat ./samples/*.log | ctrlb-decompose analyze --json > patterns.json ``` ## Links - [Interactive demo](https://ctrlb.ai/decompose) - [GitHub](https://github.com/ctrlb-hq/ctrlb-decompose) --- # Site map The site map page is a human-friendly index linking major marketing routes, blog taxonomy entry points, and machine-readable discovery surfaces including the XML sitemap, RSS feed, llms.txt catalog, and AGENTS.md orientation for automated agents. ## Metadata - **Canonical HTML**: https://ctrlb.ai/site-map - **Last updated**: 2026-05-01 ## Discovery endpoints | Resource | URL | |----------|-----| | Sitemap XML | https://ctrlb.ai/sitemap.xml | | RSS | https://ctrlb.ai/rss.xml | | llms.txt | https://ctrlb.ai/llms.txt | | AGENTS.md | https://ctrlb.ai/AGENTS.md | ## Sections on site-map page Browse marketing pages, blog taxonomy (tags/authors), and product highlights. --- --- title: "Unlocking Headless Observability: The Power of MCP" description: "Picture this: PagerDuty just woke you up. The billing service is throwing 500 errors, and customer checkouts are failing.  What is your next move?  If you are like most engineers, you are about to open a dozen tabs. You’ll pull up your logging platform, your tracing tool, maybe a security…" canonical: "https://ctrlb.ai/blogs/unlocking-headless-observability-the-power-of-mcp" publishedTime: "2026-03-23" modifiedTime: "2026-03-27T12:32:22+0000" author: "Adarsh Srivastava" tags: [] --- # Unlocking Headless Observability: The Power of MCP Picture this: PagerDuty just woke you up. The billing service is throwing 500 errors, and customer checkouts are failing.  **What is your next move?**  If you are like most engineers, you are about to open a dozen tabs. You’ll pull up your logging platform, your tracing tool, maybe a security dashboard, and start frantically writing complex query languages (that you half-remember) to hunt for the needle in the haystack.  But what if, instead of all that, you just opened Claude or your VS Code terminal and asked:  "Show me all critical alerts and error logs from the billing service over the past 10 minutes, and cross-reference them with the latest deployment."  And what if... it just gave you the answer?  No hallucinations. No generic troubleshooting steps. Just the exact root cause, pulled directly from your live production data.  Welcome to the future. **The Model Context Protocol (MCP) is officially LIVE on CtrlB**, and it is ushering in the era of **Headless Observability**.  ## **The Problem: Prediction Engines vs. Execution Engines**  Generative AI and coding assistants like Cursor and Windsurf have revolutionized how we write code. But when it comes to running and debugging systems in production, they hit a massive wall.  Fundamentally, **LLMs** like Claude or ChatGPT are **prediction engines**. They are incredibly smart at generating text and code based on their training data, but they cannot execute real-world tasks on their own. By default, an LLM cannot query your database, check your Slack messages, or pull a live metric trace.  Until now, bridging the gap between your AI tools and your live system telemetry meant copying and pasting massive walls of JSON into a chat window. It was slow, prone to token limits and a security nightmare. Before MCP, there was no standard way to connect AI with external tools. Every integration required custom, brittle API glue code.  Your AI was effectively a brilliant detective locked in a room without access to the crime scene.   ## **Enter MCP: The "REST API" for AI**  To fix this, the industry needed a universal standard. Think of the **Model Context Protocol (MCP)** as the REST API standard, but built specifically for AI agents.  Originally introduced by Anthropic as an open standard, MCP provides a unified protocol that enables AI models to interact seamlessly with external tools, systems, and data. It transforms LLMs from simple text generators into **action-performing agents**.  ## **Under the Hood: How MCP Works**  MCP utilizes a clean **Client-Server architecture**:  The Client: Your AI application or IDE (like Claude Desktop, Cursor, or VS Code).  The Server: A lightweight backend service (like the one CtrlB just launched) that exposes your private data and capabilities securely.  Building an MCP server is surprisingly simple for developers—you write normal backend functions and expose them to the protocol. MCP then supplies context to the AI model through four powerful primitives:  Tools (Functions): These enable the AI to execute tasks. For example, CtrlB exposes tools like query_logs(service, timeframe) or fetch_security_alerts(). The AI decides when and how to call these tools based on your natural language prompt.  Resources (Data): Structured access to static or dynamic data. This allows the AI to read your database schemas, configuration files, or raw log streams directly.  Prompts (Instructions): Reusable templates that standardise how the AI should behave or format its requests when interacting with specific domains.  Sampling (Model Orchestration): A more advanced feature allowing the server to request LLM completions through the client, enabling complex, multi-step agentic workflows securely.   ## **What is "Headless Observability"?**  By its very definition, "headless" means separating the core backend data engine from the presentation layer (the UI). For years, observability platforms forced you into their proprietary, expensive, and rigid dashboards.  CtrlB breaks that mold. Because we are a headless observability engine, we give you the freedom to consume your telemetry exactly how you want to:  Bring Your Own Visualization (BYOV): CtrlB integrates seamlessly with external visualization platforms like Grafana, Apache Superset and Preset. You get all the power of CtrlB's petabyte-scale backend without having to rip out the dashboards your team already knows and loves.  Machine-Native Consumption (via MCP): For the times you don't want to build or stare at a dashboard, our new MCP servers allow your AI agents to query, analyze, and understand your system's telemetry natively.  Your AI is no longer just a code generator. **It is now your lead incident investigator.**  ## **How CtrlB Supercharges Your AI Agents**  With our new MCP integration running in production, you can now seamlessly connect CtrlB to the tools your team already lives in (Claude, Cursor, Windsurf, VS Code).  But connecting AI to your data is one thing; doing it at an enterprise scale is another. When you use MCP with CtrlB, you unlock our core platform superpowers:  Petabyte-Scale Context: CtrlB’s architecture separates compute from storage. Your AI isn't just looking at the last 15 minutes of data; it can instantly search months of historical logs to establish baselines and find anomalies.  Zero Data Egress Taxes: Querying massive datasets usually results in terrifying cloud bills. Because CtrlB operates securely within your own cloud boundary, your AI can analyze infinite data without you paying a single cent in egress taxes.  Security: SecOps and SREs can breathe easy. Your data never leaves your environment, and CtrlB maintains strict access controls over exactly which Tools and Resources the AI can utilize.  ### **The Future Potential of MCP**  We are only scratching the surface. The widespread adoption of MCP is paving the way for a massive shift in software development.  Soon, we will see thriving **tool marketplaces** where developers can plug and play MCP servers into their AI assistants. We will see AI-driven automation where agents autonomously monitor systems, detect anomalies, write the patch, and deploy the fix all while logging their actions securely.  Let’s put this into perspective. Imagine you just connected your Cursor IDE to CtrlB via our new MCP server.  Instead of writing a complex regex query to find an issue, you simply type: "Why are database queries from the user-auth pod timing out and are there any active security alerts associated with those IPs?"  In seconds, the AI invokes the query_logs tool, reads the auth_schema resource, checks the security alerts, and tells you that a newly deployed config file is causing a connection leak.  ### **It’s not magic. It’s Headless Observability.**  --- --- title: "Architecture Deep Dive: Why CtrlB’s LSM Approach Outperforms MergeTrees for Observability. " description: "Introduction  ClickHouse is a remarkable piece of engineering. Its MergeTree engine has rightfully earned its place as the gold standard for OLAP, processing billions of rows to answer analytical questions like \"What was the average revenue last quarter?\" with incredible speed.  However,…" canonical: "https://ctrlb.ai/blogs/architecture-deep-dive" publishedTime: "2026-02-12" modifiedTime: "2026-03-27T11:57:23+0000" author: "Adarsh Srivastava" tags: [] --- # Architecture Deep Dive: Why CtrlB’s LSM Approach Outperforms MergeTrees for Observability.  **Introduction**  ClickHouse is a remarkable piece of engineering. Its MergeTree engine has rightfully earned its place as the gold standard for OLAP, processing billions of rows to answer analytical questions like "What was the average revenue last quarter?" with incredible speed.  However, observability is a fundamentally different challenge. When an engineer is debugging an outage, they aren't looking for averages. They are hunting for a needle in a haystack—a specific Trace ID, a unique error log, or a single causal event—hidden within a chaotic, bursty stream of high-cardinality data.  While ClickHouse attempts to adapt to this workload using sparse indexes and rigid parts, we built CtrlB from the ground up using a decoupled **Log-Structured Merge (LSM)** architecture. By treating storage, indexing, and coordination as independent components, we solve the specific pain points that traditional analytical databases struggle with.  Below is a breakdown of why our architecture—powered by S3, Postgres and NATS—provides a more resilient solution for observability workloads.     **1. Solved: The "Too Many Parts" Ingestion Bottleneck**  One of the most common frustration points for ClickHouse users is the dreaded DB::Exception: Too many parts error.  In the standard ClickHouse MergeTree model, data is written into directories called "parts" on the local disk. To maintain performance, the database requires writes to be heavily batched (e.g., 10,000 rows at a time). If an application streams real-time logs directly, it generates thousands of tiny parts. The background merger simply cannot keep up with this fragmentation, causing the database to lock up. This usually forces teams to manage complex external buffers, like Kafka, just to feed the database safely.  **The CtrlB Approach: A Multi-Stage Flush**  We decided to decouple ingestion completely using a tiered buffering strategy. This allows us to absorb massive traffic bursts without them ever impacting long-term storage stability.  The data flows through four distinct stages:  Memory (Mutable): Incoming data lands in a mutable in-memory buffer (Memtable), allowing for instant acknowledgement.  Immutable Memory: Once filled, this buffer freezes into an immutable state, while a new buffer is immediately created to handle fresh writes.  Local Disk: The immutable block is flushed to the local disk to ensure durability.  S3 Upload: Finally, these files are uploaded to Object Storage (S3) for permanent, cost-effective storage.  Crucially, we only update our metadata in Postgres (the file_list table) once the file is safely in S3. This architecture eliminates the "too many parts" failure mode.     **2. The Pico file (solving the compaction task)**  This is the most significant divergence between our architecture and traditional databases.  In a standard MergeTree, when background compaction triggers to merge several small files into a larger one, the database often has to re-read the raw data to rebuild the primary index. This is computationally expensive—essentially "work about work"—burning CPU and I/O re-processing logs that haven't changed.  **Our Innovation: Binary Index Merging**  CtrlB uses a dual-file format: the **Data File** (Parquet) and a separate **Primary Index** (which we call the **Pico** **File**).  When our Compactor merges small files, it doesn't touch the raw Parquet data. Instead, it reads the corresponding small Pico files. Because these index files are sorted binary sketches, the compactor can simply perform a binary merge on them directly.  Think of it as Index A + Index B = Index C.  By stitching these small indexes together without re-reading the heavy raw data, our compaction process becomes orders of magnitude lighter and faster. We avoid the heavy lifting that other systems perform when organizing old logs.  CtrlB's Architecture-  **3. Decoupled co-ordination - Postgres & NATS **  State management in distributed databases is notoriously difficult. Distributed coordination is the "hard part" of any database. Modern ClickHouse has moved to ClickHouse Keeper, a C++ implementation of the Raft consensus algorithm. While it removes the old Java dependencies, it still requires a tightly coupled consensus quorum. This means you must manage a "brain" of 3 or 5 nodes that must stay in perfect sync. If the Keeper nodes experience disk latency or network partitions, the entire cluster's ingestion and replication can freeze.    CtrlB takes a "Separation of Concerns" approach by using two industry-standard, independent tools:  We opted for a simpler, decoupled approach using industry-standard tools that live independently of the data nodes:  Postgres as the Source of Truth: We use two tables, file_list and index_list, to track files in S3 and their indexing status. When a file arrives, it is marked as "unindexed." An asynchronous indexer simply asks Postgres "What needs work?", processes the file, and updates the status.  NATS for Query Coordination: We don't rely on static shard maps. When a query arrives at a Leader node, it asks NATS "Who is available to work?". The work is then dispatched dynamically to available Worker nodes.  This separation allows us to scale our compute (Queriers) independently of our storage (S3) and our coordination logic.    **4. The “Needle in Haystack” query**  Finally, there is the query performance itself. ClickHouse uses a Sparse Primary Index that points to "Granules" (blocks of ~8192 rows). To find a specific log line, it often has to scan that entire block. This is excellent for range queries but mediocre for the unique point lookups common in observability (e.g., "Find this specific Trace ID").  Because CtrlB merges its Pico files into a clean tree structure, the root of our LSM tree can guide the query engine to the exact file and offset required. We don't scan unrelated data blocks; we traverse a lightweight index tree to jump straight to the data you need. **What’s Next**  In the next part of this series, we’ll share a detailed benchmark and architectural comparison between ClickHouse and CtrlB. We’ll look at ingestion performance, compaction efficiency, query latency, and operational complexity under real-world observability workloads.  Our goal is to move beyond theory and show measurable differences in how each system behaves at scale.  --- --- title: "We Raised $2.5M Seed Funding to build the Future of Search" description: "We Raised $2.5M Seed Funding to build the Future of Search" canonical: "https://ctrlb.ai/blogs/seed-funding" publishedTime: "2025-12-03" modifiedTime: "2026-03-27T12:05:32+0000" author: "Adarsh Srivastava" tags: [] --- # We Raised $2.5M Seed Funding to build the Future of Search CtrlB, the next-gen data lake for observability helping modern engineering and security teams take control of their telemetry data, announced that it has raised **USD 2.5 million in a Seed funding round** led by **Chiratae Ventures**. The round includes participation from **Equirus**, **InnovateX Fund**, **Campus Fund**, **Point One Capital.**  “Our mission is to make high-scale observability affordable, fast, and reliable for every engineering team. This investment will allow us to strengthen our platform, deepen R&D, and serve enterprises that need high-performance observability at scale. Our customers want better speed, better economics, and simpler operations. With this round, we’re doubling down on improving their experience end-to-end,” said **Adarsh Srivastava**, Founder and CEO of CtrlB.  This round is especially meaningful because it celebrates the people building CtrlB every day - the team that has poured craft, care, and countless late nights into bringing this vision to life. With their dedication and this new backing, we’re ready to scale faster, strengthen the platform, and bring high-performance observability to engineers around the world.  Since launching, CtrlB has built a **diskless, cloud-native observability data lake** that unifies logs, traces, metrics, and security events on object storage, powered by patent-pending indexing, high-speed ingestion, and up to 200x compression to dramatically cut costs. CtrlB today supports a growing base of engineering teams across **India and the United States**.  --- --- title: "Optimizing Kubernetes Observability with CtrlB’s Schema-Agnostic Ingestion" description: "Kubernetes observability generates chaos. Every pod, container, and service emits logs, traces, and metrics, each in different formats. Connecting those fragments into a clear picture often feels like trying to trace a single drop of water in a storm. Most observability stacks solve this by…" canonical: "https://ctrlb.ai/blogs/optimising-kubernetes-observability-schema-agnostic" publishedTime: "2025-11-16" modifiedTime: "2026-03-27T12:07:13+0000" author: "Adarsh Srivastava" tags: [] --- # Optimizing Kubernetes Observability with CtrlB’s Schema-Agnostic Ingestion Kubernetes observability generates chaos. Every pod, container, and service emits logs, traces, and metrics, each in different formats. Connecting those fragments into a clear picture often feels like trying to trace a single drop of water in a storm. Most observability stacks solve this by enforcing schemas, normalizing formats, and pre-indexing data before it’s even useful. That’s powerful but painfully rigid. In dynamic Kubernetes environments, schemas change faster than your dashboards can catch up. **CtrlB’s schema-agnostic ingestion** takes a fundamentally different route. It brings flexibility, scalability, and simplicity to Kubernetes observability, without forcing structure upfront. ### **The Challenge: Too Much Structure, Too Early** Traditional observability pipelines (built on tools like Elasticsearch or Loki) require telemetry data to fit into a fixed schema before it’s stored. Every log line must be parsed, mapped, and indexed at ingestion. That model has three big drawbacks: Inflexibility: Adding new log formats or tracing data means updating schemas and reindexing data. Resource Overhead: Index-heavy architectures drive up storage and compute costs. Latency: Reindexing and schema enforcement slow down ingestion, especially during scale events. In Kubernetes, where container lifespans are measured in seconds, schema rigidity becomes a constant tax on speed and clarity.  Modern solutions have evolved. Elasticsearch offers dynamic mapping. OpenTelemetry handles multiple formats. But they still apply structure early in the pipeline, limiting flexibility. ### **How CtrlB's Architecture Works** CtrlB stores raw telemetry data directly to object storage like S3 (reliable storage at S3's eleven-nines durability) When you query, CtrlB's Ingestor engine springs into action: Reads relevant data partitions from blob storage Applies parsing rules dynamically based on your query Correlates logs and traces across services in real-time Returns results typically within 200-800ms for most datasets This separates storage from compute completely. Data lives cheaply on S3. Compute resources activate only during queries. For frequently-queried data, and exploratory debugging or long-term retention, CtrlB often wins.. ## **Kubernetes Integration in Minutes** To make onboarding effortless, CtrlB provides pre-built configurations for Kubernetes via the[ **ctrlb-k8s** reposi](https://github.com/ctrlb-hq/ctrlb-k8s)tory. You can deploy it in minutes using two simple options based on your observability needs: **Option 1: Logs Only (Fluent Bit)** For teams focusing purely on log observability: Deploy a lightweight Fluent Bit DaemonSet to collect container logs. Logs are automatically formatted as JSON and streamed to CtrlB. Ideal for infrastructure or system-level visibility. **Option 2: Logs + Traces (OpenTelemetry Collector)** For full-stack observability: Deploy the OpenTelemetry Collector DaemonSet. It collects both container logs and distributed traces via OTLP (gRPC or HTTP). Best suited for application-level debugging and correlation. Both options forward data securely to CtrlB using your instance host, stream name, and API token. ### **Quick Setup Walkthrough** You can get started directly from the repository: ``` git clone https://github.com/ctrlb-hq/ctrlb-k8s.git ``` ``` cd ctrlb-k8s ``` Then, update the placeholders in the ConfigMap with your CtrlB instance details: ``` = your-instance-host = your-stream-name = your-api-token ``` Deploy your preferred collector: **For Fluent Bit (Logs Only):** ``` kubectl apply -f fluent-bit/ ``` **For OpenTelemetry (Logs + Traces):** ``` kubectl apply -f otel/ ``` That’s it. Your Kubernetes cluster starts streaming logs and traces directly into CtrlB’s schema-agnostic ingestion engine. ### **Why Schema-Agnostic Matters in Kubernetes** In dynamic environments like Kubernetes, **you don’t always know what you’ll need to debug tomorrow**. A schema-first pipeline can’t adapt to new formats or data sources without downtime or data loss. CtrlB’s model gives you:  **Instant flexibility** — Ingest any log or trace format without changing configurations. **Query-time structure** — Define fields and filters only when you need them. **Unified view** — Correlate logs and traces from across services seamlessly **Multi-format environments**: Mix structured and unstructured data freely **Exploratory debugging**: Query patterns you didn't anticipate **Long-term retention**: Years of data without reindexing costs **Rapid iteration**: New services ship without schema coordination. ### **Operational Efficiency by Design** By separating storage from compute, CtrlB eliminates traditional trade-offs between retention and speed. Durability: Logs and traces are stored once, forever queryable. Elasticity: Compute scales up only when you query or perform analysis. Simplicity: Kubernetes-native deployment through prebuilt DaemonSets. You spend less time managing pipelines and more time understanding your systems.  ## **Technical Considerations** **Query Capabilities**: CtrlB supports filtering, aggregation, and correlation. Complex joins require multiple passes. It's optimized for trace and log analysis. **Data Governance**: Set retention policies at the S3 level. Use bucket lifecycle rules for compliance. CtrlB queries respect these boundaries automatically. **Integration**: CtrlB provides APIs for Grafana and REST endpoints, so it complements existing tools.** ** ## **Moving Forward** Kubernetes environments demand flexible observability. Fixed schemas work until they don't. When that pod you never monitored becomes critical, pre-indexed fields won't help. CtrlB's schema-agnostic model isn't universally better. It's different. It optimizes for exploration over efficiency, flexibility over speed, simplicity over features. For teams drowning in format varieties, fighting schema drift, or exploring unknown unknowns, this tradeoff makes sense. Your raw data remains queryable forever. New questions get answers without reindexing. Whether you deploy Fluent Bit or OpenTelemetry, CtrlB transforms telemetry chaos into on-demand clarity. No migrations. No schema committees. Just questions and answers, when you need them. The future of Kubernetes observability might not be faster indexes or smarter schemas. It might be no schemas at all, until the moment you need them. --- --- title: "Resilient Architectures for Cloud-Native Log Handling" description: "Your cloud-native service just scaled to hundreds of containers. Each one is generating logs. Your logging pipeline? It just crashed. Welcome to cloud-native logging in 2025, where the challenge isn’t collecting logs, it’s surviving the flood of data without breaking your infrastructure.\n The…" canonical: "https://ctrlb.ai/blogs/blog_resilient_architecures" publishedTime: "2025-11-11" modifiedTime: "2026-03-27T12:07:50+0000" author: "Adarsh Srivastava" tags: [] --- # Resilient Architectures for Cloud-Native Log Handling Your cloud-native service just scaled to hundreds of containers. Each one is generating logs. Your logging pipeline? It just crashed. Welcome to cloud-native logging in 2025, where the challenge isn’t collecting logs, it’s surviving the flood of data without breaking your infrastructure. ## **The Logging Problem Nobody Warned You About** In the past, logging was simple. One app, one log file. You’d tail /var/log/app.log and ship it somewhere. Done. Cloud-native systems changed everything. Now, your application is made of many services. Each runs in containers that can start, stop, or move at any time. Logs are scattered across short-lived environments. If a container crashes and restarts, its logs might be gone before you even notice. This isn’t just annoying, it’s an architectural problem. ## **Three Ways Logging Systems Fail** ### **1. Ephemeral Storage** Containers often write logs to local files. But those files disappear when the container stops or moves. If your service crashes at 3 am and restarts, the logs that explain why? Gone. **Resilience tip:** Stream logs out immediately. Use host-level agents or sidecar containers to send logs to external storage as soon as they’re written. ### **2. Backpressure Overload** When your system is under load, it can generate millions of log lines per second. If your logging pipeline can’t keep up, you face three bad options: Buffer everything → memory overload Drop logs → lose visibility Block the app → slow down your service **Resilience tip:** Use queues with limits. Let your log pipeline slow down the producer when needed. Message queues like Kafka or SQS help absorb spikes and prevent crashes. ### **3. Configuration Drift** You set up logging in one environment. It works. You deploy the same config elsewhere, and logs stop flowing. Why? A small difference in networking, permissions, or file paths. **Resilience tip:** Use infrastructure-as-code for logging configs. Version control everything. Monitor your logging pipeline like any other service. If logs stop flowing, trigger an alert. ## ** Resilient Logging Architectures** Let’s look at three patterns that help logging systems survive real-world failures. ### **Pattern 1: Host-Level Agent** Deploy a logging agent on each node or VM. It watches container logs and sends them to a central system. Examples: Fluent Bit, Vector, Filebeat Reads logs from stdout/stderr or container files Adds metadata (service name, environment, etc.) Buffers locally if needed **Why it works:** One agent per host. Survives container restarts. Scales with your infrastructure. ### **Pattern 2: Sidecar Container** Add a logging container next to your app container. They share a volume. The app writes logs, the sidecar reads and forwards them. Useful for apps that don’t log to stdout Allows custom log processing per service Isolated failure: one sidecar crashing doesn’t affect others **Tradeoff:** Adds overhead. Use selectively. ### **Pattern 3: Three-Layer Pipeline** A production-grade logging setup has three parts: Collection Layer Agents collect logs from containers or hosts Add metadata Buffer locally Aggregation Layer Message queue (Kafka, SQS, etc.) Handles spikes Allows multiple consumers (security, ops, etc.) Storage & Analysis Layer Centralized log system (CtrlB, Elasticsearch, Loki, etc.) Long-term retention Search and visualization **Why it works:** The queue in the middle absorbs pressure. If storage slows down, the queue holds logs until it recovers. Your app keeps running. ## ** Handling Failure Gracefully** Even with a good architecture, things break. Here’s how to fail smart: ### **1. Exponential Backoff** If a log forwarder can’t reach the backend, don’t retry every second. Wait longer each time: 1s, 2s, 4s, 8s… up to a limit. This avoids flooding the system. ### **2. Circuit Breakers** If a backend fails repeatedly, stop trying for a while. Buffer logs locally. Check again later. Resume when it’s healthy. ### **3. Graceful Degradation** Not all logs are equal. During overload: Keep: Errors, security events Sample: Info logs (keep 10%) Drop: Debug logs Better to lose some logs than crash the whole system. ### **4. Monitor Your Logging Pipeline** Your logging system needs observability too. Track: Are logs flowing from all services? Are any agents crashing? Is the queue growing too fast? Use tools like Prometheus, Grafana, or Datadog. Treat your logging pipeline like a production service. ## ** Final Thoughts** Cloud-native logging isn’t just about collecting data. It’s about building systems that survive scale, failure, and complexity. Whether you’re running containers, serverless functions, or hybrid environments, resilience starts with architecture. Logs are your lifeline during incidents. Make sure they’re still there when you need them most. --- --- title: "Evolving Observability Standards in Multi-Cloud Architectures" description: "As more organizations adopt multi-cloud strategies to use the best services from each provider and avoid being locked into one vendor, observability has become one of the biggest challenges in managing modern infrastructure. Monitoring apps across AWS, Azure, Google Cloud, and on-premise…" canonical: "https://ctrlb.ai/blogs/evolving-observability-standards-in-multi-cloud-ar" publishedTime: "2025-10-05" modifiedTime: "2026-03-27T12:08:24+0000" author: "Adarsh Srivastava" tags: [] --- # Evolving Observability Standards in Multi-Cloud Architectures As more organizations adopt multi-cloud strategies to use the best services from each provider and avoid being locked into one vendor, observability has become one of the biggest challenges in managing modern infrastructure. Monitoring apps across AWS, Azure, Google Cloud, and on-premise environments is complex. This has pushed the need for common observability standards that give unified visibility without taking away flexibility. ## **What makes OpenTelemetry different from older protocols?** OpenTelemetry (OTel) is now the go-to standard for observability instrumentation. It came from the merger of OpenTracing and OpenCensus and provides a complete set of APIs, SDKs, and tools for collecting, processing, and exporting metrics, traces, and logs. Compared to older protocols like StatsD or Jaeger’s tracing format, OTel has big advantages. StatsD is simple and good for metrics, but it doesn’t support rich metadata or data correlation, which modern systems need. Jaeger and Zipkin are excellent for tracing, but they don’t cover metrics and logs, creating silos. OTel removes these silos with one unified approach. The real power of OTel lies in its semantic conventions, which standardize how telemetry is structured and labeled. This makes it possible to understand and compare data consistently, no matter which infrastructure or monitoring tool you use. ## **Why are vendor-agnostic data pipelines important?** Traditional monitoring tools often tied data collection, processing, and storage tightly together. This made it hard to swap tools or send the same data to different platforms. Modern observability is moving toward vendor-agnostic pipelines. The OTel Collector acts as a central hub: it can receive, transform, and route telemetry to multiple destinations at once. Example: you could send traces to Jaeger for debugging and also to a cloud APM service for alerting without changing your app code. The collector can also sample, filter, or enrich data before sending it out. This reduces the cost and effort of switching vendors or adding new tools. Instead of re-instrumenting apps, you just configure new exporters. ## **What challenges does hybrid cloud monitoring create?** Hybrid cloud (mix of cloud + on-prem) brings extra challenges. Network delays affect trace collection. Different security and compliance rules complicate things. Teams end up juggling multiple dashboards and query languages. Teams also face **operational problems**: multiple dashboards and context-switching between tools. This slows down debugging and increases MTTR (mean time to resolution). Standardized protocols help here by ensuring telemetry looks the same everywhere. With consistent data formats, teams can use unified dashboards and alerts across environments, improving efficiency and reducing friction. ## **How does standardization improve portability?** The biggest benefit of standardization is portability**.** If you use OTel for instrumentation, you can move apps between cloud providers without losing observability. Tracing, logs, and metrics still connect, no matter where the workload runs. This also enables true multi-cloud setups. For example: Run compute-heavy workloads on AWS Use Google Cloud for AI services Use Azure for compliance-driven workloads With standardized observability, this becomes manageable. It also cuts costs: organizations can negotiate better vendor deals and route different data types to cheaper storage or analysis tools. ## **What trends are shaping the future of multi-cloud observability?** Several trends are shaping the future of multi-cloud observability: Service meshes (like Istio, Linkerd) now use OTel as the main observability mechanism, giving automatic instrumentation across services. Kubernetes acts as a unifying layer, with observability tools increasingly building on top of it. APIs like Vertical Pod Autoscaler show how platform-level standards drive innovation. Edge computing requires new data strategies. More organizations process and filter telemetry at the edge first, only sending critical data to central systems. GitOps integration is growing. Observability configs, dashboards, alerts, and monitoring rules are being versioned and deployed as code, just like apps. ## **Final Thoughts** Multi-cloud and hybrid architectures are here to stay. The organizations that adopt **open, vendor-agnostic observability standards** will be the ones with the edge: faster debugging, lower costs, and more freedom to use the best tools from each provider. This shift toward open standards is more than a technical upgrade. It’s a fundamental change in how we think about and manage observability in a complex, distributed world. --- --- title: "Optimizing Ingestion from Diverse Sources in Unified Data Lakes" description: "Data is everywhere today, in apps, servers, devices, and cloud platforms. To make sense of it all, businesses need a single place to store and access this data. That’s why many use a data lake: a central system that can handle massive amounts of data in different formats. But here’s the real…" canonical: "https://ctrlb.ai/blogs/optimizing_ingestion-from-diverse-sources-in-data-lakes" publishedTime: "2025-10-01" modifiedTime: "2026-03-27T12:08:59+0000" author: "Adarsh Srivastava" tags: [] --- # Optimizing Ingestion from Diverse Sources in Unified Data Lakes Data is everywhere today, in apps, servers, devices, and cloud platforms. To make sense of it all, businesses need a single place to store and access this data. That’s why many use a **data lake**: a central system that can handle massive amounts of data in different formats. But here’s the real challenge: **getting data into the lake efficiently.** This step is called **ingestion**. It’s the process of pulling data from all your different sources and making it ready for use. If ingestion is slow, unreliable, or expensive, the whole data lake becomes less useful. This blog looks at how to optimize ingestion when dealing with diverse sources, and covers key topics like connector frameworks, schema evolution, throughput, and data quality. ## **Why Optimized Ingestion Matters?** A data lake is like a large water reservoir. Rivers, taps, and rain all feed into it. If those sources are blocked or polluted, the reservoir doesn’t help anyone. In the same way, ingestion ensures that: Data from all sources arrives in the lake. Data remains fresh, reliable, and consistent. Users can access it without waiting hours. Optimizing ingestion means keeping this flow smooth, cost-efficient, and ready to scale. ## **Connector Frameworks for Heterogeneous Inputs** Organizations deal with many types of data: logs, metrics, traces, events, and SaaS records. Each comes in different shapes and speeds. Connector frameworks help by acting as **bridges** between sources and the data lake: OpenTelemetry (OTel): Provides a standard format for collecting logs, metrics, and traces from different services. Especially useful for federating observability data across multiple platforms. Fluent Bit: A lightweight, fast log collector that can run at the edge (e.g., inside Kubernetes or on servers) to gather data and ship it to the lake. Vector: An open-source, high-performance data pipeline that collects, transforms, and routes logs and metrics from multiple sources to various destinations. It’s designed for efficiency, using minimal resources while supporting advanced processing at the edge or in centralized environments. The right mix of connectors ensures smooth ingestion without building custom pipelines for every source. ## **How to Handle Schema Evolution?** One of the biggest headaches with ingestion is handling **schema evolution**. Data formats don’t stay the same forever. A log field may get renamed, new attributes may be added, or a JSON structure may change. If ingestion depends on a fixed schema, these changes break pipelines. That’s why modern data lakes adopt **schema-on-read** or **schema-less ingestion**. Instead of forcing a structure at the start, raw data is stored as-is, and the schema is applied later when you query it. This makes it easy to handle new fields without rewriting ingestion logic. CtrlB, for example, supports schema-less log search, so users don’t have to worry about rigid formats.  ## **What are Some Throughput Optimization Techniques?** Data doesn’t arrive at a steady pace. Some days are quiet; other days, an outage can generate millions of log lines in minutes. To handle this, ingestion pipelines need **throughput optimization**, the ability to scale up when the load is high, and scale down when it’s low. Common techniques include: Batching: Grouping smaller events together before writing them, reducing overhead. Compression: Reducing payload size to improve network speed. Parallelism: Processing multiple data streams simultaneously. On-demand compute: Instead of pre-allocating resources, compute is spun up only when needed (as CtrlB does with its Ingestor). These techniques ensure ingestion remains cost-efficient without dropping or delaying data.  ## **What are the Data Quality Assurance Processes?** Fast ingestion is pointless if the data itself is unusable. That’s where **data quality assurance** comes in. Processes to ensure quality at ingestion include: Deduplication: Removing repeated data points that waste storage. Validation: Checking if required fields (like timestamps or IDs) exist. Enrichment: Adding metadata like service names or environments (prod, dev) for easier filtering later. Error handling: Logging ingestion failures and retrying automatically. Without these steps, the data lake risks turning into a **data swamp**, full of incomplete or inconsistent records. ## **Putting It All Together** An optimized ingestion strategy combines all these elements: Connector frameworks for diverse inputs (OTel, Fluent Bit, API connectors). Schema flexibility to handle evolution without breaking pipelines. Throughput optimization to handle bursts while staying cost-efficient. Data quality checks to keep information reliable and usable. When these pieces work together, the result is a **unified, reliable, and future-proof data lake**. ## **Conclusion** Optimizing ingestion is not about building bigger or more complex pipelines. It’s about designing smarter flows that handle diversity, growth, and change with ease. By using the right connectors, supporting schema evolution, optimizing throughput, enforcing data quality, and enabling federation, organizations can ensure that their data lakes stay clean, reliable, and cost-friendly. In the end, a data lake is only as good as what flows into it. With the right ingestion strategy, you can turn raw, scattered inputs into a unified foundation for insights, troubleshooting, and innovation. --- --- title: "Cost-Effective Telemetry Scaling with CtrlB’s Cloud Object Storage" description: "Observability isn’t just about insight anymore, it’s about cost. As systems scale, telemetry (logs, traces, metrics) becomes one of the fastest-growing expenses for engineering teams. Platforms like Datadog and New Relic make it easy to get started, but hard to sustain. You pay for ingestion,…" canonical: "https://ctrlb.ai/blogs/cost-effective-telemetry-scaling" publishedTime: "2025-09-25" modifiedTime: "2026-03-27T12:09:23+0000" author: "Adarsh Srivastava" tags: ["Simran"] --- # Cost-Effective Telemetry Scaling with CtrlB’s Cloud Object Storage Observability isn’t just about insight anymore, it’s about cost. As systems scale, telemetry (logs, traces, metrics) becomes one of the fastest-growing expenses for engineering teams. Platforms like Datadog and New Relic make it easy to get started, but hard to sustain. You pay for ingestion, retention, and dashboards & the bill grows as your traffic does. Whereas CtrlB, by building on **cloud object storage** (like S3, GCS), makes telemetry **cheaper, scalable, and queryable on demand** without relying on expensive SaaS infrastructure or redundant hot storage. Let’s explore how this approach works and why it’s reshaping how teams think about observability costs. ## **How does CtrlB cut costs compared to SaaS observability tools like Datadog?** SaaS platforms charge based on **data volume ingested**, not how much you actually use. You pay every time a log line passes through their system, whether you look at it or not. That model might seem convenient early on, but at scale, it becomes painful. With CtrlB, the economics flip. Your logs stay in your cloud object storage (S3, GCS, Azure Blob). CtrlB only applies compute when you query. You don’t pay for continuous ingestion, indexing, or replication. This separation of storage and compute makes a big difference. Teams using CtrlB typically reduce their observability spend by **60–80%** compared to SaaS tools without deleting data or reducing visibility. Because storage itself is cheap (as low as $0.02/GB/month on S3) and highly durable (11 nines), you can **retain all your logs indefinitely** while paying only for what you actually query. ## **How does CtrlB handle data spikes without “bill shocks”?** If you’ve ever experienced a production incident, you know what happens next: debug logs flood your pipelines, ingestion costs spike, and next month’s observability bill doubles. This happens because most observability platforms **charge at ingestion time.** Every new log means more indexing, more storage, and more compute, even if it’s only relevant for a short investigation window. CtrlB’s architecture prevents that. Incoming data is written directly to object storage, not an always-on cluster. The system builds micro-indexes dynamically, targeting only the relevant files instead of your entire dataset. This means even during massive data surges, CtrlB’s costs remain predictable. You’re not paying for “hot” capacity you don’t use; you pay only when you query. For example, an e-commerce platform running a festive sale can log terabytes of traffic data without worrying about scaling infrastructure or facing a surprise invoice. ## **What is intelligent data tiering, & How does CtrlB do it differently?** Traditional systems rely on “hot” and “cold” tiers: Hot storage: Expensive, but searchable instantly. Cold storage (like S3 or Glacier): Cheap, but not queryable without rehydration. CtrlB eliminates the hard divide between the two. All data lives in **object storage**, but CtrlB automatically tiers it. For example: Recently written logs stay “warm” with lightweight micro-indexes for fast search. Older logs stay fully in object storage, but with index metadata stored separately for quick retrieval. When a query spans multiple tiers, CtrlB’s control plane automatically routes it, fetching only what’s relevant. You don’t need to maintain pipelines or rehydrate data manually. Whether logs are from **last night or last quarter**, they remain searchable within seconds. ## **What strategies help manage log volume in high-scale environments like e-commerce?** E-commerce systems generate **huge, bursty telemetry loads**, checkout logs, payment gateway traces, search analytics, promotions, and fraud monitoring. The challenge isn’t just storing all of this; it’s storing it **efficiently.** Here’s how teams can optimize storage using CtrlB and cloud object storage: Adopt schema-on-read instead of schema-on-write. Let your logs flow directly to storage in their native structure. CtrlB gives you results dynamically at query time, no rigid schemas or upfront transformations needed. Use Parquet-based indexing for compression and query speed. CtrlB stores and processes data in columnar formats like Parquet, which compresses well and enables fast, selective scans. This keeps storage efficient and queries fast. Retain everything, but prioritize access. Define policies: recent logs (<30 days) get lightweight indexing; historical data stays untouched until queried. This ensures cost balance without losing long-term visibility. Avoid re-ingestion loops. Don’t build separate ETL jobs to rehydrate logs for analysis. CtrlB reads directly from object storage, which means your pipelines stay simple and your costs are predictable. These practices make it possible for even data-heavy platforms to maintain **deep observability** without storage bloat or operational overhead. ## **How does this change the way teams think about observability architecture?** Traditional observability tools are built around **control through ingestion;** they want your data to live inside their platform. CtrlB reverses that. It treats your cloud as the observability backbone, not an external dependency. Instead of being locked into a SaaS storage tier, you control: Where your data lives How long has it been retained When and how it’s queried That’s a major shift. Observability becomes **a system design choice**, not a line item on your monthly bill. ## **Why cloud object storage is the future of observability** Cloud storage has already replaced disks for backup, and analytics observability is next. By combining: Durability (99.999999999% reliability) Elastic scaling Pay-per-query compute Native log correlation CtrlB makes cloud object storage behave like a high-performance observability lake. You no longer have to decide between **visibility** and **affordability. **You get both infinite retention, real-time search, and predictable cost. ### **In Summary** Most observability platforms make you choose between **retention and cost**. CtrlB lets you have both by treating cloud object storage as a first-class citizen, not an archive. You don’t need to delete old data, rebuild pipelines, or fear data spikes. With CtrlB, you can store everything, query instantly, and scale observability the same way cloud storage scales: **cheap, elastic, and infinite.** ## **FAQs** **1. How is CtrlB different from Datadog or other SaaS tools? ** SaaS tools charge per ingested GB and store data in their infrastructure. CtrlB stores data in object storage, charges only for query compute, and gives you full control over retention. **2. Can I use CtrlB for both recent and old data? ** Yes. CtrlB’s micro-indexing allows you to query both recent and historical logs directly from object storage with sub-second latency. **3. How does CtrlB handle large-scale or spiky traffic? ** It scales compute elastically during traffic bursts, spins up compute to process queries, then scales down automatically. You never pay for idle capacity. **4. Is data transformation required before storing logs in S3? ** No. CtrlB supports schema-on-read. You can store raw JSON or structured logs. CtrlB interprets them dynamically at query time. --- --- title: "Sustainable Practices in Large-Scale Log Data Management" description: "As organizations generate ever-increasing volumes of log data, the environmental impact of storing and processing this information has become a critical concern. Modern observability stacks consume substantial computational resources, contributing significantly to an organization’s carbon…" canonical: "https://ctrlb.ai/blogs/sustainable-practices-in-large-scale-log-data-mana" publishedTime: "2025-09-20" modifiedTime: "2026-03-27T12:10:12+0000" author: "Adarsh Srivastava" tags: [] --- # Sustainable Practices in Large-Scale Log Data Management As organizations generate ever-increasing volumes of log data, the environmental impact of storing and processing this information has become a critical concern. Modern observability stacks consume substantial computational resources, contributing significantly to an organization’s carbon footprint. The challenge lies in balancing the need for comprehensive observability with environmental responsibility. This blog explores how companies can adopt sustainable practices in log data management while maintaining operational excellence. ## **The Sustainability Paradox** Comprehensive logging is vital for **security, compliance, and operational visibility**. But the infrastructure required to support terabytes of logs daily, storage systems, compute clusters, and cooling consumes vast amounts of energy. A “keep everything forever” mindset often leads to redundant ingestion, over-indexing, and oversized clusters that waste both money and energy. Sustainable log management aims to break this cycle by rethinking how data is ingested, stored, and queried. ## **Energy-Efficient Storage and Processing** ### **Intelligent Data Tiering** Not all logs need to live in expensive, always-on storage. Hot data can stay on high-performance SSDs for quick access, while older logs move to warm or cold object storage. Services like Amazon S3 Glacier or Azure Archive Storage consume far less energy per gigabyte. With automated tiering policies, organizations can reduce energy consumption by up to **40%** without losing accessibility. For example, logs older than 30 days may shift to warm storage, while anything older than 90 days moves to cold storage. ### **Compression and Deduplication** Compression algorithms designed for log data, like **Zstandard (zstd)**, can achieve **10:1 ratios**, slashing storage needs. Deduplication further removes redundant patterns, common in repetitive log files. Implementing compression during ingestion, transit, and at rest not only saves space but also reduces the energy footprint of queries. ### **Edge Processing and Filtering** Filtering logs at the edge prevents noisy or redundant data from traveling across networks. Instead of sending every HTTP 200 response to a central system, organizations can aggregate them locally and only transmit anomalies. This can cut volumes by **60–80%**, saving on bandwidth, storage, and compute. ### **Optimized Query Processing** Broad, unscoped searches waste massive compute cycles. Using **columnar formats like Parquet or ORC**, proper indexing, and pre-aggregated views dramatically improve query efficiency. While pre-aggregation requires extra storage, it reduces repetitive compute-heavy queries, leading to **net energy savings** and faster results. ## **Smarter Retention Policies** ### **Regulatory-Aligned Retention** Many companies keep logs far beyond what compliance requires. For example, PCI DSS mandates one year, but some organizations store payment logs for several years. Aligning retention to actual requirements reduces unnecessary storage and energy use. ### **Graduated Retention Strategies** Keep full raw logs for 30 days, then shift to aggregated metrics or sampled data for long-term insights. This can reduce storage by **90%** while maintaining analytical capabilities. ### **Automated Lifecycle Management** Tagging and classification systems enable automated archiving, compression, or deletion based on business value and compliance needs. This ensures retention rules are applied consistently without human error. ## **Measuring What Matters: Sustainable Observability Metrics** Sustainability improves when it’s measured. Organizations should track: Power Usage Effectiveness (PUE): Efficiency of data centers (target closer to 1.0). Carbon Intensity Metrics: The carbon footprint of workloads, influenced by the energy mix of a region. Storage Efficiency Ratios: Compression, deduplication, and utilization benchmarks. Query Efficiency Scores: Data scanned per query or CPU cycles per insight, highlighting inefficient searches. Dashboards that expose these metrics encourage teams to optimize not just for speed, but for efficiency and carbon impact. ## **ROI of Sustainable Practices** Sustainability and savings often go hand in hand: Direct Cost Savings: Compression and tiering can reduce storage costs by 50–70%. For a company managing 100TB of logs monthly, cutting storage by 60% could save hundreds of thousands annually. Energy Cost Reductions: Lower compute and cooling translate into reduced energy billsespecially for enterprises running their own data centers. Regulatory & Reputation Benefits: Meeting sustainability mandates early positions companies favorably with regulators, customers, and employees. Performance Gains: Compressed data moves faster, and efficient queries complete sooner, improving developer productivity. Most sustainable log management initiatives see ROI within **12–18 months**. ## **The Future of Sustainable Log Management** Emerging trends point to even more efficiency: On-Demand Compute Models: Indexing happens at ingest time, while resources are consumed at query time, not upfront. AI-Assisted Filtering: Identifying redundant logs automatically. Carbon-Aware Workloads: Scheduling non-urgent tasks when renewable energy availability is higher. ## **Conclusion** Sustainable log management is both an environmental imperative and a business opportunity. By embracing **intelligent tiering, compression, edge filtering, query optimization, smarter retention, and measurable sustainability metrics**, organizations can reduce their carbon footprint, control costs, and improve performance. Some platforms, like **CtrlB**, are already moving in this direction by **decoupling compute and storage**. Logs remain in durable, low-cost object storage, and compute is applied only on demand. This reduces unnecessary processing, cuts costs, and lowers energy consumption. The companies that adopt these practices now will not only save money but also lead in responsibility, efficiency, and resilience. In a world where log data volumes will only continue to grow, sustainable practices are no longer optional, they are the foundation of observability’s future. --- --- title: "Optimizing Observability for Edge Computing Environments" description: "Why Observability at the Edge Matters Edge computing pushes data processing closer to where it’s generated factories, retail stores, autonomous vehicles, and IoT devices. This reduces latency and bandwidth costs but also creates a new layer of operational complexity. Traditional observability…" canonical: "https://ctrlb.ai/blogs/optimizing-observability-for-edge-computing-enviro" publishedTime: "2025-09-15" modifiedTime: "2026-03-27T12:10:36+0000" author: "Adarsh Srivastava" tags: ["Simran"] --- # Optimizing Observability for Edge Computing Environments ### **Why Observability at the Edge Matters** Edge computing pushes data processing closer to where it’s generated factories, retail stores, autonomous vehicles, and IoT devices. This reduces latency and bandwidth costs but also creates a new layer of operational complexity. Traditional observability stacks designed for centralized data centers often fall short at the edge. With thousands of distributed nodes generating telemetry data, ensuring visibility, reliability, and cost-efficiency becomes a challenge. ### **The Unique Observability Challenges of Edge Environments** Unlike centralized or cloud-native systems, edge environments have: Distributed Nodes – Dozens or hundreds of edge locations, each generating logs, traces, and metrics. Limited Connectivity – Intermittent or high-latency network links to the cloud. Resource Constraints – Limited CPU, memory, and storage at edge nodes. High Data Volume – Large streams of telemetry data from devices, sensors, and local workloads. Security and Compliance – Data sovereignty and regulatory requirements for sensitive edge data. These constraints make “lifting and shifting” your existing observability pipeline impractical. ### **Techniques for Low-Compute Telemetry Transmission** To cope with bandwidth and compute limits, telemetry collection at the edge needs to be lean: Batching & Compression Instead of sending every piece of data immediately as it's generated, smart edge systems collect multiple data points and compress them before transmission. Think of it like filling up a suitcase rather than making multiple trips with individual items. This approach reduces the processing power needed for network operations and cuts down on the constant "chatter" between edge devices and servers. Modern compression algorithms can shrink telemetry data by 70-90% without losing important information, making every byte of bandwidth count. Adaptive Sampling: Not all data points are equally important. During normal operations, an edge device might sample temperature readings every minute. But when an anomaly is detected, like a sudden spike in temperature, it can automatically increase sampling to every few seconds. This dynamic approach ensures critical events get the attention they deserve while conserving resources during routine periods. It's like having a security guard who pays closer attention when something seems off, rather than maintaining the same level of vigilance at all times. Delta & Event-Based Updates Rather than continuously sending complete status reports, efficient edge systems transmit only what has changed since the last update. If a sensor reading hasn't moved from 72°F, why keep sending "72°F" every few seconds? Instead, the system sends updates only when values change significantly or when specific events occur, such as crossing a threshold or detecting an error condition. This dramatically reduces data volume while ensuring nothing important gets missed. Lightweight Protocols The communication method matters as much as the data itself. Protocols like gRPC, MQTT, and HTTP/2 are designed to be "lightweight", they accomplish the same communication goals as older protocols but with less overhead. Think of them as express delivery services that strip away unnecessary packaging while ensuring your data arrives intact and secure. These protocols are particularly effective over cellular connections or satellite links, where every bit of bandwidth is precious. These techniques work best when combined. An edge device might batch sensor readings every 30 seconds, compress them by 80%, and send only the changes from the previous batch using MQTT, all while automatically increasing the update frequency when anomalies are detected. This layered approach helps edge nodes conserve resources while still providing high-value observability data upstream. ### **Resource-Efficient Agent Deployment Strategies** Running full-featured collectors on every edge node is rarely feasible. Instead: Use Modular Agents – Deploy only the plugins needed for each workload instead of monolithic agents. Shared Agents per Host – Run a single agent that collects data from multiple local services to minimize CPU/memory. Remote Configuration – Centralize configuration management so agents don’t need heavy local state or manual updates. Containerization – Package agents as lightweight containers to ease upgrades and rollbacks without downtime. On-Demand Processing – Offload heavy parsing or enrichment to the cloud or a regional hub instead of the edge itself. This keeps the footprint small and reduces maintenance overhead. ### **Case Studies: IoT and Remote Device Monitoring** Smart Retail – A chain of stores collects checkout system logs locally. Lightweight agents compress and batch only high-severity errors for immediate upload, with full logs synced overnight. Industrial IoT – A manufacturing plant monitors thousands of sensors. Edge nodes extract metrics from verbose device logs locally and transmit only aggregated metrics to the cloud, cutting bandwidth by 70%. Remote Health Devices – Medical IoT devices buffer telemetry locally during connectivity outages and upload encrypted data once a secure link is restored, maintaining compliance while ensuring no data loss. These examples show how small tweaks at the edge can dramatically cut costs and improve reliability. ### **Tools for Handling Intermittent Connectivity ** Edge environments can’t rely on constant connectivity. Useful patterns and tools include: Local Buffers with Backpressure: When internet connections drop, edge devices need somewhere to store incoming data until connectivity returns. Local buffers act like temporary storage warehouses, typically using the device's hard drive or SSD to hold data that can't be transmitted immediately. The "backpressure" mechanism is equally important; it's like a pressure valve that slows down data collection when storage starts filling up. Instead of losing data when the buffer overflows, the system intelligently reduces sampling rates or drops less critical data points, ensuring the most important information survives the outage. Store-and-Forward Architectures: This approach treats edge devices like digital post offices. When data can't be sent immediately, it's stored locally with proper timestamps and sequence numbers. Once connectivity resumes, the system methodically forwards all queued data in the correct order. This ensures that when engineers later analyze the data, they get an accurate timeline of events, even if the network was down for hours or days. The system remembers exactly where it left off and picks up seamlessly. Retry & Acknowledgment Protocols: Network hiccups are common at the edge, so robust systems never assume data arrived successfully. Every time data is sent, the receiving system sends back a confirmation message, like a delivery receipt. If no receipt arrives within a reasonable time, the edge device automatically retries the transmission. This "at-least-once delivery" approach means that while you might occasionally get duplicate data (which can be filtered out), you'll never lose important information due to network glitches. Regional Gateways: Instead of every edge device trying to reach a distant data center directly, regional gateways act as intermediate collection points. Picture them as local distribution centers that are geographically closer to edge devices, perhaps in the same city or region. These gateways have better connectivity to the central platform and can handle the complex work of batching, retrying, and managing connections on behalf of multiple edge devices. When an edge device loses connectivity to the central platform, it might still reach the regional gateway, which buffers the data until the main connection recovers. Compression + Deduplication: When connectivity returns after an outage, there's often a flood of backlogged data to transmit. Smart systems compress this data heavily before transmission and remove any duplicate entries that might have occurred during retry attempts. Deduplication is particularly important because retry mechanisms can sometimes result in the same data being sent multiple times. The system identifies these duplicates by comparing timestamps and data signatures, keeping only one copy of each unique data point while maintaining the compressed format for efficient transmission. These techniques often work together to create remarkably resilient systems. An oil rig might lose satellite connectivity for several hours during a storm. Its edge devices continue collecting sensor data, storing everything locally with compression. When connectivity returns, the regional gateway (perhaps on shore) receives the backlogged data in compressed batches, deduplicates any retry attempts, and forwards everything to the central monitoring platform. Engineers see a complete, uninterrupted view of operations as if the connectivity issue never happened. Modern observability platforms support these patterns out of the box, so teams don't have to reinvent them. The complexity is handled behind the scenes, allowing engineers to focus on analyzing data rather than worrying about network reliability. ### **Security and Access Control** Implement fine-grained role-based access and encryption: Ensure only authorized teams can query or modify edge telemetry. Encrypt data at rest and in transit, especially for sensitive locations. Use signed configurations so rogue agents cannot send fake telemetry. ### **Bringing It All Together ** Optimizing observability at the edge can reduce: MTTR (Mean Time to Resolution) – by surfacing the most urgent signals quickly. Operational Costs – by cutting down on noisy telemetry. Compliance Risks – by enforcing encryption and access policies at the source. But there’s a trade-off. **Every filter, every discarded log, every aggregated metric introduces blind spots:** What if the “dropped trace” was the one linking an outage back to its root cause? What if the anomaly only showed up in the raw logs that never left the device? What if compliance teams need evidence you no longer have? In practice, edge-first strategies often lead to **gaps in visibility**, gaps you only discover when you need the data most. That’s why modern platforms are evolving beyond edge-only optimization. By combining: Schema-less log search (so all formats, from all devices, are retained), Centralized control planes (so policies don’t drift across thousands of nodes), and On-demand compute with durable object storage (so you can analyze everything when you need it) …teams can reduce immediate overhead **without sacrificing long-term completeness.** Because at the edge, efficiency matters, but **blind spots can be costly.** ### **Conclusion** Edge computing is redefining how and where data is processed and observability must evolve with it. By adopting lightweight telemetry transmission, resource-efficient agent strategies, and tools for intermittent connectivity, organizations can keep distributed systems observable without breaking budgets. This translates to faster decisions, higher uptime, and a true competitive edge. --- --- title: "Detecting Node-Hopping Attackers: Correlating Traces and Logs at Sub-Second Speed" description: "Introduction Modern cyberattacks rarely stop at a single system. Once inside, attackers often move laterally, jumping between machines and services, in search of sensitive data or admin access. This tactic is known as node-hopping or lateral movement.\n Detecting these attackers is not easy. Each…" canonical: "https://ctrlb.ai/blogs/detecting-node-hopping-attackers-correlating-trace" publishedTime: "2025-09-08" modifiedTime: "2026-03-27T12:10:59+0000" author: "Adarsh Srivastava" tags: ["simran"] --- # Detecting Node-Hopping Attackers: Correlating Traces and Logs at Sub-Second Speed ## **Introduction** Modern cyberattacks rarely stop at a single system. Once inside, attackers often move laterally, jumping between machines and services, in search of sensitive data or admin access. This tactic is known as **node-hopping** or **lateral movement**. Detecting these attackers is not easy. Each step looks harmless in isolation. But when you connect logs and traces across systems, the pattern becomes clear. The key is doing it **fast** with **real-time log correlation and sub-second detection speeds**. ## **What Is Node-Hopping (Lateral Movement)?** Node-hopping, a form of **lateral movement**, happens when: An attacker compromises one machine (Node A). Steals user credentials or tokens. Uses them to log in to another system (Node B). Repeats the process until they reach sensitive servers or data. In simple terms, it’s like a burglar sneaking room to room inside a building, rather than breaking directly into the vault. ## **Why Node-Hopping Attacks Are Hard to Detect** Lateral movement detection is difficult because attackers: Mimic normal activity. Logins, file access, or admin commands look legitimate on their own. Exploit log silos. Server logs, network logs, and cloud traces live in separate systems. Without centralized log correlation, the bigger picture is missed. Rely on delays. If your SIEM or monitoring tool processes logs in minutes or hours, attackers can move several hops before you even notice. This is why **real-time threat detection** is critical. ## **How Real-Time Log and Trace Correlation Works** **Logs** record what happens inside each machine: logins, process starts, file access, and more. On their own, these entries can look normal. But when you **correlate logs across multiple systems in real time**, you start to see suspicious chains of activity that reveal lateral movement. For example, you might find a pattern like: Node A → A user logs in. Node B → A remote connection happens. Node C → An admin command is executed. Individually, none of these is alarming. Logins, remote connections, and admin commands all happen in normal operations.  Together, they paint the picture of a **node-hopping attacker**. **Traces** add another layer of visibility; many systems tag logs with session IDs or trace IDs. Traces essentially act as the thread that ties logs together into a clear attack path. This makes it easier to follow an attacker’s path step by step, from one node to the next.. Without traces, you’d have to infer connections by timing, usernames, or IP addresses. With trace IDs, the link is explicit.  And if you **correlate** them (look at them together, in sequence, within a short time window), you realize this is suspicious: A user logged into one machine. Seconds later, that login was used to jump into another machine. Then an admin-level action took place on a third machine.  Logs are the raw events. But many systems also tag these logs with **trace IDs** (or session IDs). Think of a trace ID as a thread or breadcrumb trail that ties related events together across systems. If Node A and Node B both have logs carrying the same trace ID, you instantly know those actions are connected. This isn’t an after-the-fact investigation; it’s **lateral movement detection in real time**, giving defenders the speed to contain threats before they spread. ## **Why Sub-Second Detection Matters** Now, let’s add speed into the picture. If your system can: Correlate these events in real time, and Spot when a suspicious chain is forming …then you can catch the attacker while they’re still moving. That’s the difference between: Batch analysis → you discover the attack hours later, when damage is already done. Sub-second correlation → You raise an alert the moment the sequence unfolds, giving your team a chance to respond immediately. With logs + traces correlated in real time, you can: Spot attack chains instantly. Instead of seeing just “a login” or “a process,” you see the whole suspicious story. Build clear attack paths. Logs linked by trace IDs make it easy to visualize: Node A → Node B → Node C. Trigger alerts early. Instead of incident response being a postmortem exercise, you intervene while the attacker is active.  ## **Why Speed and Visibility Matter** **Speed** and **visibility** are the foundations of effective threat detection: Lower MTTD (Mean Time to Detect). Sub-second log analysis shrinks detection times from hours to milliseconds. Faster incident response. Teams can disable accounts or isolate machines before attackers escalate privileges. End-to-end visibility. Correlating logs and traces across all systems (servers, networks, cloud) eliminates blind spots. Attackers depend on slow, siloed systems. **Real-time correlation turns the tables.** ## **Tools for Real-Time Lateral Movement Detection** Different types of platforms support **real-time log correlation and lateral movement detection**: Modern SIEM platforms – Collect logs from across environments and apply correlation rules to detect suspicious sequences. Extended Detection and Response (XDR) solutions – Combine endpoint, network, and identity data for a unified view of attacker behavior. Distributed tracing and observability tools – Track activity across applications and services, linking traces with logs for richer context. Streaming analytics frameworks – Process logs and traces at scale in real time, enabling sub-second alerting. Each of these plays a role in helping security teams gain **speed and visibility**, but they often come with trade-offs around **cost, complexity, or data retention limits**. ## **Conclusion** Node-hopping attackers thrive on blind spots and slow monitoring. Traditional methods miss the bigger picture because they analyze logs in isolation or too late. The solution is **real-time log and trace correlation at sub-second speed**. By connecting events across systems instantly, you can expose **lateral movement attacks** in progress, not after the damage is done. Speed plus visibility equals stronger defense. With the right tools and approach, defenders can catch node-hopping attackers in motion and shut them down before they reach critical targets. At CtrlB, we believe, correlating logs and traces should not come at the cost of either speed or affordability. By pairing sub-second log search with trace-aware correlation directly from durable object storage, we help teams catch lateral movement even in historical data, without maintaining massive hot indexes or expensive SIEM stacks.  --- --- title: "Beyond Index‑Only: Building Tiered Observability with CtrlB" description: "What’s Wrong With the Old Way of Logging? For years, the default way to handle logs has been simple: collect everything, index everything, and hope your search engine can keep up. This “index-only” mindset made sense when log volumes were small. But today’s systems generate terabytes daily, and…" canonical: "https://ctrlb.ai/blogs/beyond-indexonly-building-tiered-observability-wit" publishedTime: "2025-09-03" modifiedTime: "2026-03-27T12:11:25+0000" author: "Adarsh Srivastava" tags: [] --- # Beyond Index‑Only: Building Tiered Observability with CtrlB ## **What’s Wrong With the Old Way of Logging?** For years, the default way to handle logs has been simple: collect everything, index everything, and hope your search engine can keep up. This “index-only” mindset made sense when log volumes were small. But today’s systems generate terabytes daily, and indexing all of that data has become painfully expensive. Teams end up forced into trade-offs: keep only a short window of logs, sample aggressively, or drop valuable context altogether. The result? Blind spots when you need answers the most. ## **Why Think About Tiers Instead of One-Size-Fits-All?** Not all logs are created equal. Some are vital in the moment (like errors during an outage), while others matter weeks or months later (like security audits). Treating both the same is wasteful. **Tiered observability** takes a layered approach: Hot data stays close for fast troubleshooting. Cold data sits in cheaper long-term storage but remains accessible when needed. This way, you avoid paying premium prices to index every single log while still keeping the full history intact. It’s about control and flexibility, storing data based on how you’ll actually use it. ## **How Do Engineers Benefit From Tiered Observability?** Think of a developer on-call at 2 a.m. If an outage happens, they don’t need to sift through six months of logs; they need the last hour. That’s the hot tier. Now, picture the compliance officer three months later who needs to pull login activity for a security review. That’s the cold tier. Both jobs rely on logs, but their needs are different. Tiered observability makes sure each gets the right balance of speed and cost. Engineers debug quickly without draining budgets, and compliance teams know historic data will be there when asked. ## **Where Does CtrlB Fit In?** CtrlB was built around this idea from the start. Instead of running heavy clusters that index everything 24/7, CtrlB: Stores all logs in object storage like S3 (cheap and durable). Uses on-demand compute to query data only when you ask. Keeps lightweight indexes for fast filtering, so even big queries feel interactive. This design flips the script: you don’t pay for endless indexing you rarely use. You pay when you run a query. That means teams can afford to **keep full-fidelity logs for months or years**, not just days. ## **What Does This Look Like in Practice?** Here are two fresh scenarios that highlight the difference: A startup is scaling fast. Traffic doubles overnight after a product launch. Traditional logging would mean spiraling costs just to keep new data searchable. With CtrlB, they continue collecting everything without fear, knowing yesterday’s surge won’t blow up next month’s bill. A bank preparing for an audit. Regulators request six months of login activity. Instead of digging through tapes or rehydrating archives, the compliance team runs a query in CtrlB directly against cold storage. The logs are still there, still intact, and ready in minutes. In both cases, tiered observability isn’t about saving pennies; it’s about **trusting that the data you need is always there** without stressing about cost. ## **Why Does This Matter for the Future of Observability?** Systems are only getting bigger, noisier, and harder to manage. Observability that relies on indexing everything upfront won’t scale forever. Tiered models like CtrlB’s give teams room to grow. They let you: Keep more history without cutting corners. Control which logs are fast vs. slow without losing them. Stay audit- and compliance-ready at all times. This shift is bigger than storage. It’s about giving engineers and businesses confidence that they can see what’s happening in their systems now and later without compromise. ## **FAQ: Tiered Observability and CtrlB** **Q1. Is tiered observability only about saving money? ** No. It’s also about coverage. Teams don’t have to drop logs or shorten retention. You keep the full picture and decide when you need speed vs. when you just need history. **Q2. Does storing logs on S3 make searches slow? ** Not with CtrlB. Queries spin up compute on demand and use smart micro-indexing. That means you still get interactive results, even from huge datasets. **Q3. Can I use CtrlB for both debugging and compliance? ** Yes. Hot logs are great for developers troubleshooting issues. Cold logs give compliance and security teams the history they need for audits or investigations. **Q4. How is CtrlB different from “archive and rehydrate” solutions? ** With CtrlB, you don’t have to restore data into a cluster to query it. Logs are always queryable where they live. That cuts out delays and complexity. **Q5. Who benefits most from this approach? ** Engineering teams tired of log limits, compliance-heavy industries (finance, healthcare), and fast-scaling startups that can’t afford runaway observability costs. --- --- title: "Index Is Just One Tool: Why Observability Needs Multiple Storage Patterns" description: "TL;DR: Indexes are great, but they’re not a religion. At a modern scale, “index everything” slows writes and bloats storage. Observability works best when multiple storage patterns work together. A single index can’t handle every need. Durable object storage keeps all data safe and complete. Small,…" canonical: "https://ctrlb.ai/blogs/index-is-just-one-tool-why-observability-needs-mul" publishedTime: "2025-08-30" modifiedTime: "2026-03-27T12:11:51+0000" author: "Adarsh Srivastava" tags: [] --- # Index Is Just One Tool: Why Observability Needs Multiple Storage Patterns **TL;DR**: Indexes are great, but they’re not a religion. At a modern scale, “index everything” slows writes and bloats storage. Observability works best when **multiple storage patterns** work together. A single index can’t handle every need. Durable object storage keeps all data safe and complete. Small, focused indexes make frequent searches faster. Traces link logs from the same request, showing the full path it took. You get high fidelity, sane cost, and fast queries without pre‑deciding what to drop. ## **The problem with making Index the hero** Indexes make searches faster when you know the question. But observability questions are noisy, emergent, and messy. Production traffic shifts. Incident queries are often ad hoc. If your system uses one big index for everything, you’ll run into at least five big problems: Write amplification – Shards rebalance and rebuild as high-cardinality fields grow. Storage bloat – Indexes can take 30–200% more space than the raw data, sometimes more. Backfill pain – Re-indexing historical data needs a separate, costly pipeline. Mismatch – Full-text indexes struggle with time-range scans; time-series indexes struggle with fuzzy search. Operational drag – Hot shards, skew, and capacity planning add daily overhead Observability shouldn’t be a choice between high costs and losing detail; there’s a better way. ## **Different questions need different storage patterns ** Instead of one store that tries to do everything, use different storage patterns, each built for a specific type of question. Teams get the best results by combining a few complementary patterns: A durable, long-term store – Keep all raw logs and traces safe and complete in low-cost, high-durability storage. This ensures you can always go back to the source for broad searches, compliance checks, or incident reviews. Fast-path accelerators – Use small, focused indexes or summaries to quickly filter down to the data that matters most during active debugging. These cut out noise without forcing you to drop detail upfront. Context links – Maintain strong links between related events, such as tying logs to their traces. This makes it easy to follow the full story of what happened without complex joins or guesswork. By matching the storage pattern to the type of question - wide scans, targeted lookups, or context-driven analysis, you keep fidelity high, control costs, and get answers faster. ## **Why single‑index systems buckle under real workloads?** **High cardinality → index blow-up **Fields like user_id, session_id, or request_id create millions of distinct values. The index grows fast, shards keep splitting and rebalancing, and writes slow down. Memory and storage climb just to keep the index healthy. **Multi-modal queries → cross-shard slowdowns **Incident queries mix styles: full-text search in logs, time-range filters, and joins to traces. A single index can’t optimize for all of that. The query has to touch many shards and then merge the results. Latency spikes right when you need answers most. **Lifecycle churn → constant background work **Real systems roll retention, replay data, and backfill missed events. In a single-index setup, each of these triggers re-indexing and segment moves. That background work competes with live traffic and turns routine maintenance into risk. **Cold data economics → paying for “hot” you don’t use **Keeping months or years of data hot and indexed is costly. Most questions hit the last few hours or days, but the index still carries the whole corpus. You pay the “hot” tax even when you only need cold data for occasional forensics or compliance. **Operational fragility → hotspots and skew **Traffic isn’t even. Some services or tenants are noisier than others. Those keys create hot shards and skew. Teams spend time on shard sizing, capacity planning, and firefighting the index instead of debugging the incident. Bottom line is, Observability questions are diverse. The more your queries vary, the less any one index fits them all. That’s why a multi-pattern approach (lake for truth, micro-indexes for speed, trace links for context, and light summaries for quick checks) holds up better under real load. ## **The CtrlB perspective** CtrlB’s architecture was designed around these principles: Lake-first, disk-less core – All raw logs and traces live in durable object storage. You keep every detail without paying to keep it “hot” all the time. Schema-less search – You can run queries without having to define a perfect structure first, because production data is never perfectly structured. Micro-indexes – Small, focused indexes on key fields like time, service, or trace_id cut through noise fast without ballooning storage costs. // Trace-aware links – Traces are built-in, so you can move between logs and traces easily with shared IDs. On-demand compute – Heavy processing happens only when needed Fast first results – Even broad searches return quickly (sub-second speeds), even when wide scans are noisy and messy. When you’re comparing architectures, the real question isn’t “Which index is best?” It’s “Which mix of approaches gives quick answers, keeps all the details, and stays affordable?” ## **Closing thought** Indexing is a powerful tool. But in observability, data is multi‑modal & questions are unpredictable. Treat the index as one tool in the kit, not the entire workshop. When object storage, micro‑indexes, trace‑aware relationships, and lightweight caches work together, you get the holy trio: **speed, fidelity, and cost control**. ## **FAQ** **Do I need to index everything? **No. Index the pivots (time, service, env, level, trace_id). Keep full fidelity in the lake. **Will a lake‑first design make queries slow? **Not if you pair it with micro‑indexes and a small hot cache. **How do I tie logs to traces without timestamp joins? **Propagate and store trace_id (and service keys) in both. Pivot by ID. **Where do I start if my data is already indexed elsewhere? **Keep the index for the hot path, but move the durable source of truth to object storage. Layer micro‑indexes and trace IDs over time. --- --- title: "From Alert to Action: Incident Response in a Search‑First World" description: "Introduction In today’s fast-changing, cloud-based systems, developers and SREs deal with more moving parts than ever. Systems are highly distributed. Logs and traces grow rapidly. Alerts come in constantly, often missing key context. Teams use too many disconnected tools and must run complicated…" canonical: "https://ctrlb.ai/blogs/from-alert-to-action-incident-response-in-a-search" publishedTime: "2025-08-25" modifiedTime: "2026-03-27T12:12:18+0000" author: "Adarsh Srivastava" tags: [] --- # From Alert to Action: Incident Response in a Search‑First World ### **Introduction** In today’s fast-changing, cloud-based systems, developers and SREs deal with more moving parts than ever. Systems are highly distributed. Logs and traces grow rapidly. Alerts come in constantly, often missing key context. Teams use too many disconnected tools and must run complicated queries that slow them down. This makes it harder to find the root cause of problems and increases the time it takes to fix things (MTTR). Alert fatigue sets in as teams jump between dashboards, wasting time. A search-first approach can change this. It lets teams look directly at raw data, find problems faster, and take quick, focused action. It brings clarity to a noisy, complex system. ### **Why Do Traditional Incident Workflows Stall?** Older observability tools slow things down and cause frustration. Why? Disconnected tools and teams: Different tools for logs, metrics, and tracing create silos. Teams only see parts of the issue. One team sees a graph, another sees a log, and nobody sees the full picture. Slow, complicated queries: Older tools often use pre-built dashboards or slow SQL queries. These take too long and make live troubleshooting hard. You can’t ask new questions easily. Rigid schemas: Traditional databases need a fixed structure. But logs change all the time. It’s hard to keep up, and teams waste time updating schemas before they can search. Too much switching: Engineers often jump between tools and dashboards to find answers. This breaks focus and wastes time. Without a way to connect logs and traces, alerts become noisy and confusing. All these issues make incident response slower and more painful. Fixing problems becomes like solving a puzzle with missing pieces. ### **How Does a Search-First Platform Help?** A search-first approach brings all observability data into one place. Logs are stored in a flexible, schema-less system that supports fast search. Engineers can search across everything, like using a search engine. No need to build dashboards or define fields in advance. Each log includes rich context, service names, user IDs, and error codes, so teams get answers quickly. Big companies like Netflix and eBay already use this model. They use search engines that can scan huge amounts of data in seconds. In a search-first system, you can ask: Which service had a spike in errors? What was affected? What changed at that time? You don’t need multiple tools. Just write a query, or use a simple interface, and get results instantly. With platforms like CtrlB and its Flow engine, teams can search all data right away using Lucene or SQL. There’s no waiting to define schemas or load data, it’s ready to search immediately. ### **Unified Search Powers Faster Incident Response** A search-first model improves incident response in several key ways: Connect logs and traces instantly: All your data is in one place, so you don’t need to switch tools. You can see logs, alerts, and traces together in one view. This gives you full context quickly. Fast search for root cause: Search engines give results in seconds. You can test different queries during an incident to find what broke, when, and why. Faster root cause analysis means faster fixes. Lower MTTR and fewer alerts: By keeping everything in one tool, you avoid wasting time switching between systems. You also get fewer duplicate alerts. Cross-checking data highlights the real issues, not just symptoms. Real-time data access: Search-first tools index data as it comes in. You can search new logs in real time. CtrlB streams data live and supports instant search at any scale without delays or missing data. No fixed schema needed: You can search new fields right away, without setup. In fast-changing environments, this is a big win. You can sort, filter, and group logs using any field, even if it just appeared. Better teamwork: When everyone sees the same logs and traces, there are no blind spots. DevOps, security, and developers work from the same data. This leads to faster, more accurate incident resolution. ### **Real-World Impact and Examples** Companies using search-first tools get real benefits. Many report faster MTTR and more reliable systems. With modern search tools, teams cut query times from minutes to seconds. This also saves money by reducing the number of tools and cutting maintenance costs. For example, Wayfair built a single observability system using OpenTelemetry and search tools. By standardizing logs and traces, they avoided tool silos and improved troubleshooting. This helped them scale their e-commerce systems more easily. Other companies find that unified search cuts alert fatigue and boosts developer productivity. Instead of dozens of related alerts, one clear incident is raised. Engineers respond only to real problems, not noise. On-call work becomes more manageable, and incidents are resolved faster. ### **CtrlB and the Search-First Movement** New tools are built around this search-first idea. CtrlB’s Flow platform collects logs with no schema and minimal indexing. Everything is queryable right away. Engineers can search logs, traces, and services in one place. There’s no need to guess field names or wait for indexes. You just search and get answers. ### **Conclusion: From Alert to Action** Search-first observability changes how teams respond to incidents. Instead of guessing or jumping between tools, teams use fast search to find root causes and take action. This cuts MTTR, reduces alert fatigue, and improves reliability. With tools like CtrlB, platform engineers and SREs get a huge advantage. They can search any log, at any time, and act right away. Alerts become answers. Incidents become solvable in real time. --- --- title: "Immutable Audit Trails: How CtrlB Helps You Prove What Happened" description: "In cloud-native apps, logs are the source of truth. They record errors, user actions, and security events. If logs can be edited or deleted, you lose trust. You also lose proof. That’s why immutable audit trails and tamper-proof logs are vital. CtrlB is built to lock your records in place.\n Why…" canonical: "https://ctrlb.ai/blogs/immutable-audit-trails-how-ctrlb-helps-you-prove-w" publishedTime: "2025-08-20" modifiedTime: "2026-03-27T12:13:06+0000" author: "Adarsh Srivastava" tags: [] --- # Immutable Audit Trails: How CtrlB Helps You Prove What Happened In cloud-native apps, logs are the source of truth. They record errors, user actions, and security events. If logs can be edited or deleted, you lose trust. You also lose proof. That’s why **immutable audit trails** and **tamper-proof logs** are vital. CtrlB is built to lock your records in place. ## **Why Immutability Matters for Your Business** FinTech and other regulated industries must keep logs for 7+ years under rules like PCI-DSS, SOX, or GDPR. You face: Security investigations that need untampered evidence. Regulatory audits that demand a clear history. Legal disputes that hinge on logs you can defend. If someone can change records later, you can’t prove what happened. That risks fines, bad press, or lawsuits. ## ** CtrlB’s Ledger-Style, Secure Log Storage** CtrlB treats logs like a digital ledger in the cloud. Each record is: Append-only – New entries go in. Old ones stay the same. Access-controlled – Only approved roles can write or delete. Indexed once – Records are indexed on ingest. No re-indexing later. Query-true – Searches return the original log. No hidden transforms. You get **audit trail compliance** with zero extra steps. And thanks to **micro-indexing**, queries run in **sub-second search** speed, even on years of data. ## **How do You Investigate Events Fast?** Scenario: An API key pops up in a strange region. With CtrlB, you: Search for that exact key or user ID. Filter by time and service. View each entry exactly as ingested- complete and untampered. No rewrites. No guesswork. You move fast and present airtight findings. ## **When Compliance Says "Log Everything"** In regulated industries, logging isn’t optional; it’s mandatory. Compliance teams often demand full visibility: every API call, DB query, file access, and user action. But the reality? 🔸 CloudTrail bills spike. 🔸 S3 storage piles up. 🔸 Alert rules flood your team with noise. 🔸 And you still need to **prove nothing was altered**. For many teams, log costs soon rival or exceed actual compute spend. CtrlB lets you meet these demands without sinking in cost or complexity: Immutable by Default: Logs can’t be changed or quietly deleted. Cost-Effective Storage: Long-term retention lives on blob storage, not hot disks. That means durability, without daily costs. Role-Based Access: Devs, security, and compliance teams each get what they need, no more, no less. Smart Alerting: Alert on what matters, not on everything. When auditors demand “log everything”, CtrlB helps you say “yes” without draining your budget or burning out your team. ## **Balancing Retention and Cost** Seven-year retention need not slow you down. CtrlB lets you: Set retention policies per service. Move old logs to cost-effective cloud storage. Keep search instant with micro-indexed data. You meet legal rules and keep performance high. ## **Two-Layer Defense: Immutability + Access Control** Immutable logs alone aren’t enough. In CtrlB, your audit trail is protected on two fronts. First, logs are locked in place; once written, they can’t be altered. Second, only authorized roles can delete data, and every read, write, or delete request is itself recorded. Developers debug without seeing private data. Security teams audit without tampering. Compliance leads enforce delete rules and track deletions. Every action is itself logged, so you know who did what and when. ## **Skip the Bloat of Traditional SIEM** CtrlB scales without a heavy SIEM: Cloud object storage keeps costs low. Micro-indexing uses minimal compute. Elastic performance grows with your data. You get enterprise-grade audit trails, without extra complexity or cost. ## **Why This Matters to Your Bottom Line** Immutable, tamper-proof logs are your business insurance. They help you avoid fines by proving records are original, speed up incident response with trusted data, and build customer credibility by showing you protect their information. If you had to defend your system behavior in court or in front of a compliance officer, would you be confident in your logs?  With CtrlB, you can be. Your logs become a **bulletproof ledger**, ready for any audit, any dispute, any regulatory review. Because observability shouldn’t just tell you what’s going on, it should help you **prove what happened**.  --- --- title: "Cold Storage Doesn’t Have to Be Cold: How CtrlB Keeps Your Old Logs Lively" description: "Ever feel like searching old logs means digging through a dusty basement?\nImagine you're on a midnight debug mission, trying to find a clue in last year’s logs. The process is slow, messy, and a little frustrating. In most setups, “cold” log storage is exactly that, pushed aside to save money, but…" canonical: "https://ctrlb.ai/blogs/cold-storage-doesnt-have-to-be-cold-how-ctrlb-keep" publishedTime: "2025-08-16" modifiedTime: "2026-03-27T12:09:47+0000" author: "Pradyuman" tags: ["pradyuman"] --- # Cold Storage Doesn’t Have to Be Cold: How CtrlB Keeps Your Old Logs Lively _Ever feel like searching old logs means digging through a dusty basement? _Imagine you're on a midnight debug mission, trying to find a clue in last year’s logs. The process is slow, messy, and a little frustrating. In most setups, “cold” log storage is exactly that, pushed aside to save money, but painful to access when you actually need it. It **doesn’t have to be this way**. In this post, we’ll chat about how **CtrlB** turns that dusty basement of old logs into a lively, accessible library. No more shivering at the thought of cold storage, let’s warm it up! ## **​​What’s the chilly reality of cold log storage? ** Engineering teams see a flood of logs every day. Keeping all of them in fast, expensive systems isn’t possible, so older logs get pushed into cheaper “cold” storage. The problem? Once they’re there, they may as well be frozen. When you need them during an incident, they’re hard to reach. As one blog put it: cold storage is “cheaper, but painfully slow to search.” You save money, but lose quick access & that can hurt when the pressure’s on. ### **What common woes come with cold storage?** Slow to fetch: Getting data back from cold storage can take forever. For example, pulling logs from AWS Glacier can take hours. By then, your incident may already be over. Hard to search: Cold logs are usually compressed and stored in ways that don’t support quick search. You often have to “rehydrate” them first, basically load them back into a live system, before you can even run a query. It’s like thawing a frozen book every time you want to read it. Extra work and surprise costs: Cold storage needs more setup and maintenance. Teams build scripts and pipelines just to move and reload logs. You may also get surprise bills for retrieving data. And worst of all? These issues often show up at 2 AM when you’re on call. In short, traditional cold storage saves dollars but costs **time and sanity**. It translates to cheap storage & costly retrieval. Engineers end up treating old logs as a last resort, only accessed if necessary. But what if we could **flip that script**? ## **How Does CtrlB Keep Archived Logs Alive?** CtrlB’s philosophy is simple: _“Cold storage doesn’t have to be cold.”_ Instead of making you choose between cost and accessibility, CtrlB **eliminates the trade-off**. So how do we keep those old logs as lively and handy as the fresh ones? Let’s pop the hood (just a little) on CtrlB’s approach: Lake-first storage: From day one, your logs live in durable, low-cost blob storage, essentially a data lake rather than on an expensive, limited disk that later gets shuffled off to an archive. Think of it like shelving your books in a public library immediately, instead of locking them in a vault after a week. On-demand compute with micro-indexing: When you query logs, CtrlB doesn’t make you wait while it “finds the tape”. It spins up compute and uses fine-grained micro-indexes to fetch even months-old data in sub-second time. It’s as if a super-fast librarian instantly knows which shelf and page to grab, no matter how old the book is. Schema-less, flexible search: Ever had to define a schema or indexes for logs before querying? With CtrlB, you don’t have to pre-plan that. The system lets you search on any log fields or text without upfront modeling. In other words, you can ask anything of your historical logs on the fly, no rigid cataloging required. By combining these elements, CtrlB keeps **all your logs searchable and responsive**. We store logs in a cost-effective way **without** turning them into inaccessible “frozen archives”. And because we bring the computing power to the data when you need it, even data from last year comes back **as fast as data from the previous hour.** The result: your old logs never really go stale, and “cold” storage feels just as lively as hot storage. In plain terms: imagine all your logs, recent and archived, sitting together in one big library. Whenever you have a question, a smart librarian (CtrlB) zooms through the aisles with a jetpack, finds exactly the information you need, and hands it to you immediately with no waiting around. ## **What do lively logs look like in action?** To make this concrete, let’s look at a couple of **real-world scenarios** that many engineers know too well, and how things change with CtrlB: Middle-of-the-night incident It’s 2 AM. An on-call engineer is chasing down a critical outage. The trail leads back to logs from six months ago. In a normal setup, this would be a nightmare. You’d have to request those logs, wait for them to be restored from cold storage, and sit around drinking coffee while the system crawls. With CtrlB, the engineer can pull up those year-old logs in seconds, right inside their console, no delays. The logs are ready and searchable, which means the issue gets fixed faster. This can cut hours off the time it takes to resolve incidents. Compliance audit crunch Now, picture a fintech company preparing for an audit. Regulators ask for transaction logs from over a year ago. In most setups, this would mean digging through archives, reloading files, and praying nothing is broken, a slow and stressful process. With CtrlB, the team just runs a query for the dates they need. The logs show up instantly, as easy to search as yesterday’s. This saves hours of work and lowers stress. The engineers can walk into audits knowing they’ll have the data ready without last-minute panic. In both cases, CtrlB turns hard, time-eating jobs into simple steps. Teams can focus on solving problems and meeting goals instead of wrestling with storage tiers or waiting for logs to thaw.  ## **Why is it time to rethink log storage?** It’s time to rethink how we store logs. We don’t need to push them into frozen vaults and hope we never need them again. Tools like CtrlB show that log storage can be both cheap and fast. You can keep years of logs and still search them instantly, without breaking the budget. No more trade-offs. No more late-night “data digging.” From now on, keeping logs warm and easy to use is the new standard. By embracing these new ideas, we turn our log archives from dusty backrooms into active data goldmines. It’s a positive shift that means faster troubleshooting, easier compliance, and happier engineers. After all, when it comes to log data, **letting it go cold is so last decade**. Here’s to a new era of warm, friendly log storage! --- --- title: "When Logs Lie: The Risk of Blind Trust in Ingested Data" description: "Developers and IT professionals rely on logs every day to understand what’s happening in their systems. We treat logs as the diaries of our infrastructure, if nothing alarming appears there, we assume all is well. But what if the logs themselves are misleading or incomplete? Blindly trusting…" canonical: "https://ctrlb.ai/blogs/when-logs-lie-the-risk-of-blind-trust-in-ingested-" publishedTime: "2025-08-12" modifiedTime: "2026-03-27T12:13:57+0000" author: "Adarsh Srivastava" tags: [] --- # When Logs Lie: The Risk of Blind Trust in Ingested Data Developers and IT professionals rely on logs every day to understand what’s happening in their systems. We treat logs as the **diaries of our infrastructure**, if nothing alarming appears there, we assume all is well. But what if the logs themselves are misleading or incomplete? Blindly trusting ingested log data can lead us astray at the worst times. ## **How Logs Can Mislead You** **Logs can be tampered with or missing:** In a perfect world, logs are append-only truth-tellers. In the real world, attackers (or software bugs) can meddle with them. A determined intruder who gains server access might delete or alter log entries to cover their tracks. As one expert put it, _“Attackers go after logs to cover their tracks… many logs are not read-only; therefore, attackers find the logs and change them”._ If logs aren’t securely stored, an attacker can literally rewrite history, destroying evidence of their actions. We’ve even seen attackers disable logging entirely during an attack, for example, the SolarWinds malware turned off security logging while it installed its backdoor, then re-enabled logging afterward. To anyone trusting the system logs, it looked like nothing happened, which is exactly what the attackers wanted. **Logs can be incomplete or delayed:** Logging is a complex pipeline, and things go wrong. A misconfigured or overwhelmed log system might drop events without anyone realizing. For instance, if an application suddenly emits logs in a new format, a rigid parser might ignore those events altogether. It’s also common for logs to be delayed: some services batch their output, so an important event might not show up in your console until minutes or hours later. During a production outage or security incident, these gaps and lags can be devastating. You could be staring at an “all clear” dashboard while the real issue is stuck in transit or lost in translation. **Logs can trigger false alarms: **Even when logs are collected correctly, automated monitoring can misinterpret them. For example, AWS GuardDuty once misidentified normal network traffic through a load balancer as a port scan by malicious hackers, triggering scary alerts that turned out to be false positives. Teams scrambled to respond, only to realize nothing was actually wrong; it was a quirk of how the logs were interpreted. Such false alarms waste time and can erode trust in the monitoring tools. If we **blindly trust every log-based alert** as truth, we may chase ghosts and miss the real problems. ## **Real-World Wake-Up Calls** Take **Uber’s 2016 breach**. Attackers stole millions of user records, and Uber’s leadership chose to pay off the hackers while keeping the incident hidden. For over a year, no signs appeared in the logs that customers or regulators could see. The absence of log evidence didn’t mean no breach; it meant the truth never made it into the logs in the first place. Or look at the **SolarWinds attack**. Attackers backdoored the SolarWinds Orion software used by thousands of organizations. The attackers stayed hidden in global networks for months, partly because of their manipulated logging. The malware disabled logging on targeted systems during its most malicious activities. By shutting it off during their moves and re-enabling it afterward, attackers essentially **blinded the security monitors**. Many organizations took “no log entries” to mean “no issue”, exactly what the attackers counted on. ## **The Trap of Overconfidence in Log Platforms** Most log platforms promise a single pane of glass: ingest everything, normalize it, and serve it back through dashboards and alerts. And while this looks clean, the hidden risk is that teams start trusting the picture too much. If ingestion rules are too rigid or parsing drops events, you might never notice. A polished dashboard can give false comfort. That’s why newer architectures are shifting away from brittle ingestion-first pipelines. **CtrlB, for example, takes a different approach**: schema-less log search, micro-indexing, and durable blob storage mean you aren’t forced to pre-decide what matters. You can query raw data on demand, correlate logs with traces instantly, and still keep years of history intact. Instead of compressing the truth into pre-modeled dashboards, you keep the full fidelity and context available whenever you need it. The point isn’t to distrust the platform, it’s to avoid overconfidence. The real risk comes when teams assume that “if it’s not on the dashboard, it doesn’t exist.” CtrlB’s design helps reduce those blind spots, but developers still need to approach logs with curiosity and validation, not blind faith. ## **Trust Logs But Verify** Logs are powerful, but they’re not the whole truth. If your logs say “everything’s fine,” but your traces point to failing requests or your users are reporting problems, don’t stop at the logs. Logs capture detail, but traces show how a request moves across services, and user signals tell you how it feels in the real world. Looking at them together keeps you from missing what’s really happening. Test your pipeline deliberately. Trigger a few controlled errors in staging and make sure they show up in your log search. If they don’t, you’ve found a gap you need to fix before production hits it. Keep logs safe. Store them in a way that can’t be edited or wiped, so you can trust their integrity when you need them most. Even simple checksums or append-only storage can go a long way here. And above all, don’t let a green dashboard lull you into false confidence. When something feels off, cross-check with traces, service health, and user feedback. Logs are essential, but never perfect. Treat them as one strong signal, not the only one. --- --- title: "Breaking Up with Pipelines: Why CtrlB’s On-Demand Ingestor Changes the Game" description: "“Dear Pipelines,\nIt’s not me, it’s you. I can’t handle your rigidity anymore. You cost too much, and honestly, you never change. I’ve found someone new for on-demand ingest. They understand me, they scale with me, and they don’t drain me 24/7”.\n\n Introduction Extract-Transform-Load (ETL) pipelines…" canonical: "https://ctrlb.ai/blogs/breaking-up-with-pipelines-why-ctrlbs-on-demand-in" publishedTime: "2025-08-06" modifiedTime: "2026-03-27T12:14:24+0000" author: "Adarsh Srivastava" tags: [] --- # Breaking Up with Pipelines: Why CtrlB’s On-Demand Ingestor Changes the Game _“Dear Pipelines, It’s not me, it’s you. I can’t handle your rigidity anymore. You cost too much, and honestly, you never change. I’ve found someone new for on-demand ingest. They understand me, they scale with me, and they don’t drain me 24/7”. _ ## **Introduction** Extract-Transform-Load (ETL) pipelines have been the workhorse of data engineering for decades. In a typical ETL pipeline, data is **extracted** from various sources, **transformed** into a consistent format or schema, and then **loaded** into a database or index for analysis. This approach is widely used because it ensures data is structured and ready for queries, a reliable way to bring different kinds of data together, so you can analyze it more easily. Many organizations still rely on legacy ETL pipelines to collect logs and metrics, feeding them into search indexes or data warehouses so that engineers can query recent data quickly. After all, ETL was the standard solution for integrating multiple data sources and prepping data for BI reports or debugging dashboards. It’s familiar and time-tested. But as systems grow and requirements evolve, these traditional pipelines are showing their age.  ## **The Limitations of Traditional ETL Pipelines** Even though ETL pipelines are everywhere, they still have big drawbacks. Let’s break down some of the biggest pain points that CTOs, SREs, and DevOps engineers face with always-on pipelines:  Rigidity and Fragility: “Legacy ETL pipelines are brittle. A small change in log format or a new field often breaks them, forcing painful schema updates. If not updated in time, valuable data may be dropped without notice. In short, they don’t adapt well to change. High Latency: Because ETL jobs often run in batches or on fixed schedules, they introduce delays between data generation and availability. If you archive logs to cold storage, querying them can be slow and cumbersome, often involving hours-long batch jobs. In practice, this means slower root cause analysis and delayed insights when you need answers now, not tomorrow. Maintenance and Operational Overhead: ETL pipelines need constant upkeep. You have to maintain servers, scripts, databases, and clusters that run 24/7, burning resources even when no one is querying. Teams end up babysitting pipelines, fixing failures, and scaling infrastructure instead of building a product. As data grows, so does the complexity, often requiring a dedicated team just to keep it alive. Cost Inefficiency: Always-on pipelines are costly. You pay upfront to ingest, transform, and index everything, even if most logs are never touched. To save money, teams often keep only a few days of data ‘hot’ and push the rest to S3. But cold logs then become hard to reach, needing extra jobs to query. So either pay huge bills for full retention or save money but sacrifice quick access to history. Siloed and Inflexible Data: “Traditional pipelines weren’t built for modern observability. Logs, metrics, and traces end up siloed, making correlation hard. Once data is forced into a schema, it’s stuck; dropping fields or re-parsing means rewriting pipelines and reprocessing data. This rigidity makes it difficult to explore freely, while today’s teams need to ask ad-hoc questions and get answers without pre-planning every detail In summary, legacy ETL pipelines can feel like an anchor slowing you down, **rigid, slow to update, complex to manage, and costly to scale**. They served us well in the past, but today’s cloud-native, real-time world is exposing their cracks. ## **Meet CtrlB’s On-Demand Ingestor ** Imagine if you could get rid of all that ETL baggage, no more constantly running pipelines, no more rigid schemas upfront, no more paying for infrastructure that sits idle. This is exactly the idea behind CtrlB’s on-demand ingestor. It flips the traditional model on its head: instead of collecting and indexing data **before** you ask a question. Here’s how it works and why it’s different: Storage in Cheap Object Stores: With CtrlB, you send all your logs and telemetry data straight to a durable, low-cost store (Amazon S3). Your raw data stays in object storage indefinitely if you want. This means you’re not paying for lots of SSD storage or running big databases just to keep data “hot.” All your logs (even from a year ago) can sit quietly in S3 until needed. Compute-On-Demand: When you want to search or analyze your data, CtrlB’s Ingestor spins up only when you run a query. In other words, there is no always-on processing happening in the background, burning money. The moment you hit “search”, CtrlB dynamically allocates compute power to read the relevant data from storage, parse it, and execute your query. Once you get your answer, that compute can spin back down. You’re not paying for idle servers or constantly running ETL jobs. This on-demand model is inherently more efficient. No Rigid Schemas Required: Because CtrlB defers the data processing until query time, you don’t have to predefine rigid schemas or parsing rules upfront. Logs are stored as-is. This dynamic approach means you can extract whichever fields you need on the fly. If your log format changes or a new field appears, nothing breaks; you just adjust your query to pull out the new information. There are no more “oops, we dropped that field in the pipeline” surprises. CtrlB’s schema-less ingestion ensures full-fidelity data is always available to explore. No More Indexing Pipeline Lag: CtrlB queries data directly from the source on demand. You could search yesterday’s, last week’s, or last year’s logs instantly without rehydrating archives. CtrlB was built to query logs in S3 directly without reingestion or complex reprocessing. The result is a more fluid experience: you ask and you receive, without worrying where the data lives or how old it is. Integrated Context and Correlation: Because the ingestor is part of a broader observability platform, CtrlB doesn’t just fetch raw log lines in isolation. It can also correlate logs with trace spans or service metadata. This means when you run a query, the system can pull in related traces or service context, giving you a rich, contextual answer (for example, tying an error log to the specific microservice and request that produced it). In a legacy setup, you might have to query logs in one system and traces in another & then mentally stitch them together. With CtrlB, no more bouncing between different tools or manually aligning timestamps, the context comes included, automatically. In short, CtrlB’s on-demand ingestor is like having an **ETL pipeline that only runs when you need it**, exactly for what you need. You **avoid the always-on waste and delay** of legacy pipelines. By keeping data in a cheap lake and activating compute only for queries, you get the best of both worlds: **retain everything** but **only pay when you actually use it**. The architecture is inherently cloud-native, decoupling storage from compute & it aligns costs with usage. This is a fundamentally more **elastic and scalable** way to handle observability data. ## **Real-World Benefits: Efficiency, Flexibility, and Cost Savings** Theory is great, but how does this on-demand ingestion approach make a difference in practice? Here are a couple of real-world examples and use cases that show how CtrlB’s ingestor improves efficiency, flexibility, and cost for engineering teams: Cost Savings and Long-Term Retention: One early user of CtrlB’s platform was dealing with an avalanche of logs on the order of 57 TB of log data per week across their Kubernetes clusters. They could only afford to keep a few days of data in their “hot” index due to high ingestion and storage costs. Older logs were dumped to S3, essentially turning into cold, unsearchable data. In the past, when an incident required digging into week-old logs, the team had to run Athena queries or Spark jobs on the S3 archives, a process so slow (taking hours) that they often skipped it unless absolutely necessary. After adopting CtrlB, they moved to a much simpler model: log data goes straight to S3 and stays there, and the CtrlB on-demand engine handles queries whenever needed. The impact was huge; they cut their observability costs by over 70% while actually improving their ability to look back further in time and correlate issues across services. They saved money and got better visibility. By decoupling growth in data volume from skyrocketing costs, the team no longer had to agonize over what logs to keep or throw away. Flexibility and Resilience at Scale: The benefits of on-demand, schema-less ingestion aren’t just seen in small startups; even tech giants have recognized the need for this shift. For example, companies like Uber (with its massive microservice architecture) have gravitated toward schemaless logging to handle their scale. In traditional systems, any time a service team at Uber added a new field to their logs or changed a log format, the central pipeline could break or require a schema update. This was a huge bottleneck. With a deferred ingestion approach similar to CtrlB’s, Uber’s teams could let logs evolve freely; logs are stored as-is, allowing faster debugging and more resilient operations across hundreds of changing services. The lesson here is that flexibility is not a luxury at scale, it’s a necessity. On-demand ingestion gives you that flexibility by not hard-coding assumptions upfront. New service? New log field? No problem, the system adjusts on the fly. This leads to less firefighting for pipeline fixes and more time solving actual engineering problems. Operational Efficiency and Workflow Improvements: By removing the heavy lifting from the day-to-day, CtrlB’s approach also streamlines engineering workflows.  By removing the heavy lifting from day-to-day tasks, CtrlB streamlines engineering workflows. Instead of spending hours maintaining pipelines or waiting on data, teams report big boosts in productivity and confidence with a unified, on-demand pipeline. As one CTO put it, “CtrlB’s intuitive interface made managing logs effortless, improving workflow efficiency”. Another called the ability to dynamically route different data types only when needed a “game-changer”, saving time and simplifying operations. The takeaway is simple: with agile ingestion, engineers spend less time wrangling tools and more time solving real problems. ## **Conclusion: Rethink Your Pipeline (It’s Time for On-Demand)** It’s clear that running always-on ETL pipelines, rigid schemas, and paying heavy upfront costs is no longer the best way to handle observability data. CtrlB’s on-demand ingestor offers a modern alternative: flexible, fast, and cost-effective. By separating storage from compute and activating only when you query, it removes the waste and overhead of legacy pipelines. You no longer have to choose between keeping all your logs and blowing out your budget, or throwing data away and hoping you won’t need it. With on-demand ingestion, every log is available when you need it, without the extra baggage. For CTOs, SREs, and DevOps leaders, the takeaway is simple: flexibility matters most. Switching to an on-demand model boosts troubleshooting speed, cuts costs, and frees engineers from pipeline maintenance. If you’re still up late fixing broken jobs or cleaning up log indexes, maybe it’s time to ask: Is there a better way? The future of observability is arriving on demand; time to take the leap. --- --- title: "The Silent Threat of Stale Logs: Why Retrieval Speed Matters" description: "In today’s DevOps and security environments, logs are the backbone of observability, but only if they’re fresh. Stale logs (delayed, incomplete, or hard to retrieve) hide what’s really happening in your systems. They create blind spots, slow down incident response, and leave room for attackers or…" canonical: "https://ctrlb.ai/blogs/the-silent-threat-of-stale-logs-why-retrieval-spee" publishedTime: "2025-08-02" modifiedTime: "2026-03-27T12:14:53+0000" author: "Adarsh Srivastava" tags: [] --- # The Silent Threat of Stale Logs: Why Retrieval Speed Matters In today’s DevOps and security environments, logs are the backbone of observability, but only if they’re fresh. **Stale logs** (delayed, incomplete, or hard to retrieve) hide what’s really happening in your systems. They create blind spots, slow down incident response, and leave room for attackers or outages to do more damage. It’s easy to overlook the timeliness of logs because teams often focus on _what_ they capture: errors, warnings, traces, or events. But the real value lies in **how quickly those logs surface** when they’re needed most. This post explores why retrieval speed matters, the risks of stale logs, and practical ways to design for fast, reliable access. ## **The Risks of Stale Logs** Even short delays in log visibility can snowball into bigger problems: Slower response: If logs arrive late, responders are “fighting blind.” Imagine an attack that begins at 1:00 PM, but monitoring only sees the relevant logs at 1:30 PM. That’s a 30-minute head start for the attacker. In incident response, those lost minutes often decide whether you contain an issue or watch it spiral. Longer downtime: Every extra minute of MTTR (mean time to resolution) translates into customer frustration, SLA breaches, or lost revenue. Without quick access to logs, engineers spend more time guessing and less time fixing. Missed threats: Many cyberattacks unfold in hours, not weeks. If your SIEM or detection tools are processing stale logs, brute-force attempts, insider anomalies, or privilege escalation events can slip through entirely. In short, stale logs don’t just reduce efficiency; they actively increase risk. ## **Why Speed Matters** Fast log retrieval changes the outcome of incidents. Real-time alerting: Fresh logs mean anomalies trigger alerts instantly, spikes in error rates, failed login attempts, or sudden traffic surges. This early warning system prevents escalation before users or systems feel the impact. Faster fixes (lower MTTR): Engineers don’t waste precious time waiting for logs to propagate. Efficient indexing and high-speed access surface clues quickly, allowing fixes or rollbacks within minutes. Better security (lower MTTD): Mean time to detect (MTTD) matters just as much as MTTR. Rapid retrieval lets analysts correlate suspicious events across systems immediately, cutting attacker dwell time and reducing damage. Reliable systems: Observability is only as strong as its weakest link. With fresh logs, small anomalies, rising latency, resource spikes, and failing requests can be spotted early and corrected before they ripple into outages. ## **Where Log Latency Hurts Most** ### **Microservices & Cloud-Native Systems** Modern applications rarely live in one place. They’re spread across dozens of microservices, containers, and serverless functions. Each component logs independently, but the real story emerges only when those logs are pieced together. If logs aren’t centralized and streaming in real time, debugging feels like chasing shadows. A user transaction might fail because of a downstream service, but if backend logs arrive late, engineers could waste hours hunting in the wrong service. In fast-moving cloud environments, even a few hours of delay is unacceptable. Real-time aggregation ensures logs from short-lived containers or ephemeral environments aren’t lost when instances terminate. Without this, visibility gaps grow wider, and critical evidence disappears. ### **CI/CD Pipelines** Logs also play a vital role in development velocity. Build, test, and deploy logs are the heartbeat of continuous delivery. If failures are buried in a build server and discovered hours later, entire teams lose time, or worse, faulty code gets promoted to production. Immediate log feedback keeps pipelines flowing smoothly. Failed tests or broken deployments trigger alerts the moment they occur, allowing teams to fix issues before they cascade downstream. In production, fresh deployment logs enable quick rollbacks when errors spike after a release. ## **Best Practices for Faster Log Retrieval** Designing for log speed isn’t about over-engineering; it’s about ensuring teams can act when it matters. Here are practical ways to keep logs fast and useful: Index smartly: Organize logs by fields like timestamp, level, and service so queries don’t scan everything blindly. This makes terabytes of data searchable in seconds. Cache recent data: Keep “hot” logs in memory or SSD storage for sub-second access. For most incidents, it’s the last few hours of data that matter most. Tier your storage: Store recent logs in fast, indexed storage while archiving older data cost-effectively. This balances performance with compliance needs. Stream ingestion: Build pipelines that push logs in real time. Avoid bottlenecks where logs pile up before being indexed. Use structure and context: JSON formats, metadata tags, and correlation IDs make log queries sharper and faster. They also let teams pivot quickly across services, sessions, or users. ## **Why It’s a Business Issue Too** It’s tempting to treat stale logs as just a technical nuisance, but the costs ripple outward: Downtime costs: Every extra minute offline translates to lost revenue, especially in industries like e-commerce or financial services. Compliance gaps: Many regulations require timely log analysis. Stale logs can mean missed reporting deadlines or audit failures. Team fatigue: Nothing burns out engineers faster than “flying blind” in an outage, waiting for the system to tell them what’s wrong. In other words, log speed isn’t only an engineering concern; it directly affects business resilience, compliance, and customer trust. ## **What if cold logs were just as fast?** Most teams treat cold log storage as a trade-off: it’s cheaper, but painfully slow to search. That’s why stale data creeps in, once logs move to cold storage, they’re effectively out of reach during an incident. At CtrlB, we designed an architecture that eliminates this trade-off: Disk-less, lake-first design: logs live in durable blob storage from day one. On-demand compute with micro-indexing: even cold data can be retrieved in sub-second time. Schema-less search: so you don’t need to pre-model logs before querying at scale. The result: logs never really go stale. Whether it’s data from last hour or last year, retrieval is equally fast, making cold storage just as actionable as hot. --- --- title: "Schemaless Logging: The Future of Scalable, Cloud-Native Observability" description: "Logging has come a long way. It started with plain text files, easy to write, but hard to search or analyze. Then came structured logging, using formats like JSON, which made logs easier to filter and read. After that, schema-based logging became common. Logs had to follow a fixed format, which…" canonical: "https://ctrlb.ai/blogs/schemaless-logging-the-future-of-scalable-cloud-na" publishedTime: "2025-07-25" modifiedTime: "2026-03-27T12:15:17+0000" author: "Adarsh Srivastava" tags: [] --- # Schemaless Logging: The Future of Scalable, Cloud-Native Observability Logging has come a long way. It started with plain text files, easy to write, but hard to search or analyze. Then came structured logging, using formats like JSON, which made logs easier to filter and read. After that, schema-based logging became common. Logs had to follow a fixed format, which made searching faster but also introduced fragility. A small change in log format could break pipelines or cause data loss. Today, with fast-moving, cloud-native applications spread across many services, rigid schemas often get in the way. That’s why more teams are adopting schemaless logging, where logs are stored in their raw form and structured only when needed. ### **So, What Is Schemaless Logging?** Schemaless logging means you don’t need to define a fixed structure before collecting logs. Logs are stored as-is, and you query them later. This doesn’t mean logs have no structure; it means the structure is flexible. You can extract whatever fields you need, whenever you need them. That way, if your log formats change over time (which they usually do), your system doesn’t break. You simply adjust how you query. The result? A logging pipeline that’s flexible, resilient, and easy to work with. ### **Why does schema-based logging fail at scale?** Schema-based logging becomes a burden as your system grows. It's rigid; even small changes in log format can break pipelines or make queries fail. Developers often have to wait for platform teams to update schemas, slowing things down. It’s also expensive. Every log must be parsed and indexed up front, consuming compute and storage. Worst of all, if a log doesn’t match the expected schema, it might be dropped entirely, and you lose valuable data without even knowing it. ### **How Schemaless Logging Reshapes Ingestion** Traditional log ingestion pipelines tightly couple ingestion with structure. Logs are parsed, validated, and transformed as they pass through tools like Fluentd or Logstash. This forces engineers to define schemas, write parsing rules, and make sure logs conform, all before storing them. This slows things down. Any change, a new field, or a different log format requires updates to parsers or pipeline configs. If a log doesn’t match the schema, it might be dropped or stored incorrectly. Schemaless logging removes that friction. Lightweight agents like FluentBit, Vector, or OpenTelemetry just forward logs in their raw form. Logs land directly in cloud-native storage- untouched, unparsed. ### ** Debugging Checklist for Dynamic Logs** Use this checklist when you're debugging across evolving microservices, especially in a schemaless logging setup: Can I search logs without needing to know the exact schema? Are raw logs stored as-is, even if they're malformed or missing fields? Can I extract fields (like user_id or error_code) at query time? Do I have visibility across all services, regardless of format differences? Can I correlate errors, trace IDs, and service logs together without reindexing? _If you answered “no” to any of these, your current logging setup might be too rigid. Schemaless systems like CtrlB are built to solve these exact pain points._ ### **How Does Schemaless Logging Help Cut Cloud Costs?** Schemaless logging isn’t just flexible, it’s cost-efficient. Traditional stacks like Elasticsearch or ClickHouse store logs on high-performance disks and run constantly, using compute and memory 24/7. You pay for ingestion, transformation, and idle infrastructure even when no one is looking at logs. Schemaless systems take a different path. Logs go straight to object storage platforms like S3 or Azure Blob. These are cheap, durable, and easy to scale. You don’t manage hot/warm/cold tiers or worry about SSDs. You just store the raw data. When someone searches, the system spins up compute temporarily, runs the query, and parses what’s needed. You pay only for what you use, and nothing sits idle. This model is perfect for long-term retention. You can keep months or years of logs at a low cost and still search them on demand. ### **How Does CtrlB Implement Schemaless Logging at Scale?** CtrlB is built around this architecture. It stores logs in cloud-native object storage like S3 and uses compute for search, so there’s no indexing overhead// or constant infrastructure to manage. We support full SQL and hybrid search, so you can mix structured filters (like status_code = 500) with full-text search (like message CONTAINS 'timeout'). Even though logs are schemaless, you get fast, sub-second queries thanks to smart micro-indexing and selective compute. And since CtrlB applies structure only when needed, your system stays flexible. Log formats can evolve freely, and you can still explore, debug, and analyze without rewriting pipelines. ### **Real-World Example: Why Companies Like Uber Choose Schemaless** At companies like **Uber**, log formats often change with every deployment, new services introduce new fields, or older ones evolve their output. In rigid systems, even small changes like these would break pipelines or cause indexing failures. But with **schemaless logging**, teams don’t need to update schemas constantly. Logs are stored as-is and queried dynamically when needed, which enables **faster debugging** and **resilient operations**, even across hundreds of microservices. ### **Does Schemaless Logging Mean the End of Schema?** Not really. Schema still matters, especially for metrics, dashboards, and structured reports. But logs are different. They’re messy, unpredictable, and constantly changing. With logs, what matters most is flexibility. You need to be able to search and debug without getting stuck on format rules. The future of logging isn’t about forcing order. It’s about making sense of the mess quickly, reliably, and without slowing developers down. That’s the promise of schemaless logging: fewer rules, more results. ### **FAQ: Common Questions about Schemaless Logging** **Q: What is schemaless logging? **Schemaless logging means logs are stored without enforcing a fixed format during ingestion. **Q: Does schemaless mean no structure at all? **No, it means the structure is dynamic. Logs still have fields and values, but you extract them when needed instead of defining them upfront. **Q: Why is schemaless logging useful in modern applications? **Because modern apps are fast-changing and distributed. Log formats vary often, and enforcing strict schemas slows teams down and risks data loss. **Q: Is schemaless logging slower? **Not necessarily. With systems like CtrlB that use smart micro-indexing and on-demand compute, you can achieve fast search. **Q: Can I still use SQL with schemaless logs? **Yes. CtrlB supports full SQL querying by dynamically interpreting the structure at query time. **Q: When should I choose schema-based logging instead? **If your environment is stable and logs rarely change, schema-based logging may offer performance benefits. But for most dynamic, cloud-native apps, schemaless is more flexible. **Q: What’s hybrid search? **It’s the ability to mix full-text search with structured SQL-like filters. --- --- title: "Log Search Built for Developers" description: "What Makes Traditional Log Search Hard for Developers? Today’s log search is often a tangled mess of dashboards, cryptic filters, and tool overload. Developers bounce between multiple panels, write fragile regex patterns, and still struggle to uncover what went wrong in production. Most log tools…" canonical: "https://ctrlb.ai/blogs/log-search-built-for-developers" publishedTime: "2025-07-21" modifiedTime: "2025-07-28T08:54:36+0000" author: "Adarsh Srivastava" tags: [] --- # Log Search Built for Developers ## **What Makes Traditional Log Search Hard for Developers?** Today’s log search is often a tangled mess of dashboards, cryptic filters, and tool overload. Developers bounce between multiple panels, write fragile regex patterns, and still struggle to uncover what went wrong in production. Most log tools are built around infrastructure needs, not around how developers actually debug. This fragmentation leads to lost context, constant context switching, and hours wasted correlating telemetry data. ## **What Developers Need from Log Search** Developers don’t want to hunt through disconnected dashboards or master complex query languages. They want to see the problem clearly, moving through their system step-by-step, like following a story. They need a unified experience where logs, traces, services, and alerts are connected, enabling fast, intuitive navigation from a failing service or API right down to the exact log entries that matter. When something goes wrong, you're often forced into a workflow like: Checking multiple dashboards across different tools Switching between logs, traces, and metrics in separate interfaces Writing complex regex queries or learning unfamiliar query syntaxes Losing context as you jump between systems Spending hours correlating data that should be connected CtrlB delivers exactly what developers need: a log search experience that understands your intent, preserves context, and eliminates the friction of traditional tools. It’s a log search that works the way developers think.  ## **What’s the Developer Workflow in CtrlB?** CtrlB doesn’t force developers to adapt to complex query languages. Instead, it matches how you naturally investigate issues: You begin with the service you care about, say it's auth-service or checkout. From there, you pick the operation you want to look into, like a POST /orders or GET /users/42, whose request latency has shot up recently. Then, you jump into the traces for that specific operation. You see how the request moved through the system, across services. Once inside the trace, you view only the logs tied to that exact path. Just the logs that matter help you identify the problem and resolve it faster. ## **Powerful Log Exploration Features** CtrlB offers several features to make log exploration efficient and intuitive : Instant Attribute Discovery: CtrlB automatically surfaces all available attributes (fields) in your log data, so you never have to guess or remember field names. Guided Query Building: As you type in the query box, CtrlB suggests relevant attributes and supported operators (like CONTAINS, EXISTS, or IN), helping you quickly build precise filters. Focused Filtering: Filter logs using specific attributes (e.g., service = "payment" and status_code = 500) to instantly narrow down millions of log lines to those relevant to your issue. Pattern Recognition: CtrlB highlights common structures and recurring values in your logs, helping you spot anomalies or outliers efficiently. Consistent Context Across Services: Attribute insights enable you to correlate logs across different services or microservices by common fields (like trace_id or user_id), providing end-to-end visibility for distributed systems. ## **Navigate from Services to Traces to Logs** Traditional observability tools keep data in silos: logs in one tool, traces in another, and metrics in a third. CtrlB provides unified navigation, letting you move seamlessly from a service experiencing issues to its traces and then to the specific log entries that matter. When investigating a production issue, you start with the problematic service, drill down to the trace that shows the request flow, and see the exact logs generated during that trace span. No need to switch context, lose track of correlation IDs, or dig through multiple platforms. ## **Schemaless Search That Adapts to You** You don’t need to define log schemas up front. CtrlB stores your raw logs as-is and applies structure only when you query. That means: You can ask new questions about old data, even if your log format has changed. You never lose data due to schema mismatches. You get sub-second results without re-indexing or pre-processing. This keeps log search flexible and forgiving, especially in fast-moving systems where structure evolves. **Do You Still Need Regex? **Most tools have you thinking in filters and keywords, not in services or flows. You end up: Writing fragile queries that break with one typo Flipping through tabs to piece together context Manually aligning timestamps across services CtrlB’s Log Explorer lets you search logs without relying on dashboards or regex skills by focusing on intuitive, developer-friendly features: On‑Demand Structure: Logs stay raw until you query them. No schemas to define or dashboards to maintain when formats change. Unified, Contextual Navigation: Jump straight from a service to its traces and then to the exact logs you need, all in one interface. Guided Query Building: As you type, CtrlB suggests fields and operators, so you never have to memorize syntax or resort to regex. Pattern Recognition: Quickly spot recurring values or anomalies in your logs, so you can isolate issues without sifting through noise. By removing the need for regex expertise and dashboard maintenance, CtrlB empowers developers to find answers quickly and efficiently. Debugging starts with context, not complexity. ## **Go One Step Further with CtrlB** Even with powerful search, structured logs, and guided filters, sometimes the information just isn’t there. Maybe the right data was never logged. Maybe you need to see what the system is doing now, not what it did a minute ago. That’s where CtrlB takes things further. With Live Debug, you don’t rewrite code or push a new build just to add a print statement. You can add a virtual logline or tracepoint directly to your running service. The moment a request hits that code path, you see exactly what it’s doing: variable values, condition checks, and even the stack if you need it. It’s still log-based debugging, but now the log line is yours, and it appears when and where you need it. That’s what makes CtrlB a developer-first mindset. It doesn't stop at search. It helps you finish the story. ## **Built for Debugging, Designed for Developers** Most observability tools were built for monitoring, displaying visually appealing graphs, and alerting you when a threshold is crossed. But when you’re debugging a production issue, that’s not enough. You need fast, iterative querying. You need to move from a failing service to its trace to the exact logs, without switching tools or matching timestamps. You need to inject visibility right where it’s missing, without redeploying. CtrlB is built for that moment. Not to monitor what you already know might break, but to help you investigate what you don’t. This isn’t just a tooling upgrade. It’s a shift in how we approach debugging. From dashboards to flows. From metrics to behavior. From passively observing to actively understanding. Because when production breaks, you don’t need another dashboard. You need answers. ## **FAQ: Developer-Centric Log Search** **Q: Can CtrlB replace grep or Kibana for developers? ** A: Yes. CtrlB supports fast, schema-less search across logs, while CtrlB Live Debugging adds trace-aware, context-rich log navigation. **Q: Does CtrlB require setting up dashboards or alert rules? ** A: No. CtrlB is built to reduce setup overhead using the service and trace context instead of dashboards. **Q: Can CtrlB trace logs back to services and APIs? ** A: Yes. CtrlB maps logs to the exact service and trace operation that generated them. **Q: How can I leverage CtrlB’s attribute insights to improve my debugging efficiency? ** A: Start by glancing at the auto‑discovered attributes to see what fields exist. Pick a high‑value attribute (like error_code or user_id) to quickly narrow your scope. Use the suggested operators to pivot filter by top values or pinpoint anomalies. As you drill down, rely on consistent fields (like trace_id) to trace an issue end‑to‑end across services. This cuts straight to the root cause. **Q: Can CtrlB handle large-scale log data without blowing up my cloud bill? ** A: Yes. CtrlB uses object storage and a pay-per-query model, so you can store everything without paying for always-on indexing. --- --- title: "How to Optimise Queries in CtrlB" description: "Logs are messy, especially in cloud-native systems where structure is an afterthought. CtrlB was built for that reality; its schema-less, on-demand data lake lets you search anything. But just because you can throw everything into a query doesn't mean you should. Query optimisation isn’t about…" canonical: "https://ctrlb.ai/blogs/how-to-optimise-queries-in-ctrlb" publishedTime: "2025-07-16" modifiedTime: "2025-07-28T08:45:31+0000" author: "Adarsh Srivastava" tags: [] --- # How to Optimise Queries in CtrlB Logs are messy, especially in cloud-native systems where structure is an afterthought. CtrlB was built for that reality; its schema-less, on-demand data lake lets you search _anything_. But just because you _can_ throw everything into a query doesn't mean you _should_. Query optimisation isn’t about limiting your power; it’s about getting to the answer faster. Whether you’re debugging a spike in status 500 requests, tracing a flaky deployment, or chasing an elusive edge-case bug, how you write your query directly impacts how quickly you get clarity. Here’s how to do it well: ## 1. Think Narrow First, Broad Later The biggest mistake engineers make is starting broad: ``` error ``` It technically works, but it’s slow, noisy, and rarely actionable. Instead, lead with what you already know: the service, environment, log level, and status code. Narrow first with filters; you’ll make both engine and brain work less. Example: ``` service="auth" AND env="prod" AND level="error" ``` This approach lets the engine prune irrelevant data before diving into a more expensive text search. **Query Example** ``` body contains “error” ``` **Result: **Slow, noisy, overwhelming **Query Example** ``` service="auth" AND env="prod" AND level="error" AND "token expired" ``` **Result: **Focused, fast, actionable ## 2. Embrace Structure, Even in Unstructured Logs Your logs may not have a strict structure, but CtrlB parses fields on the fly, so use them! Query with fields like status, user_id, or request_path. You don’t need to clean or pre-index logs in advance. Treat logs more like a database: query with fields, not just strings. _Pro Tip: Use the Attributes Panel to auto-add fields to your query. Check the boxes next to fields (like status=500), and CtrlB automatically groups and connects them for you. No typos, no guesswork, just relevant filters._ ## 3. Avoid Accidental Full Scans Wildcards and NOT logic are tempting but expensive: timeout → forces full-text scan, slows everything down. status != 200 → no pre-filtering; checks every log entry. Phrase your logic to say what you _want_, not just what you _don’t want_. Example: Rather than: status != 200 Prefer: status = 500 OR status = 404 ## 4. Use Specificity for Diagnosis When drilling into an issue, be surgical: ``` service="auth" AND env="staging" AND level="error" AND "unauthorized" ``` Start with the broadest field filters you know, then add specific terms as you learn more. ## 5. Time Ranges Are Everything Logs accrue fast. Searching “everything” means scanning terabytes, unscalable! Always scope your query to a relevant time window. ``` @timestamp > now - 30m ``` Narrow your time window whenever possible. Ask: _When exactly did this happen?_ Zoom in accordingly. _Pro Tip: CtrlB’s bar-graph timeline makes this even easier. Drag across a spike or dip to instantly set your query time range, speeding up RCA._ ## 6. Trace It Back One of CtrlB’s superpowers is trace-log correlation. When a log looks suspicious, click into its trace to see the upstream and downstream context, other services, related logs, and response times. Instead of running 10 separate queries, one trace might answer them all. _Pro Tip: In the Side Panel, open the “Surrounding” tab to view the five logs immediately before and after your selected entry, perfect for finding root causes or effects in event chains._ ## 7. Explore & Customise Your Results After running a query: Expand rows to see full log texts inline. Add columns for any field you care about (e.g., user_id, endpoint). Customise the Summary column to show combined key values (useful for scanning high-cardinality data at a glance). _Pro Tip: Most actions (add/remove filters, columns) can be done from the Side Panel with a single click (+ to add a filter, – to exclude, table icon to add a column)_ ## 8. Stay Synced and Collaborate CtrlB keeps panels, filters, and tables in sync. Save queries, copy dashboard permalinks, and share context instantly with your team for faster triage and learning. For sensitive data, fine-grained access controls in Settings keep your logs secure and your audit posture tight. ## Quick Checklist: Smarter Querying in CtrlB Start with fields, not keywords. Always set a reasonable time range. Phrase logic for what you want, not just what you don’t. Leverage UI tools to save time and reduce errors. Share the knowledge: save queries, share links, and collaborate securely. Final Thought: Great queries are like great questions: specific, contextual, and always refined by what you already know. Optimizing your approach in CtrlB gets you to answers faster and helps your team improve, together. --- --- title: "Data Strategy for SREs: Usability Over Cleanliness" description: "In cloud-native environments, teams collect massive volumes of logs, traces, and metrics. But when a service goes down or a user reports a critical bug, that data often becomes noise. The issue isn’t a lack of information; it’s a lack of context. Legacy observability stacks are built to collect…" canonical: "https://ctrlb.ai/blogs/data-strategy-for-sres-usability-over-cleanliness" publishedTime: "2025-07-10" modifiedTime: "2026-03-27T12:16:20+0000" author: "Adarsh Srivastava" tags: [] --- # Data Strategy for SREs: Usability Over Cleanliness In cloud-native environments, teams collect massive volumes of logs, traces, and metrics. But when a service goes down or a user reports a critical bug, that data often becomes noise. The issue isn’t a lack of information; it’s a lack of context. Legacy observability stacks are built to collect everything, but they’re not built to help engineers find what they need when it matters most. Site Reliability Engineers (SREs) don’t need more data. They need usable data. CtrlB rethinks this approach. Instead of trying to make every log clean and structured, it focuses on making messy data searchable, fast, and useful in real time. The result is a data strategy that enables SREs to debug production issues quickly and with less friction. ## **The Legacy Problem: Clean Data, Slow Debugging** Traditional observability tools are built on rigid schemas and dashboards. They expect logs to follow a fixed format. They assume you’ll build dashboards before the problem occurs. They rely on tags to correlate logs, traces, and services. But in the real world, log formats change. Stack traces vary. Fields go missing. One malformed JSON line can break ingestion entirely. In a system with dozens or hundreds of microservices, maintaining clean and consistent telemetry is nearly impossible.  Even when data makes it in, querying it can be painful. Engineers have to remember field names, match exact labels, and rely on dashboards that may not reflect the latest code changes. A typical outage flow looks like this: dashboard alerts, then a flurry of slow, complex queries to figure out what actually happened. That delay costs teams time, and sometimes, trust. ## **CtrlB’s Approach: Make the Mess Usable** CtrlB takes a usability-first approach to observability. It accepts that logs will be messy and services will evolve. Instead of forcing a structure, it focuses on searchability and context. ### **Schema-less Log Ingestion** CtrlB ingests logs as they are raw JSON blobs, key-value pairs, or unstructured text. There’s no need to define a schema up front. Every log is stored without requiring upstream formatting. This means new services can onboard quickly. If one team logs user_id, another logs userId, and a third emits a nested object, it all works. You don’t lose visibility just because field names don’t match. You also don’t need to enforce strict conventions across teams. That flexibility is critical in fast-moving environments where code and logging libraries change often. ### **Real-Time Micro-Indexing** To make logs searchable at speed, CtrlB uses micro-indexing. Each field and token is indexed individually at ingestion time. This allows for fast, field-aware queries without needing to rebuild large indexes or predefine field types. If you're searching for user_id=1234 or just the string "timeout", results come back in milliseconds across terabytes of data.   This eliminates a major source of friction for SREs. In traditional systems, searching unstructured logs is slow. With CtrlB, you can search across structured and unstructured data with the same speed and flexibility. ### **Trace-First Correlation** When something breaks, the root cause often spans multiple services. CtrlB links logs, spans, and service metadata automatically into a single, trace-first view. That means you can jump from a single log line into the full trace, seeing upstream and downstream context, related spans, and even correlated log entries. This correlation works without requiring tags or manual instrumentation. CtrlB leverages OpenTelemetry to propagate context and link data together behind the scenes. You don’t need to manage trace IDs yourself or worry about inconsistent tagging. This makes debugging faster and more intuitive. Instead of piecing together clues from multiple dashboards, you follow the path of a request, and all related logs are already stitched together. ### **No More Dashboard Dependency** In legacy tools, dashboards are the primary interface for observability. But dashboards break when code changes. Fields disappear, metrics drift, and charts stop reflecting reality. CtrlB replaces static dashboards with a search-based workflow. You enter a query, get instant results, and visualize data as needed, no YAML, no panel maintenance. If you want to filter logs by region and user, you run a query. The system responds in real time. You’re not limited to what was pre-built. This flexibility is especially valuable during incidents. You’re not bound by the dashboards you made last week. You can ask questions on the fly and get immediate answers. Everything we’ve covered so far, schema-less ingestion, trace-first correlation, and search-driven workflows, is about making observability usable. But there’s another side to that equation: making observability manageable.  SREs and platform engineers often find themselves editing YAML on dozens (or hundreds) of machines. Rolling out a config change means SSH’ing into hosts or writing brittle automation. Upgrading agents requires careful coordination. Collectors consume more resources than expected. One misconfigured node stops sending logs entirely, and nobody notices until it’s too late. CtrlB includes a powerful control plane to centralize management of observability pipelines across logs, metrics, and traces. Built on OpenTelemetry standards and inspired by OpAMP, it abstracts away the grunt work of collector management. Instead of managing configs per node, you define them once. Instead of deploying agents per tool, you route all telemetry through your existing OpenTelemetry collectors, with one central interface to manage them. Together, they solve both sides of the observability equation: You don’t just collect telemetry,  you understand it. You don’t just manage collectors,  you control them at scale. ## Latency Spikes in a SaaS CI/CD Pipeline **Scenario: **A developer platform offers a CI/CD service used by thousands of teams. Users start reporting that builds are hanging randomly. The metrics dashboard shows a spike in latency, but not where or why.  **Problem: **The system uses a mix of services written in Go, Node.js, Rust, Java and Python, each with different logging formats. Some logs are structured JSON, others are plain text. Dashboards are missing context, and logs aren’t tagged consistently with trace IDs. **With CtrlB: **Engineers run a broad query across the entire pipeline: message contains "build started" AND duration > 120s They jump directly into traces linked to these logs, revealing that a new artifact storage service is introducing random I/O stalls. The culprit was a change in the file chunking algorithm, which increased disk pressure on some nodes. The fix is deployed, and latency normalizes, all within 20 minutes of investigation. **Why it works: **Shows CtrlB handling **multi-language, inconsistent logs**, correlating them **without tags**, and helping SREs debug **cross-service latency**, not just errors. ## **The Real-World Impact for SREs** Teams using CtrlB see faster incident resolution and less operational overhead. When issues happen, they don’t waste time tweaking queries or fixing dashboards. They get straight to the problem. This also reduces cognitive load. Engineers don’t have to memorize field names or switch tools constantly. They debug in one place, with one query, and all the context they need. And as services evolve, CtrlB’s approach remains resilient. A new version of a microservice might change its logging library or rename fields, but your searches still work. Because the system doesn’t rely on a brittle structure, your observability doesn’t break when the app changes. ## **Why This Data Strategy Works for SREs** SREs need observability tools that are fast, flexible, and usable in real-world scenarios. That means: Search over the structure. Don’t wait for perfect logs. Use what you have. Correlation over collection. Don’t rely on tags. Follow the trace. Context over dashboards. Ask questions. Get answers. CtrlB’s data strategy aligns with how modern systems behave. It embraces unstructured logs, adapts to change, and delivers usable insights without ceremony. ## **Conclusion** Legacy observability focused on collecting more. CtrlB focuses on making it more usable. By removing the need for strict schemas, rigid dashboards, and manual correlation, CtrlB empowers SREs and developers to debug production issues faster. You don’t need perfectly clean data to solve problems. You just need to be able to find the answer, and CtrlB makes that possible. In the world of SRE observability, what matters most is how quickly you can get answers, not how neat the dashboard looks. ## **🔍 FAQ: Data Strategy for SREs** ### **What is a data strategy for SREs?** A data strategy for SREs focuses on making observability data usable, searchable, and context-rich, not just clean or structured. It enables fast debugging, real-time incident response, and system understanding without forcing rigid schemas or manual dashboard upkeep. ### **Why do traditional observability stacks fail SREs?** Legacy stacks rely on strict schemas, manual tagging, and prebuilt dashboards. They struggle with inconsistent logs, slow queries, and high maintenance. During outages, these systems delay root cause analysis by making it hard to search or correlate logs and traces. ### **How does CtrlB improve observability for SREs?** CtrlB replaces dashboard-driven monitoring with **search-first observability**. It ingests logs without requiring schemas, builds fast micro-indexes, automatically correlates logs and traces, and lets SREs ask real-time questions during incidents without needing perfect data. ### **What is schema-less log ingestion, and why does it matter?** Schema-less log ingestion means logs are accepted in any format, JSON, key-value, or free-form text. No field mapping or standardization is required. This flexibility ensures logs are never dropped and new services can onboard quickly, even with inconsistent formats. ### **How does CtrlB correlate logs and traces without tags?** CtrlB uses OpenTelemetry for automatic context propagation. It links logs, traces, and services by request or span ID, even if logs lack explicit tags. This “trace-first debugging” approach helps engineers view the full request flow with all related data. ### **Why is no-dashboard observability better for SREs?** Dashboards can break when code changes or fields shift. CtrlB removes this dependency by letting engineers run real-time queries and visualizations on demand. This reduces overhead and ensures observability always reflects the current system state. ### **Can CtrlB help debug production issues faster?** Yes. By making all logs searchable instantly and linking them to traces, CtrlB shortens Mean Time to Resolution (MTTR). SREs can search symptoms directly (e.g., “500 errors in checkout”) and immediately trace back to the cause, even with unstructured data. ### **What makes CtrlB different from other observability tools for SREs?** CtrlB is built around **usability, not just collection**. It doesn’t require clean data or perfect tagging. Instead, it delivers fast, search-based observability, schema-free ingestion, and trace-first correlation, helping SREs work with real-world systems, not idealized ones. ### **How does CtrlB support querying unstructured logs?** You can use simple queries like status >= 500 AND service = "checkout" to filter logs without knowing exact field names. CtrlB tolerates missing or malformed data, so searches still work even when logs are inconsistent or partially structured. ### **Is CtrlB suitable for cloud-native environments?** Absolutely. CtrlB is designed for distributed, fast-evolving architectures. It handles schema drift, service sprawl, and high-throughput telemetry without breaking. This makes it ideal for cloud-native observability and modern SRE workflows. --- --- title: "Unstructured Data at Scale: Why Real-World Data Is Messy and How to Make It Useful" description: "The Reality of Unstructured Data \nMost data today isn’t neatly organized in tables. Instead, it comes as logs, emails, chat messages, API payloads, and sensor streams. This information is valuable, but doesn’t follow a set format, making it hard to search or analyze. Logs are a great example. Every…" canonical: "https://ctrlb.ai/blogs/unstructured-data-at-scale-why-real-world-data-is-" publishedTime: "2025-07-04" modifiedTime: "2026-03-27T12:17:53+0000" author: "Adarsh Srivastava" tags: [] --- # Unstructured Data at Scale: Why Real-World Data Is Messy and How to Make It Useful ## **The Reality of Unstructured Data** Most data today isn’t neatly organized in tables. Instead, it comes as logs, emails, chat messages, API payloads, and sensor streams. This information is valuable, but doesn’t follow a set format, making it hard to search or analyze. Logs are a great example. Every system emits them, from backend services to mobile apps. They hold clues about what’s happening, but those clues are hidden in blocks of raw text. Cloud storage makes it easy to save huge amounts of logs, but understanding them, especially in large volumes, remains complex. Whether you’re debugging, investigating outages, or tracking trends, unstructured data slows you down unless you have the right tools.  As systems grow more distributed, unstructured data is the norm, not the exception. ## **Why Is Unstructured Data Hard for Observability?** At small volumes, you can search or filter logs with simple tools. But as data grows, things get complicated. Inconsistency is the main problem. Logs come in many formats: JSON, plain text, or custom layouts. Even within a single app, log formats can vary wildly. Without a standard structure, machines can’t easily process or categorize this information. Most tools expect data to be structured. They need clean fields and predictable formats. When data doesn’t fit, the tools break or require heavy engineering work to transform and clean it. At scale, you're dealing with millions of log lines per minute across dozens of services. Even small inconsistencies start causing big problems. Unstructured data at scale is hard not because it’s unreadable, but because most tools aren’t built for its volume and variety. Teams end up fixing pipelines instead of using the data.  ## **What “At Scale” Really Means** Handling unstructured data at scale brings new challenges: More Sources: Modern environments have hundreds of microservices, APIs, and tools, each logging data in its own way. More Volume: Even small logs add up quickly. Every request can generate dozens of lines, leading to massive datasets. More Diversity: Logs can be structured, messy, nested, or missing context. There’s rarely a single schema. More Urgency: You often need to debug issues or investigate incidents in real time. Waiting for data to be processed isn’t an option. This is the real cost of scale. It doesn’t just strain your infrastructure; it reveals the flaws in your tooling. Most platforms aren’t designed for messy, rapidly changing data. They assume structure, order, and predictability. At scale, that breaks. To handle unstructured data effectively, your system must embrace the mess. That means: Storing raw logs as-is Structuring data only when you query Supporting schema drift and deeply nested formats Only then can you get value from unstructured data, without constant rework or brittle pipelines. ## **What Most Tools Get Wrong** Most log tools expect strict formats. If your logs change or are inconsistent, these tools struggle. They require you to define fields, normalize formats, and pre-parse logs, adding delay and effort. Logs may be inconsistent, deeply nested, or completely unlabeled. Some are cleanly structured, while others are filled with multiline errors, stack traces, or malformed fields. Often, there's no common schema across services, just raw, unpredictable output. Traditional tools struggle with this kind of variety because they rely on predefined formats or schemas. When that structure is missing or breaks, so do the tools. For example:  ## **CtrlB’s Approach: Built for Unstructured Logs** CtrlB takes a different approach. It ingests raw logs without forcing a structure. This means you don’t have to clean or reformat your data first. Schema-less ingestion: Logs and traces are stored as-is. Micro-indexing: Fast, context-rich search, even over large datasets. On-demand structure: You can ask new questions of old data, even if formats have changed. With CtrlB, you get sub-second results, regardless of data size or messiness, and it adapts to your data, not the other way around. Soon, CtrlB will also support time-series, vector, and semantic search, all from one engine. This will let teams analyze trends, cluster events, and explore patterns without switching tools or rewriting pipelines. ## **Conclusion: Stop Fighting the Mess** Your data will never be perfect, and that’s okay. The right system doesn’t expect structure. It finds meaning when you need it, no matter how your data looks. CtrlB turns chaos into clarity without extra work from your team. Instead of forcing data into rigid molds, it adapts to real-world messiness. That’s how modern observability should work: by surfacing insight from chaos, not by demanding perfect data. In the real world, data is never clean. But your understanding of it can be. --- --- title: "The Dashboard Trap: Why Graphs Aren’t Enough" description: "Dashboards are great. They help teams monitor system health, track key metrics, and spot when things start to go off track. When latency spikes or error rates rise, a good dashboard shows you when it started and often where. Mature teams build them thoughtfully and rely on them daily.\nBut…" canonical: "https://ctrlb.ai/blogs/dashboards-are-not-debuggers" publishedTime: "2025-06-30" modifiedTime: "2026-03-27T12:18:46+0000" author: "Adarsh Srivastava" tags: [] --- # The Dashboard Trap: Why Graphs Aren’t Enough Dashboards are great. They help teams monitor system health, track key metrics, and spot when things start to go off track. When latency spikes or error rates rise, a good dashboard shows you when it started and often where. Mature teams build them thoughtfully and rely on them daily. But dashboards have limits. They show that something happened, might tell you what happened, but rarely explain why. They compress rich telemetry into trend lines and charts, which is great for spotting patterns. But when things break, you need more than a pattern. You need the story behind it. ## **What Dashboards Don’t Show** **The One Request That Broke Everything ** Dashboards show you metrics like 99th percentile latency, but they won’t point to the exact request or user that tipped the system over. You know something was slow. You don’t know who or what caused it. **The Actual Sequence of Events ** You see a spike. But was it a retry storm? A timeout cascade? Dashboards don’t follow a request across services or show the logs it triggered along the way. **The Whole Picture ** Metrics in one view, logs in another, traces in a third, you’re left juggling tabs, aligning timestamps, and guessing how things connect. **The Questions You Didn’t Plan For ** Incidents never follow the script. Want to know which user actions led to checkout failures during the flash sale? If you didn’t pre-build that view, your dashboard can’t help. _Dashboards show you there’s a fire. They don’t tell you where it started or how far it spread._ **Now picture this:** It’s 3 AM on Black Friday. The checkout just went down. You’re losing $50K a minute. Dashboards are lit up - error rates climbing, latency spiking, CPU maxed out. You even know it started at 3:14 AM, right after a deployment. But what failed? Checkout runs on 47 microservices. Is it payments? Inventory? Database? Something else? You know there’s trouble, but not what triggered it. ## **The Real Need: Context, Correlation, and Root Cause** Modern systems don’t break cleanly. A single user request can bounce between dozens of services, span multiple containers, and touch regions across the globe, all in sub-seconds. When something goes wrong, fixing it isn’t about staring at a spike on a chart. It’s about tracing that request **end-to-end**. For that, you need to: Trace the request end-to-end Pull the logs tied to each service it touched See user IDs, regions, and deployments all in one flow In our checkout example, that might mean tracing a single cart failure across payments, inventory, auth, and DB. This isn’t something dashboards were made to handle. You need context, and you need to move fast. ## **CtrlB: The Context-First Approach** CtrlB Explore is built for moments like this, when alerts are going off and dashboards are full of red, but you still don’t know what exactly broke. Instead of showing you that _something_ failed, CtrlB helps you find the **specific request** that triggered the failure. You search: _user_id:12345 checkout failed_. Instantly, you get the full trace of that request, along with the logs it produced and the services it touched. No regrets about not having a dashboard view ready.  And it doesn’t stop at one request. CtrlB lets you **see the full journey**, how that request flowed from the mobile app to the API gateway, then to the payment service, and down to the database. Every hop, error, timeout, and retry all stitched together automatically. Got a mystery bug from three months ago still haunting your postmortems? CtrlB stores everything in S3. You can query old incidents like they just happened, with no missing data and no reindexing needed. And you don’t need to switch between five tools just to get the story straight. CtrlB brings logs, traces, and service metadata into one place, already correlated, so you’re not wasting time syncing timestamps across Grafana, Splunk, and Jaeger. Dashboards help you see when something’s wrong. CtrlB helps you find out what went wrong, where, and why. ## **Where Dashboards End, CtrlB Picks Up** Dashboards still matter. They’re great for watching traffic, uptime, and overall system health. But when things break, you don’t want to be staring at a spike, guessing what went wrong. You want to see the exact request that failed. The services it touched. The logs it left behind. You want a system that tells the full story. That’s what CtrlB was built for. Stop guessing. Start understanding. Start with CtrlB. --- --- title: "The Cloud Dilemma: Balancing Observability at Scale" description: "Modern engineering teams face a fundamental tension: cloud observability promises deep insight, but achieving it often forces trade-offs. Such dilemmas can hamper visibility or agility unless you rethink the architecture. CtrlB’s unified observability data lake is designed to eliminate the…" canonical: "https://ctrlb.ai/blogs/the-cloud-dilemma-balancing-observability-at-scale" publishedTime: "2025-06-25" modifiedTime: "2026-03-27T12:20:57+0000" author: "Adarsh Srivastava" tags: [] --- # The Cloud Dilemma: Balancing Observability at Scale Modern engineering teams face a fundamental tension: cloud observability promises deep insight, but achieving it often forces trade-offs. Such dilemmas can hamper visibility or agility unless you rethink the architecture. CtrlB’s unified observability data lake is designed to eliminate the tradeoffs. It ingests any schema, stores data natively on cloud object storage, and runs hybrid-search queries via a serverless, MPP query engine. We’ll talk about 5 dilemmas here: ## **Complexity vs. Control** Observability solutions today sit between two extremes. Using a managed SaaS (e.g., Datadog, Azure Sentinel, Splunk Cloud) offers ease of use but locks you into hidden constraints and often incurs additional costs (e.g., egress, billing tiers). Running open-source stacks (Elastic, Loki, Prometheus, etc.) on your cloud gives control, but at the price of great complexity and maintenance overhead. In practice, teams find themselves burdened with managing indices, clusters, and pipelines just to keep the lights on. Self-hosting stacks like ELK on SSD drives up cost and complexity. All your telemetry data lives in our object storage (S3). We support any schema of observability, security, or analytics data with full SQL and hybrid search. In plain terms, you no longer need separate systems for logs, traces, services, or alerts; all data lands in one platform. In practice, that means fewer moving parts and more centralised control, without sacrificing the flexibility to ask any question of your data. CtrlB is more cost-effective than most SaaS observability vendors, and its total cost of ownership is significantly lower than self-hosting Elastic. ## **Flexibility vs. Fragmentation** Another key frustration in observability is that different data types (logs, traces, alerts, geospatial data, ML vectors, etc.) often live in disconnected silos. Best-of-breed tools or databases exist for each modality, but making sense of that data is a headache. Teams lose agility because correlating an event across logs can mean stitching data from two or more platforms. Industry analysts note that the observability stack is fragmented in most companies' traces, and logs are often on different platforms and are hard to connect. This fragmentation forces either expensive engineering efforts or limited point solutions. CtrlB’s answer is a **hybrid search on a single engine**. We ingest all telemetry into one unified store and support multiple search modalities on the same data. Hybrid Search combines different search modalities (full-text, analytical, time-series, vector) in one engine, letting you query and analyse data from a single source of truth. You are not limited to a homebrew query language. CtrlB fully supports ANSI SQL on any field, plus rich full-text lookups, without requiring data reshaping. This flexibility, all in one place, breaks the tradeoff between freedom and fragmentation. For example, consider a security analyst investigating an incident: they might keyword-search logs, filter by time-series anomaly. With CtrlB, they stay on one platform. Under the hood, we handle schema-less ingestion (any JSON or log schema), micro-indexing for full-text fields. The alternative (using three or four different datastores) is simply eliminated. No more stitching ELK with Prometheus and Jaeger – CtrlB gives one unified observability fabric. ## **Cost vs. Coverage** One of the hardest trade-offs is financial. Every gigabyte of telemetry can cost money, so many teams sample, filter, or drop data to keep budgets under control. The prohibitive cost of storing terabytes of telemetry data forces teams to sample data, creating blind spots in the system. For example, ingesting 700 GB/day (a moderate load in a busy cloud) costs roughly $734k/year in Azure Sentinel, $547k in QRadar, and over $500k in Splunk. Even at 100 GB/day, Splunk Cloud would be around $80k/year. And Datadog’s indexing model can exceed $3M/year for 100B events. In short, traditional vendors make unfiltered observability a luxury: organisations end up throwing away low-value logs or chopping retention from 90 days to 7 just to survive. CtrlB’s architecture slashes those costs. We store logs raw in cost-effective Parquet files on S3, achieving 15–20x compression over raw log text. In practice, CtrlB’s storage footprint is tiny.  These efficiencies translate into dramatic dollar savings. CtrlB can handle hybrid searches up to 10× faster while cutting costs by up to 90%. For example, the cost of retaining 30 TB/month (1 TB/day) for 90 days is only about $6,100 on CtrlB, versus roughly $55k on Elasticsearch or $67k on Splunk and hundreds of thousands on SaaS platforms. In other words, CtrlB lets you store and search all your telemetry (logs, traces, services) at near-unlimited scale for a fraction of the bill. By avoiding heavy indexing and using cheap object storage, we remove the need for data sampling and “cheaper tiers”. You get full coverage without breaking the bank.  ## **Speed vs. Overhead ** Traditional log platforms (like ELK) achieve low-latency results by pre-indexing every field and shard, which demands constant CPU usage and expensive SSD storage. On the flip side, “cheap” stores (like Loki on S3) minimize indexing but trade off query performance. This is the classic speed vs overhead dilemma: either you invest heavily in infrastructure or accept slower queries. CtrlB breaks this tradeoff with a compute-on-read, massively parallel architecture. Logs are indexed only once at ingest, and queries are executed by a fast, cloud-native query layer that dynamically spins up compute at scale. The result? High performance without high cost. Instead of provisioning SSD-backed nodes or long-lived clusters, CtrlB spins up just enough compute when needed and tears it down when done. This model gives you fast access to log data at scale while keeping ongoing infrastructure costs low. In everyday use, engineers get interactive query speeds on typical workloads without over-provisioning, pre-processing, or vendor lock-in. You no longer have to choose between visibility and performance. With CtrlB, you keep all logs searchable and accessible, stored cost-effectively on S3, and pay only for compute when queries are run. ## **Scaling Without Sacrifice** Finally, as environments grow, observability must grow with them, without forcing painful choices. In traditional “shared-nothing” architectures, more data requires more nodes, more indexing, and more human ops. One overloaded Elasticsearch node can slow the entire cluster, and multi-tenant contention becomes a risk. The promise of cloud-native systems is agility and scale, but the reality often looks like spiralling overhead. As infrastructure grows, so do the layers of tooling needed to keep it running: autoscaling groups, container orchestrators, security scanners, observability stacks, storage tiers, log forwarders, cost optimisers, the list never ends. For many teams, observability becomes yet another layer to manage, configure, and scale manually. Self-hosted solutions like ELK or Loki require careful tuning of shards, replicas, and retention policies. Add in pipeline management (FluentBit, Vector), tracing backends (Jaeger, Tempo), and you’re now maintaining a small fleet of observability infrastructure, often without a dedicated observability engineer. Even in SaaS setups, complexity doesn’t vanish. Now you're managing vendor billing dashboards, noisy alert thresholds, agent upgrades, and feature limitations tied to license tiers. Teams throttle data volume or delay queries to cope, essentially sacrificing responsiveness as they scale.  CtrlB’s decoupled, serverless design sidesteps this. Storage and compute are fully independent. You dump data into S3 and let our query service elastically scale Lambdas on demand. In decoupled architectures, more data doesn’t necessarily mean more nodes – you can scale compute independently of storage growth. In practical terms, CtrlB handles modern loads gracefully. Whether you ingest 1 TB/day or 7 TB/day, the ingestion pipeline saturates at the same per-second rate, and costs grow linearly only with storage (at pennies/GB). As data volumes increase, CtrlB’s query time increases only modestly, a scaling story most rivals cannot match without disproportionate overhead. This model lets you focus on what matters, understanding your systems, not on maintaining another system that helps you do that. ## **Conclusion** Observability shouldn’t demand sacrifices. CtrlB’s unified data lake approach means you no longer juggle multiple tools or compromise on data fidelity. By storing everything in cheap object storage, indexing smartly (once in compressed Parquet), and running queries in a massively parallel serverless engine, we eliminate the usual tradeoffs in speed, cost, and flexibility. Teams get full coverage, sub-second analytics across vast datasets, all while cutting total cost of ownership by an order of magnitude. In short, CtrlB turns these observability dilemmas upside-down. You gain simplicity and control, flexibility without fragmentation, coverage without ruinous cost, speed without heavy overhead, and seamless scale without compromise. Ready to unify your telemetry? Learn more at or reach out to see a live demo. Your cloud observability just became affordable and fast. --- --- title: "Control Plane: One Plane To Control Them All" description: "CtrlB provides a unified control plane for telemetry. It centralises management across all your OpenTelemetry collectors, with no need for special agents on every host." canonical: "https://ctrlb.ai/blogs/control-plane-one-plane-to-control-them-all" publishedTime: "2025-06-23" modifiedTime: "2025-06-25T08:41:55+0000" author: "Pradyuman" tags: ["pradyuman"] --- # Control Plane: One Plane To Control Them All Modern cloud environments generate massive volumes of telemetry (logs, metrics, traces) from every service and host. Each tool often requires its agent (e.g., Fluent Bit, Prometheus exporter, Splunk UF), which leads to the deployment and configuration of dozens or hundreds of collectors. In practice, operators end up manually editing agent configs on every node – a tedious, error-prone task. Telemetry collection at scale demands a structured control plane: tasks like upgrading agents, rotating credentials, or pushing new settings must be handled centrally. Without such a control plane, observability management becomes overwhelming and costly. Multiple agents and silos: Each observability tool (logging, metrics, tracing) spawns its data pipeline and agent, leading to configuration sprawl and duplicated effort. Resource overhead: Adding multiple collectors, one for logs, one for traces and one for metrics adds so much resource overhead. Manual upgrades and drift: Rolling out agent updates or policy changes requires SSH’ing into each machine or writing brittle automation. These challenges illustrate the need for a centralised way to manage observability pipelines, not just build them. While telemetry pipelines help standardise data collection and routing, deploying and operating them across a fleet is non-trivial. A control plane abstracts this operational burden by letting teams configure, deploy, and update collectors centrally. Instead of logging into each host or duplicating logic per tool, teams gain a consistent interface to manage how data is collected, transformed, and routed — all from one place.  CtrlB provides a **unified control plane** for telemetry. It centralises management across all your OpenTelemetry collectors, with no need for special agents on every host. Data from your services flows through your existing collectors, and CtrlB lets you define dynamic rules for filtering, tagging, or routing that data, all from one place. Instead of juggling configs across tools like Fluent Bit, Prometheus, or Jaeger, you gain a consistent interface to manage your observability pipelines at scale. Inspired by concepts like OpAMP, CtrlB simplifies operations so teams can update configs centrally without manual intervention on individual nodes. ## **Key Features of CtrlB Control Plane:** Unified Control Across Multiple Collectors: CtrlB gives you one central control plane that talks to all your OpenTelemetry collectors. Instead of running separate agents for logs, metrics, and traces on each host, you keep your existing OTel collectors in place and let CtrlB’s control plane pull in data from all of them. That means you can manage, configure, and upgrade every collector from a single dashboard, simplifying your stack without swapping out what already works. Dynamic Multi-Destination Routing: CtrlB lets you define rule-based routing so that data is sent to multiple backends as needed. For example, error logs can be forwarded to Datadog and a security SIEM in parallel. This removes the need to run a different agent for each tool. In practice, one CtrlB pipeline can feed many destinations simultaneously. Centralised Configuration & OpenTelemetry Management: With CtrlB, you keep every pipeline configuration in one place, your control plane, so there’s no hunting through YAML files on dozens of hosts. Want to adjust how often you collect data, filter out specific information, or send your telemetry to a different destination? Simply update it once in CtrlB, and it handles the rest. We have built on the same core concepts that power the OpenTelemetry Agent Management Protocol. Apply your update once in CtrlB, and it pushes the new configuration out to every collector automatically, no manual restarts, no SSH logins, no headaches. Health Monitoring & Self-Healing: CtrlB continuously tracks the health and performance of each agent (CPU/memory usage, throughput, errors). If an agent fails or lags, CtrlB can automatically restart it. In short, it automates the essential management tasks that OpenTelemetry outlines for at-scale deployments, ensuring your pipeline stays up without human firefighting. Visual Config Builder: To avoid drowning in YAML, CtrlB offers a user-friendly UI (and CLI) for building pipelines. You can visually map data flows and transformations, instead of hand-coding complex configs. This is a huge benefit when processing terabytes of telemetry, handwritten configs quickly become unmanageable.  CtrlB Control Plane is **open source** and built on open standards. It works with the existing OpenTelemetry ecosystem, ensuring you stay vendor-neutral. Because it is community-driven, contributions and custom extensions are welcome. ## **Next Steps** CtrlB (Telemetry Control Plane) is available now as an open-source project. It integrates with Kubernetes and Linux via containerised or packaged deployments. To get started, visit the CtrlB documentation and GitHub repository, install the control plane and set up your first pipeline. In minutes, you’ll see all your telemetry flowing through a single pane of glass, dramatically reducing operational overhead and costs. In summary, CtrlB gives DevOps teams a unified observability pipeline and control plane, turning a mass of disjoint agents into one coherent system. By centralising log routing, agent management, and health monitoring, CtrlB helps you scale telemetry reliably and affordably. Learn more or contribute on GitHub, and take control of your observability data with CtrlB here: [https://github.com/ctrlb-hq/ctrlb-control-plane](https://github.com/ctrlb-hq/ctrlb-control-plane) --- --- title: "Beyond Observability: Real-time Debugging in the age of cloud native" description: "Tldr: Most observability vendors are collecting data to surface insights later. CtrlB, through MCP and live instrumentation, enables asking new questions on the fly, something traditional observability just doesn’t accommodate. In short, Live debugging isn’t talked about enough in observability…" canonical: "https://ctrlb.ai/blogs/beyond-observability-real-time-debugging-in-the-ag" publishedTime: "2025-06-20" modifiedTime: "2026-03-27T12:17:23+0000" author: "Adarsh Srivastava" tags: [] --- # Beyond Observability: Real-time Debugging in the age of cloud native ### Tldr: Most observability vendors are collecting data to surface insights later. CtrlB, through MCP and live instrumentation, enables asking new questions on the fly, something traditional observability just doesn’t accommodate. In short, Live debugging isn’t talked about enough in observability circles, but it should be. It’s not a replacement for logs/metrics/traces, but a critical evolution that bridges observability with developer actionability. From data-at-rest to data-in-motion, CtrlB covers both. ## ** The Blind Spots of Traditional Observability** Despite the hype around observability, live debugging rarely takes center stage in the conversation. Most tools and discussions focus on the “three pillars”- logs, metrics, and traces, which help monitor infrastructure but leave developers blind to the current behavior of their code. Traditional observability is passive and retrospective: it tells you what happened, not what’s happening now. Live debugging changes that. It brings a real-time, developer-centric layer to observability, one where you can ask new questions on the fly and get answers instantly, without redeploying. Yet this capability is still treated as optional or risky by many teams, in part because legacy tools weren’t built for it. CtrlB challenges that mindset. With MCP, debugging becomes an active part of the observability stack- dynamic, interactive, and built for modern systems where change is constant and insights can’t wait. ## **Real-Time Debugging: Closing the Gap** Here, Real-time debugging comes into play. Instead of predefining logs or metrics, developers can instrument running systems on demand. CtrlB’s Live Debugger, for example, acts like a “super debugger” attached to your app: from the IDE, you set tracepoints or logpoints on the fly and immediately inspect variables and stack traces. In other words, you can ask new questions of a live service in seconds, without any hotfixes or restarts. This on-demand instrumentation means that when a live issue occurs, an engineer can pinpoint and fix it almost as fast as they can see it, vastly speeding up production troubleshooting. ## ** CtrlB’s Debugging Stack: From Passive Observability to Active Control** CtrlB powers this transformation with a smart, layered debugging stack: A cloud-hosted backend server (Atlas) that acts as the orchestration and control plane Developer IDE plugins (e.g., for VSCode) Lightweight application-side agents (Heimdall agents) A natural-language intermediary layer: MCP (Model Context Protocol) MCP is the bridge between the IDE’s chat interface and CtrlB’s plugin. It parses developer intent from natural language and turns it into structured commands. For instance, you might type “Add a tracepoint to this function if order.amount > 1000,” and MCP will understand and translate that into a precise debugging command. The plugin then routes this command to Atlas, which securely instructs the relevant Heimdall agent to inject the tracepoint into the live application. When the condition is met, live snapshot data is streamed back to your IDE, including stack traces, local variables, and request context, all in real time. This architecture enables true interactive debugging without restarts or redeployments. It allows developers to stay in the flow and treat their running systems as live, programmable entities. CtrlB’s architecture is designed with modern developer workflows in mind. It integrates seamlessly with popular IDEs, supports multiple languages (Java, Node.js, Python, Go), and runs comfortably in cloud-native environments, including Kubernetes and serverless.  Built for teams who need control, flexibility, and transparency at scale. If you’re debugging across dozens of services, working in regulated environments, or building custom internal tooling, CtrlB gives you the Legos to build what you need. Real-time debugging is part of a broader movement toward **developer-first observability**, where engineers can inspect code behavior as easily as infrastructure health. But no two teams operate in the same way. Want to trace a 3rd-party library during a production incident? Need to debug a broken webhook in a serverless lambda? Looking to correlate a tracepoint with logs stored in your own S3-based data lake? CtrlB is the way to go! ## **Solving Critical Incidents, Without the Panic** Real-time debugging isn’t just for edge cases; it’s a practical tool in daily operations. Imagine an e-commerce checkout service throwing 500 errors. With CtrlB, a developer can immediately drop a tracepoint on the payment handler and get insight into variable state and control flow without touching production code. Or if a background job is behaving inconsistently, you can snapshot its execution path on the fly to identify the root cause. CtrlB’s users have reported saving hours of debugging time during high-stakes moments, simply because they didn’t have to wait on logging redeploys or dig through noisy metrics. When time matters, CtrlB provides the immediacy and precision engineers need. ## ** Use Cases: Debugging Without Disruption** Real-time debugging opens the door to faster triage, richer insights, and lower stress. Some practical examples:  **Broken Payment Flow**: Insert a tracepoint on-the-fly in the checkout microservice to log parameters and isolate a failing input.  **Intermittent Errors**: Add a snapshot probe only during specific failures, e.g., when a variable throws an exception.  **Performance Bottlenecks**: Attach a live trace to a function to monitor latency, without affecting live traffic or alerting thresholds.  **Third-Party Library Failures**: Track how a 3rd-party SDK is behaving in production without modifying the source.  **Kubernetes and Serverless Debugging**: Instrument ephemeral pods and functions in-flight to troubleshoot misbehaving services. ## ** CtrlB’s Data Lake: Not Anti-Logging, Just Smarter Logging** CtrlB isn’t anti-logging- it’s anti-guesswork. It includes a scalable backend that acts as a **data lake**, storing long-term logs, traces, and snapshots. The difference is that CtrlB allows for contextual querying and dynamic correlation across this data. For example, you can correlate a snapshot triggered by MCP with raw logs from the same timeframe, or trace a user flow across services. The result: less noise, more clarity. This hybrid model of **logging everything but analyzing only when needed** lets developers balance cost and context. Logs are still valuable, but they become one part of a more flexible, developer-centric debugging workflow. ## **Looking Ahead: Observability Meets Interaction** The future of observability isn’t just about watching systems but also about interacting with them. Imagine an intelligent feature that lets you inspect, debug, and respond to them in real time, where debugging doesn’t interrupt flow, observability becomes actionable, and where developers finally get answers. With features like natural-language query support via MCP, dynamic instrumentation, and bidirectional IDE integration is a shift from passive observability toward interactive operations so that debugging becomes a conversation and your system becomes explorable and explainable. CtrlB redefines the purpose of debugging. With a long-term data lake as the foundation and dynamic, real-time instrumentation on top, you get the best of both: complete historical context and zero-noise live debugging. --- --- title: "How To Turn Log Noise Into Real Signals" description: "Logs are loud. Your systems shouldn’t be. In any production system, logs are everywhere: APIs, background workers, frontend proxies, and downstream services. And when they all start shouting at once? The hard part of observability isn’t getting logs in or storing them, it’s making sense of them…" canonical: "https://ctrlb.ai/blogs/how-to-turn-log-noise-into-real-signals" publishedTime: "2025-06-11" modifiedTime: "2026-03-27T12:22:32+0000" author: "Adarsh Srivastava" tags: [] --- # How To Turn Log Noise Into Real Signals ## **Logs are loud. Your systems shouldn’t be.** In any production system, logs are everywhere: APIs, background workers, frontend proxies, and downstream services. And when they all start shouting at once? The hard part of observability isn’t getting logs in or storing them, it’s making sense of them when you need answers. Most systems treat logs like static records. But CtrlB, with schema-less querying, contextual metadata attachment at query time, and on-demand compute, can **turn noise into clarity**, without the overhead of rigid pipelines or dashboards. And unless your logs are connected, stitched across services with context, they won’t be helpful. They’ll just be noise. This is where **context propagation** and **trace-log correlation** change the game. Engineers often log everything: requests, variables, errors. But without a shared context, it becomes a chaotic stream of disconnected events. Debugging feels like guesswork,  trying to connect dots across services with no shared language. You might see: ``` [Service A] Request received at /checkout [Service B] Payment initiated [Service C] Inventory checked ``` But **no shared ID, no linkage, no context**. So when debugging a slow checkout, you're stitching these together by guesswork, not logic. **Context propagation** means that stitching these messages together requires context, typically metadata such as trace_id or request_id that persists throughout the lifecycle of a request. This context travels from service to service, function to function. That way, even if your request touches five microservices, they’re all tagging their logs with the same trace ID: ``` [TraceID: abc123] [Service A] Request received at /checkout [TraceID: abc123] [Service B] Payment initiated [TraceID: abc123] [Service C] Inventory checked ``` Now, you can see how a single user interaction moved through your stack without being buried under unrelated noise. With context now consistently flowing across services, you're not just tagging requests; you're enabling a deeper level of observability. That’s where trace-log correlation comes in. Context propagation makes correlation possible. **Trace-log correlation** is what uses that shared trace ID to link logs and traces together. That way, when you're viewing a trace, say, for a slow checkout, you can see all the logs that belong to just that trace, right inside your trace view. Just click the trace, and see everything that happened during that exact request, down to log statements in your backend or app server. Now, what that looks like in practice is when a real production issue hits. This is where tracing makes all the difference, giving you a clear view of how a request moves through your system. **Real-world example:** Let’s say a user reports that checkout is taking forever. You pull up the trace for that request. You see: Service A: Auth passes in 40ms Service B: Inventory takes 80ms Service C: Payment takes 2.8s You click the span for Service C and immediately see the logs: ``` [TraceID: abc123] PaymentService - Stripe API call started [TraceID: abc123] PaymentService - Stripe timeout after 3s [TraceID: abc123] PaymentService - Retrying... ``` You didn’t have to search logs. You didn’t need to know what time the issue happened. You didn’t even need to know which service was responsible. The trace led you straight to the logs you needed. That’s a signal. ## **Why does this matter more as your system scales?** In monoliths, you could live without context. One request lived in one process. But in microservices, especially in Kubernetes or serverless environments, you don’t control how logs move. Requests travel across containers, nodes, and even regions. Without context propagation, every hop is a blind spot. Without trace-log correlation, every alert leads to a search party. With both, you get end-to-end visibility with minimal effort. ## **Let Signal Find You: P99, Throughput & Error Ranking** When you’re staring at thousands of log lines, the noise can be overwhelming. CtrlB flips this by focusing first on the signals and how each service is performing right now. Instead of wading through endless stack traces, CtrlB gives you a signal-focused view: services are automatically **ranked by P99 latency and error rate**. You also get throughput (ops/sec) insights, revealing anomalies instantly. Whether a service is quietly dying or suddenly overwhelmed, CtrlB surfaces what matters most. **_What’s P99 Latency**?_ _P99 latency shows the **slowest 1% of requests,** the rare, worst-case slowness that averages hide. It’s how you catch degraded user experiences before they spiral into incide_nts. **_What’s Throughput**?_ _Throughput measures how many operations a system handles per second. Like counting cars at a toll booth, low throughput might mean a jam or a dead service. CtrlB helps you spot both in real time._ ## **CtrlB’s Take** We support context propagation via our trace system, just clean OTEL-based tracing. We correlate logs with traces. Search by trace ID, error keyword, and CtrlB will return the precise logs tied to your issue, not the entire haystack. You can go from alert → trace → log → root cause in seconds, not hours. ## **Conclusion: If you want answers, give your logs context** Log noise isn’t just annoying, it’s dangerous. It hides root causes, delays fixes, and drains teams. With context propagation and trace-log correlation, you don’t just log what happened. So the next time something breaks, your logs won’t yell. They’ll tell you. --- --- title: "The Role of AI in Observability: Hype or Hope?" description: "Applications today aren’t just a few servers with predictable traffic; they’re made up of hundreds of moving parts, spread across clouds, running on Kubernetes, and changing constantly. Observability has always been about one thing: clarity. But in the age of microservices, Kubernetes, and…" canonical: "https://ctrlb.ai/blogs/the-role-of-ai-in-observability" publishedTime: "2025-06-06" modifiedTime: "2026-03-27T12:23:20+0000" author: "Adarsh Srivastava" tags: [] --- # The Role of AI in Observability: Hype or Hope? Applications today aren’t just a few servers with predictable traffic; they’re made up of hundreds of moving parts, spread across clouds, running on Kubernetes, and changing constantly. Observability has always been about one thing: clarity. But in the age of microservices, Kubernetes, and distributed everything, clarity is harder and more valuable than ever. Now, AI promises to bring speed, clarity, and even some predictions. The question is how much of that promise is real, and how much is just hype? From anomaly detection to summarising noisy logs, AI is being pitched as the next big unlock in observability. And while the hype is loud, the reality is nuanced. ## The Promise of AI in Observability AI has shown tangible benefits across certain layers of the observability stack: Anomaly Detection: ML models can flag spikes in latency, drops in throughput, or weird request patterns that a human might miss. Log Summarisation: NLP-powered tools are beginning to cluster, group, and summarise logs, helping teams focus on broader trends, not just raw text. Noise Reduction in Alerts: AI systems can learn which alerts are actionable, which ones are false positives, and which combinations of signals really matter. Pattern Recognition Across Services: Especially in high-cardinality environments, AI can detect subtle trends across service interactions. These are real wins, and they’re helping teams reduce Mean Time to Detect (MTTD), spot regressions faster, and find issues without brute force log digging. So where’s the catch? ## The Limits: AI Can’t Fix Bad Data Here’s the thing: AI is only as good as the data it sees. In most production systems, observability data is messy. Logs are unstructured. Traces are missing spans. Services don’t share consistent metadata. There's no context propagation. And the telemetry that AI depends on isn’t built with correlation in mind. It means **throwing AI on top of a broken or noisy telemetry foundation won’t magically produce insight.** It will produce more noise. That’s why AI looks great in demos but struggles in real systems, not because the AI is bad, but because it’s working with data that isn’t connected useful. ## Clarity Still Needs Structure AI excels at pattern recognition, but it struggles without **structure, context, and intent**. For instance, your checkout flow is slow. AI might tell you there's a latency spike, and then what? You'll still need: Logs correlated with traces Context propagation across services Clear service boundaries and ownership Runtime state visibility tied to each request **That’s architecture work, not algorithm work.** AI can assist, but it cannot replace that foundational clarity. ## CtrlB’s Approach: Focus on Fundamentals At CtrlB, we believe in building for clarity first. We correlate logs and traces via context propagation We support schema-less, trace-aware querying at scale We enable natural language search on top of structured signals We keep high-value data hot, low-value data archived, no noise, just clarity And yes, **AI can sit on top of that.** But only when the basics are clean. ## The Road Ahead AI in observability isn’t just hype. It’s evolving fast. LLMs are getting better at parsing logs. Correlation engines are learning smarter ways to prioritise alerts. And there’s real promise in copilots for incident response. But clarity still begins with your telemetry, not your tooling. If you want AI to work for you, start by structuring your logs, tracing your services, and stitching your stack with context. **Observability isn’t about choosing between human intuition and AI assistance.** It’s about enabling both by building a system that makes clarity inevitable. --- --- title: "Observability Is Not Just Three Pillars" description: "We’ve all heard it: Observability has three pillars: logs, metrics, and traces.\n That’s how you know it was a marketing person. Somewhere along the way, someone even decided Observability needed a versioning system. And if you’ve been around long enough, you’ve also heard we’re in “Observability…" canonical: "https://ctrlb.ai/blogs/observability-is-not-just-three-pillars" publishedTime: "2025-05-30" modifiedTime: "2026-03-27T12:23:43+0000" author: "Adarsh Srivastava" tags: [] --- # Observability Is Not Just Three Pillars We’ve all heard it: Observability has three pillars: logs, metrics, and traces**. ** That’s how you know it was a marketing person. Somewhere along the way, someone even decided Observability needed a versioning system. And if you’ve been around long enough, you’ve also heard we’re in “Observability 2.0” now. Because apparently, we needed semantic versioning for debugging practices, too. (Coming soon: Observability 3.0, now with extra dashboard regret.) But jokes aside, something has changed. The old model doesn’t scale, not with distributed systems, ballooning data, and dev teams that need answers before lunch. ## Observability 1.0: More Data, More Problems The original observability model focused on the three core data types: Logs to track events and debug issues Metrics to monitor performance and thresholds Traces to understand request flows across systems Each came with its backend system, its UI, and usually its own data silo. So you had more data, but not necessarily more clarity. Querying logs could take minutes. Traces might only capture partial context. And metrics often tell you something’s wrong_,_ but not why. In reality, developers didn’t need three separate tools; they needed one fast, flexible system that could answer questions, regardless of the data type. ## **Observability 2.0: From Pillars to Purpose** Observability 1.0 focuses too much on what data you collect and not enough on how you use it. In modern, cloud-native environments, we have plenty of data; what’s missing is context. And that’s what we call Observability 2.0;  Observability 2.0 is a mindset shift: From collecting more data → to making sense of it faster From dashboards and silos → to correlated, real-time exploration From rigid schemas → to schema-less, service-aware context And at **CtrlB**, that’s exactly the path we’re taking. ### **CtrlB’s Take: Observability Without the Wait** We built CtrlB to fix the part nobody talks about: the _lag_ between your question and the answer. Traditional tools are slow, siloed, and bloated. You end up memorising dashboard layouts instead of understanding your system. CtrlB is different by design: **▶ One Query, All Your Signals** Logs, traces, and services, not in separate tabs or different tools, but together. Search once, explore in real time, and pivot instantly between layers. No context-switching, no delay. **▶ Disk? What Disk?** We don’t write to disk. Not because we’re trying to be edgy, but because it’s slow, expensive, and unnecessary. Your logs stay in object storage (S3), and we query them on demand. That means no bulky indexing pipelines, no inflated infra bills, and no stale data. **▶ Compute-On-Demand, Not Always-On Waste** CtrlB’s Ingestor spins up only when you run a query. It reads logs and correlates them with trace spans or service metadata, right when you need it. You don’t pay for idle resources. You don’t wait for ETL jobs. You just get answers. **▶ Schema-Less** You don’t need to define your log format before shipping it. CtrlB figures it out when you query, dynamic fields, custom formats, whatever you’ve got. No rigid schemas. No “oops-we-dropped-that-field” surprises. Just full-fidelity data, ready to explore. **▶ Service-Aware, Not Log-Blind** A pile of raw logs doesn’t help if you don’t know which service they came from or what they were doing. CtrlB stitches logs to the services and operations behind them, giving you a view of the system, not just lines in a file. **▶ Trace-to-Log in One Click** With CtrlB, you just click, and the logs behind that span show up instantly, no timestamps to copy, no filters to guess.  ### **Context Propagation: Your Breadcrumb Trail for Root Cause** In modern systems, a single user action can trigger a storm of background service calls. Somewhere in that is the one operation that broke things. That’s why CtrlB treats context propagation like a first-class citizen. Request IDs and metadata travel across services automatically, so you’re never staring at logs wondering where they came from. You get a trace of the request journey, not just where it started and ended, but the messy, interesting middle bits too. And this is where root cause analysis gets useful. You don’t just get a slow span; you click into that span and see the exact logs that happened during that window, across every relevant service. No digging through separate tools, no guesswork, making the RCA easier. Because when you’re debugging, you’re not chasing abstract metrics. CtrlB lays those breadcrumbs out for you, in context. ### **The Future Isn’t Pillars. It’s Context.** We’ve seen enough “three-pillar” architectures to know how they usually end: with three tools, three bills, and a frustrated team. The point of observability was never to collect telemetry for its own sake; it was to _understand _systems. To debug fast. To fix things. That future doesn’t come from stacking more tools. And no, we’re not calling this Observability 3.0 - even though we probably could. But that’s how it starts, right? A couple of version bumps, a new logo, and suddenly the same old problems feel new again. We’ll skip the rebrand. Let’s just build observability that works. ### **Not Another Cheaper Datadog** The first wave of observability startups was all just **cheaper Datadogs**. Same three pillars and slightly better pricing (until it wasn’t). And now something has shifted. As Charity Majors said, this year we’ve started seeing something new: tools that look more like cheaper offerings, context-aware tracing, OTel-native pipelines, and unified querying. Because the future isn’t “which pillar are you collecting?” It’s “How fast can you go from signal to root cause?” And for that, you need more clarity and a system that gives a damn about engineers. --- --- title: "Microservices Logging: The Power of Distributed Tracing" description: "When teams transitioned from monoliths to microservices, debugging didn’t become easier; it became messier. Every service now generates its own logs. However, when a single user request traverses six different services across various containers or clusters, those logs do not naturally connect. You…" canonical: "https://ctrlb.ai/blogs/microservices-logging" publishedTime: "2025-05-24" modifiedTime: "2026-03-27T12:24:21+0000" author: "Adarsh Srivastava" tags: [] --- # Microservices Logging: The Power of Distributed Tracing When teams transitioned from monoliths to microservices, debugging didn’t become easier; it became messier. Every service now generates its own logs. However, when a single user request traverses six different services across various containers or clusters, those logs do not naturally connect. You find yourself jumping between files, dashboards, and guesswork.   In this environment, logs are necessary but insufficient. What you need is context. That’s where distributed tracing comes into play. With OpenTelemetry (OTEL) and CtrlB, that context is no longer out of reach ### ** ** **Why Logging in Microservices Is Hard** Logs are supposed to help us understand what went wrong. But in microservices, they often leave us with more questions than answers: Services talk to each other, but their logs don’t. You can’t follow a request across services easily. You end up with thousands of log lines but no real signal. Correlating logs by timestamp or request ID is slow and often unreliable Traditional log management tools weren't built for this scale or this complexity. In short, logs are fragmented, and that makes root cause analysis slow, messy, and frustrating! ### ** Distributed Tracing: The Missing Link Between Logs** A trace is the full journey of a request.** **It shows where the request started, which services it touched, how long each step took, and where things slowed down or failed. Each step in that journey is a **span**: a timestamped unit of work inside a service. Traces give you the structure: which services were involved, how long each step took, and where the bottlenecks were. Logs give you the details: what exactly was happening inside the service during that span. Without tracing, logs are just isolated fragments. But together, **logs + traces** tell the full story. ### ** OpenTelemetry (OTEL): The Backbone of Context** So, how do you link logs and traces? That’s where **OpenTelemetry** (OTEL) helps. It’s an open-source, vendor-neutral standard for telemetry data. OTEL helps your services emit consistent metadata, like trace IDs and span IDs, so logs and traces can be tied together. It auto-generates trace and span IDs. It injects those into HTTP headers and logs. It supports many frameworks and languages out of the box. It keeps you from getting locked into a specific vendor. Once your services are instrumented with OTEL, every log line can carry trace metadata, and your traces can act as high-level maps to all the related logs. ### **CtrlB + OTEL: A Powerful Combo for Microservices Teams** Now that you have logs and traces, how do you use them effectively? That’s where **CtrlB** steps in. CtrlB ingests both your logs and OTEL spans and connects them. You can: Click on any slow span → instantly see logs across services from the same time window. Search logs or trace metadata, no manual correlation needed. Investigate exemplars of slow or failed requests and drill into their logs directly. CtrlB reads your logs directly from object storage like S3 and performs on-demand search and matching, so you get both cost-efficiency and speed. ### **Live Debugging with CtrlB: No Redeploys Required** Once you've found the issue, how do you confirm it, without restarting or redeploying? That’s where **CtrlB Debug** comes in, an IDE plugin that brings **live instrumentation** into your workflow. You can add tracepoints (non-breaking breakpoints) or dynamic log lines in running code, without stopping or redeploying it. CtrlB supports OTEL-based dynamic instrumentation, which means you can add traces at runtime. You can inspect variables inside the function that handled the failing request. This turns observability from a passive monitor into an active investigation tool. ### ** A Real-World Example: Checkout Flow Debugging** A customer reports that the checkout is slow. You open **CtrlB Explore** and pull up traces from around the time of the issue. One trace pops up, the checkout-service span took 8 seconds. You click it. CtrlB automatically pulls in related logs from checkout, payment, and cart services, all linked to that one trace. In the logs, you spot something odd: a retry loop in the payment-service running too many times. You jump to your IDE, open the CtrlB plugin, and drop a **live tracepoint** in the retry block, no redeploy needed. The tracepoint reveals the root cause: a config error is causing unnecessary retries. You fix it. Push the change. And just like that, checkout is fast again. ### **From Logs to Fix, Without Leaving Flow** This is the power of using logs, traces, and live debugging as a single workflow: CtrlB Explore helps you find the problem. CtrlB Debug helps you prove and fix it. Both work together to reduce friction, context-switching, and guesswork. ### **Final Thoughts: Microservices Deserve Better Logging** Logs are still essential, but in microservices, they aren’t enough. You need correlation. You need context. You need tools that go beyond monitoring: tools that help you investigate, give you the ability to see the full journey of a request, confirm, and fix. With OpenTelemetry and CtrlB, you get all of that. Trace the issue. Inspect the logs. Drop a live tracepoint. Fix it on the spot. --- --- title: "Why Disk-Less Data Lakes Are the Future " description: "Let’s face it, working with logs and large-scale data systems can be a real grind. If you’ve ever stared at a spinning loader bar after hitting “search” or wondered why your storage bill looks like a phone number, you’re not alone. The tools we’ve used for years just aren’t keeping up. Traditional…" canonical: "https://ctrlb.ai/blogs/why-disk-less-data-lakes-are-the-future" publishedTime: "2025-05-15" modifiedTime: "2025-06-12T03:45:22+0000" author: "Adarsh Srivastava" tags: [] --- # Why Disk-Less Data Lakes Are the Future  Let’s face it, working with logs and large-scale data systems can be a real grind. If you’ve ever stared at a spinning loader bar after hitting “search” or wondered why your storage bill looks like a phone number, you’re not alone. The tools we’ve used for years just aren’t keeping up. Traditional data lakes, built around disk-based storage, were never meant for real-time data, massive scale, or the kind of flexibility modern teams need. But what if you could ditch the slow storage layer altogether? That’s where **disk-less data lakes** come in, and platforms like CtrlB are already showing how this approach makes log management not just faster, but smarter and cheaper. ### **The Problem with Traditional Data Lakes** When data lakes first came into the picture, they felt like a game-changer. Just throw in all your data: structured, unstructured, logs, metrics, and figure things out later. No need to set up strict rules or worry about structure upfront. It sounded perfect. But that dream hasn’t quite worked out. In reality, a lot of companies find themselves stuck. Traditional setups like Hadoop or Elasticsearch (ES) rely on virtual machines (VMs) and local disk storage, and that comes with significant baggage: Redundancy: You end up storing the same log or data point multiple times. Duplication: Systems like ES create extra shards and replicas, inflating storage and compute costs. Durability limits: Local disks on VMs are failure-prone. Even with replication, data loss isn’t rare. High compute costs: You pay upfront to parse and index everything, even if you never query most of it. Slow data access: Cold logs are often archived or offloaded, making them slow and expensive to retrieve. Painfully slow queries: When data lives on disk, search times suffer. The more data you have, the slower it gets. ### **So What’s Disk-Less, and Why Does It Matter?** Disk-less data lakes break the marriage between storage and compute. Instead of tying everything to disks, they: Store data in cheap, scalable object storage like Amazon S3. Spin up compute only when you need it, and store for as long as you need it. Blob storage (like AWS S3, GCS, etc.) is built for scale. It’s not tied to a physical disk or virtual machine (VM). That means: You don’t need to worry about managing servers or storage disks. You get 99.99999999999% durability (11 nines). Your data is stored across multiple locations by default, meaning built-in redundancy without you having to set anything up. Compared to storing logs on VMs, which can fail, require maintenance, and aren’t built to scale indefinitely, whereas blob storage is cheaper, safer, and way more flexible. It’s a subtle architectural shift with massive implications.  ### **CtrlB’s Take: Diskless, Durable, and Efficient** At CtrlB, we’ve seen firsthand how painful it can be to work with traditional systems, especially when your team needs answers immediately. While we’re positioning ourselves as a data lake, our architecture already leans into disk-less principles like: It stores raw logs directly on blob storage, with no disk dependence. It uses on-demand compute, pulling and processing only what you query. Logs live in efficient, scalable storage. Cold logs aren’t constantly loaded; they’re just fetched when needed. Durability is inherited from blob storage, meaning your logs are safer and cheaper to store. Our search is fast users get what they need without waiting forever. It builds context dynamically, with sub-second latency. ### **It’s Time to Rethink the Stack** The old model made sense in the early days of cloud, when data was smaller and real-time wasn’t a requirement. But today’s systems are noisy, distributed, and always-on. Disk-less isn’t just a performance boost, it’s a shift in how we think about scale, flexibility, and cost. And once you’ve worked with a platform that doesn’t make you wait around for logs, it’s hard to go back.** **If you’re: Tired of bloated observability bills Sick of maintaining costly VM-based setups Storing more logs and querying It’s time to rethink the foundation. CtrlB is the future of data lakes: fast, durable, and ready when you are. Check out ctrlb.ai and see how fast log search and analytics can be. --- --- title: "From S3 Chaos to Unified Insights\n" description: "Observability is a growing cost centre for most engineering teams. The more you scale, the more logs you generate. And while storage has gotten cheaper, most observability tools haven’t kept up.  To save money, many teams send logs to cold storage like S3 after a few days. It’s cheap, sure, but not…" canonical: "https://ctrlb.ai/blogs/from-s3-to-insights-making-archived-logs-useful-ag" publishedTime: "2025-05-10" modifiedTime: "2026-03-27T12:19:46+0000" author: "Adarsh Srivastava" tags: [] --- # From S3 Chaos to Unified Insights Observability is a growing cost centre for most engineering teams. The more you scale, the more logs you generate. And while storage has gotten cheaper, most observability tools haven’t kept up.  To save money, many teams send logs to cold storage like S3 after a few days. It’s cheap, sure, but not exactly usable. The moment you need to investigate an old issue or audit past behaviour, you’re stuck digging through frozen data that’s hard to search and even harder to make sense of. S3 is built for storage, not search, correlation, or real-time debugging. This post is about CtrlB’s approach: how we help teams search logs sitting in S3 directly, without rehydration or building complex pipelines, and how that changes the way you think about storing and accessing your data. ## **The Common Tradeoff: Retention vs Cost** The typical setup works like:  Logs are ingested, processed, and indexed in real time You keep them “hot” for a week or two, because it’s expensive Everything older gets pushed to S3 or Glacier as archived data That data is no longer part of your regular searches If you do need to query old logs, you either: Don’t bother because it’s too much of a pain Spin up a batch job to pull and process the logs (like ETL) Hope someone already saved a copy somewhere But none of these options is ideal. They’re slow, clunky, and usually require writing custom glue code or one-off scripts. You either wait hours for someone to run SQL through an external query engine, write a custom script, or, worse, you give up and guess. ## **Why Cold Storage Usually Means Cold Insight** It’s easy to store logs in S3. It’s cheap and scales forever. But once your data is there, it’s usually out of reach. That’s because most platforms charge a premium for fast, searchable storage, so teams only keep a few days or weeks of logs “hot". After that, the data is pushed to cold storage, where it’s hard to access or query. You either wait hours for a response or just give up and move on. This makes it really hard to spot patterns over time. If something breaks today, and it looks familiar, you might not be able to pull up the logs from the last time it happened. Say you want to compare Black Friday traffic this year to last year, the data’s there in S3, but it’s buried. And without that context, you’re left guessing. With CtrlB, you don’t have to make those tradeoffs. You can keep all your data in S3 for as long as you want and still get answers instantly when you need them. ## **What CtrlB Does Differently** CtrlB flips this setup. Unlike traditional observability tools that charge for ingestion, CtrlB separates storage from compute. Your data stays in S3. CtrlB provides the compute layer on top, when you need to search, explore, or debug. That means you can keep logs forever, without paying for the things you’ll never look at. And when you run a query later, whether it’s a few hours or a few months after the logs were written, CtrlB’s query engine kicks in and gives you the results on demand. ## **Logs, Traces, and Services In One View** CtrlB doesn’t stop at search. Once you find the log line or trace of interest, CtrlB correlates it with the related service, upstream calls, and downstream effects. You get full system context in a single interface, built for devs and SREs. There’s no bouncing between tools or stitching JSON in your head. Everything you need to debug is in one place, even if the data is stored in a million folders on S3. ## **Case Study: Scaling Logs Without Scaling Costs** One of our early users was dealing with 7– 8TB of logs weekly across their Kubernetes infrastructure. They were only keeping a few days' worth of hot data, mostly because of the high costs associated with ingestion and indexing charges. They tried traditional cold storage workflows, sending logs to S3, then using Athena or writing Spark jobs to search later, but it just wasn’t practical, as the RCA (root cause analysis) took hours. Often, they skipped it altogether unless the issue was critical. With CtrlB, they moved to a simpler model: Logs are written directly to S3 The data is kept for as long as you need Everything else is accessed through CtrlB when needed It helped them cut observability costs by more than 70% while improving their ability to look back in time and correlate across services. ## **Why This Matters** There’s a quiet trap a lot of teams fall into: you either keep all your logs (and pay dearly for it), or you throw them away (and lose visibility). CtrlB gives you another path - Keep everything. Search when it matters. Pay only when you query. Especially for: Debugging issues weeks after they occur Scaling without re-architecting pipelines Teams that are tired of paying to index everything upfront **S3 Isn’t the Problem. It’s the Opportunity.** S3 is a great place to store observability data. The problem is getting answers out of it. CtrlB turns S3 into an active part of your debugging workflow. You don’t need new formats or new pipelines. Just your logs, your questions, and a search bar. If this sounds interesting, we’d love to hear from you- drop us a line at support@ctrlb.ai --- --- title: "Logs as Data: Why CtrlB Treats Raw Logs Like a Data Lake" description: "In today’s cloud-native world, logs have grown beyond simple debugging tools; they’re now a valuable source of deep operational insight. At CtrlB, we see things differently. Logs are not just debugging artefacts; they are raw, untapped data. And we treat them with the same design principles you’d…" canonical: "https://ctrlb.ai/blogs/logs-as-data-why-ctrlb-treats-raw-logs-like-a-data" publishedTime: "2025-05-07" modifiedTime: "2025-06-12T03:21:03+0000" author: "Adarsh Srivastava" tags: [] --- # Logs as Data: Why CtrlB Treats Raw Logs Like a Data Lake In today’s cloud-native world, logs have grown beyond simple debugging tools; they’re now a valuable source of deep operational insight. At CtrlB, we see things differently. Logs are not just debugging artefacts; they are raw, untapped data. And we treat them with the same design principles you’d apply to a data lake. This blog explores our philosophy: **why CtrlB treats raw logs like a data lake**, how it impacts observability, and what this means for developers building and operating distributed systems. ### **The Problem with Traditional Log Stacks** In most traditional observability setups, logs are piped into a rigid pipeline: parse, index, and visualise, usually in that order. Popular tools enforce early schema binding, meaning engineers must decide upfront how their logs should look, what they should mean, and which ones are worth retaining. This approach has serious drawbacks, like: Over-indexing costs: Query performance comes at the price of expensive storage and compute. Inflexible Logging: Logs are the richest source of truth, & yet their value is often lost to rigid formats, filters, and early assumptions that discard what might matter later. Lost context: Logs become decoupled from the services and traces to which they belong. Logs as afterthought: Treated as transient, logs are often summarised or discarded quickly, missing opportunities for deeper insight. ### **Logs as a First-Class Data Source** Instead of rushing to discard logs, CtrlB treats raw logs as a **long-term, queryable asset**. Think of it like a data lake: logs are stored raw, preserved in full fidelity, and made available for ad hoc search or correlation later. ** Schema-Less Search** We don’t enforce a schema on ingest**.** Logs can be stored as key-value pairs, structured JSON, and unstructured text. When you query, **you define the structure you care about**. This allows teams to evolve their log formats over time without breaking downstream tools. ** Service-Aware Context** Logs aren’t just blobs of text. They belong to services, requests, and traces. CtrlB tags logs with service context on ingest, allowing you to filter, correlate, and trace across your stack without manually pre-processing or stitching things back together. ** Pipeline Control (In Progress)** We’re building towards a world where teams can control how logs flow, get enriched, or archived without waiting on DevOps or rewriting config files. The idea is to offer **seamless, centralised control** over log pipelines, directly from the CtrlB control plane. ### **Data Lake, Not Junk Drawer** If you store everything raw, doesn’t that become a junk drawer? Not if it's searchable, organised, and connected. Just like a data lake, CtrlB provides: Optimised full-text search over raw logs Schema-less access to raw data so you get what you want in sub-second speeds Correlation of logs with services and traces Retention flexibility, letting teams decide what’s hot vs cold data You get the benefits of full fidelity without being locked into a parsing schema from day one. ### **Who Benefits from This?** Developers can now search logs the way they think, by service, span, or trace, rather than guessing index formats. SREs, who need long-term visibility into production behaviour without juggling brittle log parsers. Security teams who want raw logs preserved for long-term retention without needing to rehydrate archived data, keeping historical logs searchable without ballooning costs. FinOps teams, who need to balance cost versus visibility and avoid overpaying for logs they rarely query, can access cold-tier logs instantly, which means no surprise rehydration delays. ### **Why CtrlB Built It This Way** CtrlB was designed from the ground up for the realities of **cloud-native, microservice-heavy systems**. Logs are not an afterthought or a sidecar; they are central to understanding how your systems behave. We believe storing them as raw, queryable data, not just rendered dashboards, is the only way to unlock their true potential. We’re not just building another logging tool. We’re building a **new foundation for observability**, one where data comes first, and insights follow: Log Native Observability. ### **Closing Thoughts** Logs are data. It’s time we treated them that way. By embracing the data lake approach, CtrlB enables teams to make logging more flexible, more affordable, and more useful. We’re rethinking what log storage and search should look like in a modern, distributed world, without forcing engineers to jump through hoops just to ask simple questions of their systems. Key Takeaways: Logs are a valuable data resource, not just debugging artefacts. Schema-less, service-aware log storage unlocks flexibility and long-term value. A data lake approach delivers better observability, lower costs, and richer insights. If you’re tired of managing brittle log pipelines or throwing away data you might need later, CtrlB might just be what you’ve been looking for.  --- --- title: "Tips to save egress cloud costs" description: "In today's cloud-driven world, managing costs is a critical aspect of running an efficient and profitable business. One of the often overlooked yet significant components of cloud expenditure is egress costs—charges incurred when data is transferred out of the cloud provider's network to another…" canonical: "https://ctrlb.ai/blogs/tips-to-save-egress-cloud-costs" publishedTime: "2024-06-03" modifiedTime: "2024-06-03T10:31:49+0000" author: "Adarsh Srivastava" tags: ["mayank","Observability","Storage","Cost"] --- # Tips to save egress cloud costs In today's cloud-driven world, managing costs is a critical aspect of running an efficient and profitable business. One of the often overlooked yet significant components of cloud expenditure is egress costs—charges incurred when data is transferred out of the cloud provider's network to another location. Whether you're running a startup, managing a large enterprise, or handling personal projects, understanding and optimizing egress costs can lead to substantial savings. In this blog let us understand what egress costs are, and how to best manage them. # A few basic concepts first ### Ingress In the context of cloud computing, ingress usually describes the data coming into the cloud from external sources. For instance, when you upload files to cloud storage or send data to a cloud-based application, that data transfer is considered ingress ### Egress In the context of cloud computing, egress typically describes the data exiting the cloud to an external destination, such as downloading files from cloud storage to a local device or sending data from a cloud-based application to users over the internet. ### Virtual Private Network (VPC) It is a virtual network dedicated to your cloud account. It is logically isolated from other virtual networks in the cloud. It provides you with a private, secure space to launch your cloud resources. Your cloud account can have multiple VPCs and all are logically separate entities (i.e., entities within one VPC cannot interact with a different VPC unless you’ve done something special called VPC peering). ### VPC peering VPC peering is a networking connection between two VPCs that enables you to route traffic between them privately. Instances in either VPC can communicate with each other as if they are within the same network. # Where is the cost involved? Ingress is mostly free. Egress comes at a cost - which depends on source and destination of network traffic among other things. We’ll discuss this in detail below. This is done by design because cloud providers want you to get inside their system and don’t want you to exit it (talk about a vendor lock in). A company once decided to switch their storage bucket from GCP to Azure because they scored some sweet deal with Microsoft. But once they started to move data, they realized the egress cost they’d pay to Google while doing it was twice the amount they’d have saved after moving to Azure. So they couldn’t move it. # How is egress cost calculated? Within a cloud, usually there are regions. For example, in GCP there are regions like: us-west, us-east, asia-south, asia-east, etc. Each region has zones for availability: asia-south-a, asia-south-b are 2 zones in 1 region. Data transferred within a zone using internal IP address: $0 Data transferred within a zone using external IP address: ~$0.01 / GB Data transferred across a zone within a region: ~$0.01 / GB Data transferred across regions is almost: ~$0.1 / GB (This changes based on source and destination regions. Refer to your cloud provider's documentation for exact figures.) The important thing here that people usually miss is that egress within a zone is only free if you are using internal IP addresses. # How to reduce egress cost? ### Case 1: First machine is on AWS, second on Azure. You’d have to pay full egress across cloud providers, doesn’t matter if same or different region. ### Case 2: Within same cloud, both machines in different regions. You’d have to pay full egress. ### Case 3: Within same cloud, same region, different zones. You’d have to pay partial egress (~$0.01 / GB) as mentioned above. ### Case 4: Within same cloud, same region, same zone, same VPC. Use the internal IP address of entities and you’d pay 0 egress. If you don’t want to use IP addresses you can configure a DNS mapping that maps to internal IP address of the machine. ### Case 5: Within same cloud, same region, same zone, different VPC. Without VPC peering, you’d pay full egress cost ($0.1) as data is going to the wide internet. With VPC peering and using the internal IP address (or the hostname DNS mapped to internal IP address), you’d pay 0 egress fees. --- --- title: "How to route Kubernetes logs using FluentD?" description: "Logs are critical for understanding what is occurring inside your Kubernetes cluster. Even while most apps come with a native logging mechanism, users in distributed and containerized environments (such as Kubernetes) will benefit from a centralized logging solution. That is because they must…" canonical: "https://ctrlb.ai/blogs/how-to-route-kubernetes-logs-using-fluentd" publishedTime: "2024-05-24" modifiedTime: "2024-05-24T12:47:05+0000" author: "Adarsh Srivastava" tags: ["Observability","Storage","kubernetes","mayank","Data"] --- # How to route Kubernetes logs using FluentD? Logs are critical for understanding what is occurring inside your Kubernetes cluster. Even while most apps come with a native logging mechanism, users in distributed and containerized environments (such as Kubernetes) will benefit from a centralized logging solution. That is because they must collect logs from many applications in various log formats and transfer them to a logging backend for storage, processing, and analysis. Kubernetes includes all of the essential resources required to achieve such functionality. In this article, we'll look at Kubernetes' logging architecture and show how to use Fluentd to collect application and system logs. We also go over some Fluentd configuration specifics to teach you how to set up log sources, match rules, and output destinations for your custom logging system. Let's get started. # Some basics first Docker containers in Kubernetes write logs to standard output (stdout) and standard (stderr) error streams. Docker redirects these streams to [a logging driver](https://docs.docker.com/engine/admin/logging/overview) configured in Kubernetes to write to a file in JSON format. Kubernetes then exposes log files to users via kubectl logs command. However, deleting a pod from the node permanently deletes all connected containers and logs. The same thing happens when a node dies. In this circumstance, users can no longer access application logs. To avoid this scenario, container logs should have their own shipper, storage, and lifespan, independent of pods and nodes. Kubernetes does not have a native storage solution for log data, but you can easily integrate your favorite logging shipper into the Kubernetes cluster using the Kubernetes API and controllers. In this article, we will be shipping logs be deploying a node-level logging agent that runs on every node. # What the hell is a node-level logging agent? This is an agent that runs on every node in your kubernetes cluster. Production clusters normally have more than one nodes spun up. If this is your case, you’ll need to deploy a logging agent on each node. The simplest method to accomplish this in Kubernetes is to build a unique sort of deployment called DaemonSet. The DaemonSet controller ensures that each node in your cluster has a copy of the logging agent pod. The DaemonSet controller will additionally check the cluster's node count on a regular basis and start/stop a logging agent if it changes. The DaemonSet structure is ideal for logging solutions because it requires just one logging agent per node and eliminates the need to alter the applications executing on the node. # Deploying FluentD as a DaemonSet We used the DaemonSet and the Docker image from the **[fluentd-kubernetes-daemonse**t](https://github.com/fluent/fluentd-kubernetes-daemonset) GitHub repository. There you can also find Docker images and templates for other log outputs supported by Fluentd such as Loggly, Kafka, Kinesis, and more. Using the repository is the simplest way to get you started if you don’t know much about Fluentd configuration. This article assumes you have the following: A running Kubernetes cluster, kubectl command line tool. ## Step 1: Grant permissions to FluentD Fluentd will receive logs from both user apps and cluster components such as kube-apiserver and kube-scheduler, thus we must grant it certain permissions. Here we will create a ServiceAccount and grant it permissions to read, list, and watch pods and namespaces in your cluster. Create a file called rbac.yml like this ``` apiVersion: v1 kind: ServiceAccount metadata: name: fluentd namespace: kube-system --- apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRole metadata: name: fluentd rules: - apiGroups: - "" resources: - pods - namespaces verbs: - get - list - watch --- kind: ClusterRoleBinding apiVersion: rbac.authorization.k8s.io/v1 metadata: name: fluentd roleRef: kind: ClusterRole name: fluentd apiGroup: rbac.authorization.k8s.io subjects: - kind: ServiceAccount name: fluentd namespace: kube-system ``` ## Step 2: Create a FluentD configuration Your FluentD process needs a configuration to understand how to route Kubernetes logs. In this example you will see the configuration to send data to CtrlB platform, however you can replace this with the destination of your choice. Save this file as fluent.conf ``` @type tail @id in_tail_container_logs path /var/log/containers/*.log pos_file /var/log/fluentd-containers.log.pos tag kubernetes.* read_from_head true @type none @type http endpoint "" open_timeout 3 json_array true headers {"X-CtrlB-License":""} @type memory flush_mode immediate ``` ## Step 3: Create a configuration to deploy pods This is the configuration to deploy FluentD as a daemonset so that Kubernetes can configure a FluentD process with each of your nodes. Save this file as fluentd.yml ``` apiVersion: apps/v1 kind: DaemonSet metadata: name: fluentd namespace: kube-system labels: k8s-app: fluentd-logging version: v1 kubernetes.io/cluster-service: "true" spec: selector: matchLabels: name: fluentd template: metadata: labels: name: fluentd spec: serviceAccount: fluentd serviceAccountName: fluentd tolerations: - key: node-role.kubernetes.io/control-plane effect: NoSchedule - key: node-role.kubernetes.io/master effect: NoSchedule containers: - name: fluentd image: fluent/fluentd-kubernetes-daemonset:v1-debian-forward env: - name: FLUENT_UID value: "0" resources: limits: memory: 200Mi requests: cpu: 100m memory: 200Mi volumeMounts: - name: config-volume mountPath: /fluentd/etc/fluent.conf subPath: fluent.conf - name: varlog mountPath: /var/log - name: varlibdockercontainers mountPath: /var/lib/docker/containers readOnly: true terminationGracePeriodSeconds: 30 volumes: - name: config-volume configMap: name: fluentd-conf - name: varlog hostPath: path: /var/log - name: varlibdockercontainers hostPath: path: /var/lib/docker/containers ``` ## Step 4: Create a config map for your config file: ``` kubectl create configmap fluentd-conf --from-file=fluent.conf --namespace=kube-system ``` ## Step 5: Deploy the Kubernetes configs ``` kubectl create -f rbac.yml && kubectl create -f fluentd.yml ``` # Conclusion In this article, we showed how Fluentd can easily centralize logs from many apps and deliver them to Elasticsearch or another output destination. Unlike sidecar containers, which must be generated for each application in your cluster, Fluentd's node-level logging simply requires one logging agent per node. --- --- title: "Power of Observability Pipelines: Understanding Their Role and Impact on Organisational Efficiency" description: "Explore the transformative power of observability pipelines in enhancing organizational efficiency. Learn how real-time insights, scalability, and cost optimization drive agile decision-making and operational excellence across diverse industries. Discover the benefits and challenges of implementing observability pipelines, and unlock new opportunities for innovation and resilience in the digital landscape." canonical: "https://ctrlb.ai/blogs/power-of-observability-pipelines" publishedTime: "2024-05-14" modifiedTime: "2025-06-25T16:59:15+0000" author: "Adarsh Srivastava" tags: ["Data","adarsh"] --- # Power of Observability Pipelines: Understanding Their Role and Impact on Organisational Efficiency ## Introduction The observability pipeline is a pivotal instrument in the modern digital landscape, orchestrating the seamless flow of telemetry data by aggregating, processing, and routing it effectively. Its multifaceted role extends across crucial domains like observability, security, and compliance, making it a cornerstone of operational success. In this article, we'll delve deeper into the intricacies of the observability pipeline, exploring its fundamental nature, and its profound impact on various aspects of digital infrastructure, and speculating on the exciting avenues of development that lie ahead. ## Understanding Observability As modern days systems are becoming more and more complex, the way that we monitor the performance of these systems is also becoming complex. To solve this problem MALT stack was introduced giving rise to the concept of observability. This gave a deeper understanding of the system helping developers in faster resolution of issues. ## The Role of Observability Pipelines With the widespread adoption of observability practices, applications began generating copious amounts of data, occasionally overwhelming traditional monitoring systems. Enter the observability pipeline—a solution designed to tackle this very challenge. By intelligently aggregating vast volumes of data while preserving its intrinsic meaning, these pipelines revolutionised how organisations manage and derive insights from their telemetry data. One of the most significant advantages offered by observability pipelines is their ability to operate in real time. This means that insights and analysis are delivered instantaneously, empowering businesses to make informed decisions promptly. The impact of this real-time capability is profound across various sectors, including software/technology, telecommunications, and healthcare. In essence, observability pipelines represent a paradigm shift in how organizations harness the power of their data. By seamlessly managing large volumes of information while maintaining contextual relevance, they unlock new possibilities for innovation and operational excellence across industries. ## Impact on Organisational Efficiency Now that we have a solid grasp of what an observability pipeline entails, let's explore how it can directly benefit you. Here are some of the major advantages it offers: Real-Time Insights: By facilitating real-time data analysis, observability pipelines empower you to gain immediate insights into the performance and behavior of your systems. This capability enables agile and responsive decision-making, allowing you to address issues swiftly and capitalise on emerging opportunities without delay. Scalability: As your organisation grows and the volume of data generated by your applications increases, observability pipelines seamlessly scale alongside your needs. Whether you're experiencing a surge in user traffic or expanding your operations, these pipelines can handle the growing data influx, ensuring that you maintain uninterrupted visibility into your systems' health and performance. Cost Optimisation: Observability pipelines play a crucial role in cost optimisation by streamlining data storage requirements, optimising data processing workflows, and enhancing resource utilisation. By efficiently managing data storage and processing resources, these pipelines help minimise infrastructure costs while maximising the value extracted from your telemetry data. ## Implementing Observability Pipelines Traversing the expansive ocean of telemetry data presents a formidable task. It's here that observability pipelines shine, seamlessly converting complexity into lucidity. These invaluable tools empower organisations across diverse sectors and scales to effectively channel and harness their data, turning it into a strategic resource. While establishing a telemetry pipeline may seem daunting initially, the rewards of enhanced data efficiency and decision-making capabilities far outweigh the initial effort. ## Conclusion In conclusion, observability pipelines are crucial assets for navigating complex digital landscapes. They offer real-time insights, scalability, and cost optimization, empowering organizations to make agile decisions and enhance operational efficiency. While implementing these pipelines may pose initial challenges, the benefits far outweigh the effort, paving the way for innovation and resilience across various sectors. --- --- title: "Unraveling the Mysteries of Digital Footprints: Understanding Traces in the Digital World" description: "Explore the significance of digital traces, uncovering insights into user behavior, capacity planning, and security. Despite challenges, discover promising trends shaping the future of traces for enhanced observability and real-time analysis." canonical: "https://ctrlb.ai/blogs/understanding-traces-in-the-digital-world" publishedTime: "2024-05-10" modifiedTime: "2024-05-10T11:59:19+0000" author: "Pradyuman" tags: ["pradyuman","Observability"] --- # Unraveling the Mysteries of Digital Footprints: Understanding Traces in the Digital World ## Introduction In the vast expanse of the digital realm, traces echo the essence of reality, mirroring its significance. They encapsulate the intricate dance of capturing and documenting the essence of application execution. These traces serve as invaluable guides, illuminating the path toward understanding user behaviour and swiftly pinpointing flaws within our creations. Join us on this journey as we delve deeper into the enigmatic world of digital traces, arming you with the knowledge to wield them effectively in your development endeavours. ## What Are Traces? Traces paint a comprehensive picture of the intricate journey initiated by a single request to an application. Whether your application stands as a solitary monolith or as a complex network of interwoven services, traces serve as indispensable guides, unveiling the complete trajectory traversed by each request. Harnessing the power of traces within applications has yielded profound insights into system dynamics, particularly within the realm of distributed systems. ## Why do Traces Matter? Traces extend far beyond mere debugging; they encompass a diverse array of horizons, including: Understanding User Behaviour: Traces, offering a granular depiction of each user request's journey, serve as a powerful tool for unraveling user behaviour within the application. Armed with this intricate insight, stakeholders can craft data-driven strategies, enriching decision-making processes with a profound understanding of user interactions and preferences. Capacity Planning and Scalability: Traces are invaluable in revealing resource utilization patterns and performance trends over time, empowering teams to conduct effective capacity planning and timely infrastructure provisioning. By leveraging this data, organisations can optimize resource allocation, ensure scalability, and maintain optimal performance levels to meet evolving demands efficiently. Security and Compliance: Traces play a crucial role in security auditing and compliance efforts by providing a detailed audit trail of data access and manipulation, helping ensure adherence to regulatory requirements and best practices. ## The Journey of Traces At its core, a trace comprises a collection of spans, each delineating a distinct unit of work within an operation's execution. These spans encapsulate the tasks performed during the runtime of application code. Once generated, they are transmitted to storage, either directly or via a collector. To extract actionable insights, a plethora of tools are available for querying and visualising this data, facilitating a deeper understanding of system behaviour and performance dynamics. ## Risks Associated with Traces Privacy Concerns: Traces, especially in the context of user interactions with applications, can contain sensitive information. Without proper safeguards, these traces may inadvertently expose personal data, raising significant privacy concerns. Whether it's user input, authentication tokens, or other identifiable information, mishandling traces can lead to breaches of privacy regulations and erosion of user trust. Data Breaches and Identity Theft: Traces often traverse various components and services within a system, potentially exposing vulnerabilities that malicious actors could exploit. Such breaches not only jeopardise the integrity of the system but also pose a direct threat to user privacy and may facilitate identity theft or fraud. Surveillance and Tracking: In certain contexts, traces may be leveraged for surveillance or tracking purposes, whether by malicious entities or even by well-intentioned organisations seeking to monitor user behaviour for targeted advertising or other purposes. ## Looking Ahead The future of traces looks promising to me but here are some trends that especially fascinate me: Enhanced Observability: Traces are poised to become an integral component of comprehensive observability platforms, providing deeper insights into system behavior, performance, and user interactions across distributed environments. This evolution will enable organisations to gain a holistic understanding of their applications and infrastructure, facilitating more proactive and data-driven decision-making. Standardization and Interoperability: Efforts to standardize trace formats, protocols, and APIs will accelerate, fostering greater interoperability and seamless integration across diverse toolsets and platforms. This standardisation will enable organisations to leverage traces more effectively, regardless of the underlying technologies or vendors involved, thereby enhancing collaboration and reducing vendor lock-in. Real-Time and Contextual Analysis: Future traces will increasingly support real-time monitoring and analysis capabilities, allowing organisations to detect and respond to performance issues, security threats, and user behaviour in near real time. ## Conclusion In conclusion, traces are pivotal in modern systems, offering insights into user behaviour, aiding in capacity planning, and ensuring security. Despite challenges like privacy concerns and data breaches, the future of traces looks promising. Trends like enhanced observability and real-time analysis will drive innovation, enabling organizations to navigate the complexities of the digital landscape with agility and confidence. --- --- title: "Cracking the Code: Understanding High Cardinality in Metrics" description: "Unlock the potential of your metrics with our guide to understanding high cardinality. Discover how granular insights can drive innovation, along with the challenges they bring. Dive into strategies for managing high cardinality metrics and explore the powerful Grafana + InfluxDB stack for efficient storage and visualisation." canonical: "https://ctrlb.ai/blogs/understanding-high-cardinality-in-metrics" publishedTime: "2024-05-07" modifiedTime: "2024-05-07T09:29:42+0000" author: "Pradyuman" tags: ["Observability","Storage","Data","pradyuman"] --- # Cracking the Code: Understanding High Cardinality in Metrics ## Introduction In our increasingly data-driven landscape, metrics serve as invaluable guides, offering intricate insights into the performance of systems and processes. They empower us to make well-informed decisions, driving efficiency and innovation across various domains. However, the true power of metrics emerges when they exhibit high cardinality – offering granular, nuanced data points that paint a comprehensive picture of operations. Yet, with this richness comes a unique set of challenges. High cardinality metrics, while immensely valuable, can strain storage capacities, complicate querying processes, and present numerous other hurdles. In this article, we'll delve into the complexities of high cardinality metrics, exploring both their benefits and the strategies required to navigate the obstacles they present. ## The Concept of High Cardinality Now that we know about the importance of having high cardinality metrics question becomes what does this mean? The **_cardinalit**y_ of a data attribute refers to the number of distinct values that it can have. For example, a boolean has a cardinality of 2 (_True, False_).  So High Cardinality Metrics mean we have metrics that can hold more values. For instance, when tracking CPU utilization, a single metric indicating overall usage provides lower cardinality compared to metrics that capture utilization for each core individually. In the latter case, where data is broken down by core, we encounter higher cardinality due to the increased granularity of information ## Challenges Posed by High Cardinality While High Cardinality Metrics offer significant advantages over their Low Cardinality counterparts, they also introduce a unique set of challenges that must be addressed. Metrics Collection: Since detailed metrics necessitate more computational resources during collection, they may potentially become a bottleneck for application performance Metrics Storage: The adage more data, more storage holds, with the storage demands escalating exponentially, particularly contingent on the cardinality of your metrics. Querying and Analysis: High cardinality metrics frequently necessitate aggregation and processing to transform raw data into actionable insights for users. ## Strategies for Managing High Cardinality > “With great power comes great responsibility” When faced with an inundation of detailed system insights, it's crucial to streamline the monitoring process by reaching a consensus on the essential information required. Without this clarity, the very data intended to aid can instead hinder progress. Here are several techniques to adeptly manage High Cardinality Metrics: ### **Time-windowed aggregation** Time-windowed aggregation entails segmenting data into fixed time intervals, allowing for the reduction of data volume while preserving its contextual significance. The selection of appropriate time frames depends on the nature of the metrics collected and the analytical requirements. ### Clustering Clustering involves grouping similar data points based on their features. For instance, clustering could be used to group machines with similar applications deployed on them. ## Stack for High Cardinality Handling Storing and visualising High Cardinality metrics can be a pain point but this has been handled well by this stack: ### Grafana + Influx DB **InfluxDB** InfluxDB is an open-source time-series database designed for storing, querying, and visualizing time-stamped data. It excels in handling high volumes of time-series data with high ingest rates and fast query performance. InfluxQL is the query language used for interacting with InfluxDB. These features make it the ideal choice for high cardinality metrics. **Grafana** Grafana is an open-source dashboarding and visualization tool that allows users to create interactive dashboards and panels to monitor and analyze data. Grafana provides a user-friendly query editor interface that allows users to write queries and retrieve data from data sources. Users can use InfluxQL queries to fetch time-series data from InfluxDB and visualize it in Grafana. --- --- title: "The High Costs of Observability: Uncovering the Burden on Modern Systems" description: "Navigating the complexities of modern computing requires a delicate balance between insightful monitoring and cost-effective practices." canonical: "https://ctrlb.ai/blogs/the-high-costs-of-observability" publishedTime: "2024-04-30T06:35:58+0000" modifiedTime: "2024-05-07T08:55:32+0000" author: "Pradyuman" tags: ["Observability","pradyuman"] --- # The High Costs of Observability: Uncovering the Burden on Modern Systems ## **Unveiling the Expense of Observability** Having immersed myself in this domain for more than three years, I’ve continually pondered the enigma of observability’s steep costs. Is it truly necessary for observability tools to incur such significant expenses in both operation and upkeep? In my quest for answers, I dissected every observability solution into three fundamental components: Data Collection Data Storage Data Analysis ## **Exploring Data Collection** Data collection stands as the cornerstone of any observability system. In this realm, agents bear the weighty responsibility of tasks ranging from basic up/down monitoring to gathering performance metrics, shipping logs, ensuring file integrity, and fortifying applications with firewalls, among others. These agents are finely tuned to utilize only a small fraction of computing resources, typically around 0.3% on most platforms. Further optimization would necessitate extensive research and may not yield substantial cost savings. ## **Understanding Data Storage** Data storage constitutes a significant portion of the cost of an observability tool, presenting two primary challenges: Determining what data to store Selecting the optimal storage method ### **Deciding What to Store** Optimal case scenario for this problem would have been a filter that dynamically changes what to store based on how system is performing. For example if system is facing some performance issue it should increase granularity of the data being stored, once system comes back to normal state it should decrease the granularity. But in most of systems this is not the case and we get a fixed data at all times, so developers have to decide upon what to keep and what not to in order to have balance between storage cost and being able to use this data effectively while solving issue.  ### **Choosing How to Store** Once we have decided on what to store next question that has to be answered becomes how to store it, do we store this in raw format or compressed form, do we store matrices directly or store aggregate values and many more such question. Depending on answers to these questions our storage cost varies significantly.  At CtrlB, we’ve tackled this challenge with finesse, enabling us to offer our solution at a significantly lower cost compared to our competitors. Intrigued? Reach out to us at support@ctrlb.ai, and let us revolutionize your observability costs beyond your wildest expectations! ## **Extracting Insights from the Observability Data** If we are not able to analyse this data it is useless. Analysing this data is pocket heavy including 2 major expense, reading from storage and then performing operations on this data. Reading cost depends on factor discussed in previous section so let's discuss about cost of processing this data.  Although we don’t have numbers about closed source software, I analysed system requirements of opensource projects such as jaeger, zipkins etc it revolves around 4–8 core CPUs & 8–16GB memory. On putting these parameters into AWS pricing calculator, bill comes around $18K — $20K/year for a single server. Bringing in distributed system and fault tolerance would increase this cost quite significantly.  ## **A Recap of Observability Costs** If you’ve made it this far, thank you for your attention. I trust that this exploration has shed light on your inquiries regarding the costs associated with observability. Make sure to check out our product in detail [here](https://ctrlb.ai/#product) --- --- title: "When should you prefer CtrlB over self-hosting Elastic or Loki?" description: "In the landscape of log management solutions, the choices can seem endless, each with its own set of advantages and drawbacks. Among these options, self-hosted platforms like Elastic and Loki have gained significant traction, offering users control and customization over their logging…" canonical: "https://ctrlb.ai/blogs/when-should-you-prefer-ctrlb-over-self-hosting-ela" publishedTime: "2024-04-01" modifiedTime: "2024-05-07T08:52:43+0000" author: "Adarsh Srivastava" tags: ["mayank","Data","Observability"] --- # When should you prefer CtrlB over self-hosting Elastic or Loki? In the landscape of log management solutions, the choices can seem endless, each with its own set of advantages and drawbacks. Among these options, self-hosted platforms like Elastic and Loki have gained significant traction, offering users control and customization over their logging infrastructure. However, in a world where cost is a crucial factor for businesses of all sizes, CtrlB emerges as a compelling alternative. As the most budget-friendly log management solution on the market, CtrlB promises to deliver powerful features without breaking the bank. In this blog, we'll delve into a comparative analysis, exploring the benefits and trade-offs of choosing CtrlB over self-hosted alternatives like Elastic and Loki, helping you make an informed decision for your logging needs. # TL;DR - key findings CtrlB is cheaper than most SaaS observability vendors in market. The total cost of ownership of ELK stack becomes much larger than using CtrlB because elastic stores data on SSDs whereas we store data on much cheaper S3. The total cost of ownership of Prometheus Loki Grafana stack is less than Elastic but a little higher than using CtrlB if you do not have a very lean DevOps team. Moreover we found out queries in Loki take much longer than us (thanks to our intelligent MPP based query engine). The ingestion performance of CtrlB is at par with Elastic and Loki. (Although Elastic is a bit slow because it indexes everything it ingests.) Storage used by CtrlB for the same amount of logs is about 1/4th of what ELK uses. Loki starts to fail on high volume / high cardinality data. Query performance: For an average daily use query, ELK takes milli-seconds, CtrlB has a sub-second latency, whereas Loki takes seconds to answer (assuming it doesn't fail). # Benchmarking Setup We will be comparing CtrlB against self-hosted  **ELK **(Elasticsearch, Loki and Kibana) stack and **PLG **(Promtail, Loki, and Grafana) stack. ## Load generator We generated fake logs using [flog](https://github.com/mingrammer/flog). It is a fake log generator for common log formats such as apache-common, apache-error, and RFC3164 syslog. Machine size: 3 machines each with: 8vCPUs, 16GB RAM Command: ``` docker run -it --rm mingrammer/flog -d 200µs -n 432000000 ``` This command generates around 5k logs per second for 24 hours. 3 such services are running, and after a day we have 1,296,000k log entries which take up 327GB in raw storage. ## Deployment details For a fair comparison, we deployed all the three platforms on VMs of same size - 32vCPUs, 64GB RAM which costs around $600 on AWS. # Benchmarking Results To ensure the effectiveness of any log management tool, these three key factors hold utmost significance ## Storage As mentioned above, we produced at total of 327 GB of logs in a day which comprised of 1,296,000k log entries. This is the amount of storage each platform takes: CtrlB: 66.48 GB Elastic: 284.04 GB Loki: 79.8 GB The large size of data in elastic is due to the massive indices elastic maintains to speed up log queries. CtrlB uses state of the art compression and we take up almost 1/4th of what elastic uses. It is worthwhile to mention that we tried to query the total document count in all platforms. While CtrlB and ELK gave the correct answer -> 1,296,000k, the document count query failed in loki. So CtrlB beats both Elastic and Loki in terms of storage efficiency. ## Ingestion Distributed cloud-native applications have the capacity to produce logs at an immense scale, so log management tools need to efficiently handle the ingestion of such large volumes of log data. We noticed the ingestion rate as: CtrlB: ~15.8k log entries per second Elastic: ~14.4k log entries per second Loki: ~15.1k log entries per second So CtrlB is at par with Elastic and Loki in terms of ingestion. The slightly lower rate in Elastic can be attributed to the large amount of indexing and process elastic has to do on log data to support milli second query latency. ## Query The performance of queries across various logging solutions holds significant importance, aiding in the selection of a solution based on whether specific querying capabilities are necessary or not. ### Query 1: Get all logs for last 5 minutes CtrlB: 0.55s Elastic: 0.163s Loki: 0.579s ### Query 2: Get all logs for last 1 hour CtrlB: 0.69s Elastic: 0.175s Loki: 0.670s ### Query 3: Get all logs for last 1 hour where body contains some string CtrlB: 0.30s Elastic: 0.174s Loki: 3.29s ### Query 4: Get all logs for last 1 hour where a log field equals some particular value CtrlB: 0.62s Elastic: 0.227s Loki: fails We will also highlight the fact that Loki fails to get the count of logs for the above queries, again highlighting the fact that it has trouble dealing with a lot of data, whereas CtrlB and Elastic successfully returned the correct document count. This data tells us that Elastic stack takes milli seconds to answer what CtrlB does in less than a second and Loki does in a few seconds (given it doesn't fail). This could be attributed to the massive amount of indexing in elastic and the fact that it stores data on costly SSD whereas CtrlB and Loki store data on cheap s3 storage. # Total Cost of Ownership comparison The above experiment produced 327GB of logs in 24 hours or 10TB data in a month. Let's also assume the following: The data needs to be retained for 30 days. 3x replication is needed for ELK stack to reliability. 2 DevOps engineers are needed to handle the self-hosted infra and 1 DevOps engineer earns around $1000 per month in India. This table details the total cost a company would pay to manage this data on different logging platforms:  *TCO comparison* In this table we note: CtrlB is cheaper than most SaaS players in observability market. The storage in self-hosted Elastic is the costliest element in its TCO. # Conclusion: When should you prefer CtrlB over self-hosted solutions? In this blog we have seen that CtrlB is as performant as Elastic or Loki stacks in terms of ingestion, querying and storage. We realized that self-hosting elastic can become expensive quickly because of the costly SSD storage. The storage in Loki is cheap but it offers poor query performance and starts having issues when faced with high volume and high cardinality. So in summary, if you're someone who is: Pissed with your observability bills and looking for a cheap log management solution with sub-second latency. Do not want the overheads of having to maintain a self-hosted solution yourself. Feel free to reach out to us at support@ctrlb.ai, and we promise to slash your observability bills. --- --- title: "Why archiving old data is a bad idea?" description: "Discover how CtrlB revolutionizes log data management, offering seamless querying of both hot and cold storage without compromising cost or accessibility. Say goodbye to expensive hot storage and complex tiered storage decisions. With CtrlB, make historical comparisons effortlessly, gain deeper insights, and eliminate conflicts within your organization. Unlock the full potential of your data with CtrlB's cost-effective and streamlined solution." canonical: "https://ctrlb.ai/blogs/why-archiving-cold-data-doesnt-work" publishedTime: "2024-03-12" modifiedTime: "2024-05-07T08:52:06+0000" author: "Adarsh Srivastava" tags: ["Data","Storage","adarsh"] --- # Why archiving old data is a bad idea? The idea of runaway expenses may give you the chills if you handle massive amounts of log data. The expense of storing your hot data is too high, and accessing your cold storage is too challenging. So now you have to make tradeoffs like reducing visibility into your older logs to save some bucks. But what if you didn’t have to make that tradeoff? What if you were able to query all your data at all times even from cold s3 storage? This is exactly what CtrlB is here to help you with. # Why hot storage costs so much? You can get excellent scalability, low latency ingest, and efficient query performance with hot storage. But there has always been a price associated with these advantages. Most platforms use pricey storage solutions like storage arrays and solid-state drives (SSDs) to achieve high performance. Although these storage options give you quick read and write times for your data, the additional expense makes hot storage very costly. When you take into account the massive volume of data that many businesses consume on a daily basis, these costs increase even more. The standard hot data storage default for many log management systems and observability platforms may be three to thirty days. Some businesses consume so much logs that, in order to save money, data must be moved to cold storage after only a few days. Additionally, some solutions require you to export your log data yourself because they won't keep it after the hot storage time period, creating additional headaches and complexity. # The problems with cold storage Comparing, comprehending, and analyzing your data becomes considerably more difficult when it is placed in cold storage. "Those who cannot remember the past are condemned to repeat it," goes the adage. With your data, one might say the same. What occurs when a significant prod incident in your team resembles one that occurred six months or a year ago, but you are unable to access the relevant data to compare and determine what went wrong? What happens, for example, if you are attempting to decipher patterns in your data that point to recurring slowdowns but are unable to go far enough back in time to fully comprehend those patterns? Large-scale, cyclical events are another common use case where cold storage presents challenges. Imagine you're having an yearly black Friday sale. When all your storage is hot, you can make comparisons across large events in near real time that can support your business. When the data from the last major event is in cold storage, though, you can’t make those connections fast enough to act on them. You lose valuable contextual information—such as what a customer bought or looked at last year—to connect to your most recent data. And the customer that almost made a purchase last year might be a near-miss this year, too. Even worse, you’ll never connect those dots and understand what happened. In the end, having more data collected over a longer period of time improves its precision and accuracy, facilitates the identification of patterns, offers insightful context, and yields richer insights. You run the danger of losing the information necessary for your business to succeed if you put data into cold storage, where it is difficult to query and access. # Introducing CtrlB Since we store data on cheap s3 storage, and allow you to query on it, you don't have to worry about losing old data anymore. This gives you the following benefits: Make historical comparisons for deeper data insights. Compare today’s data against last week, last month, or last year—with no penalty for querying cold storage and no lost data. Eliminate the complexity of storage tiers. Either you or a platform handling the migration for you will have to oversee the data transfer between levels. In order to guarantee data integrity in the event that there are migration problems, you must back up your data before the migration. Additionally, you will want additional storage capacity to accommodate duplicate data during migrations. Moving data between tiers is not a concern when all of your storage is hot. Eliminate difficult decisions about data management. Working with tiered storage forces you to make difficult choices. When should the data be moved from hot to warm to cold? It's not just about the money; you also need to know which data is critical for operations. The more applications you have, the more difficult these choices become. Do you strive to cut costs by reducing the hot storage retention duration as your data volume and expenditures increase? What happens if your CFO says costs should be cut but your engineering teams insist on the data? These choices have the potential to create conflict between teams and induce anxiety. You'll have one less issue to worry about and more time to spend elsewhere when you use us (and yet cost-effective like cold storage). If this sounds interesting, don't hesitate to reach to us at support@ctrlb.ai --- --- title: "Separating storage and compute - holy grail to cut down costs" description: "Discover how decoupling storage and compute in databases can slash costs and boost efficiency. Traditional setups, while efficient for small-scale operations, become cumbersome as data volumes soar. Learn why coupling storage and compute leads to wasteful resource allocation and skyrocketing expenses. Explore the benefits of a separated architecture, leveraging remote storage like Amazon S3 for cost-effective, scalable solutions. Uncover how this approach empowers dynamic scaling and intelligent caching for optimal performance." canonical: "https://ctrlb.ai/blogs/separating-storage-and-compute---holy-grail-to-cut" publishedTime: "2024-02-14" modifiedTime: "2024-05-07T08:51:38+0000" author: "Adarsh Srivastava" tags: ["mayank","Observability","Storage","Data"] --- # Separating storage and compute - holy grail to cut down costs Conventional databases are engineered to minimize data transport and query latency by distributing computational tasks across local nodes. This storage-compute connected design becomes problematic as data quantities and needs for real-time analysis increase. In a business environment where "cost" and "efficiency" are crucial, problems like inefficient compute resources, complicated data distribution and deployment, and expensive maintenance shoot up your observability bills, which is intolerable. It is imperative to have a decoupled architecture between storage and compute that can scale computation independently. This blog describes how a storage-compute separation helps reduce costs and increase efficiency. # **Why is storage-compute coupling a problem?** Imagine a company generating 1 terabyte of incremental log data per day, which is stored for a period of two years. But most of the time, queries are limited to log data created in the last seven days. In a storage-compute linked architecture (assuming each server has a 20TB storage capacity, 3 data replicas and a 50% compression ratio), to store 700TB of data, a total of (700 TB x 3 x 50%) / 20 TB ~ 52 servers will be required. But only log data created in the last seven days is analyzed by 80% of analytics workloads, which means (7 TB x 3 x 50%) / 20 TB ~ 0.52 servers will be enough to handle most data queries. The storage of cold data, which is rarely requested, wastes more than 95% of the server's expenses. # So what is the solution? The central idea behind storage-compute separation is that data is stored in low-cost, reliable remote storage systems such as Amazon S3. This reduces storage cost, ensures better resource isolation, and high elasticity and scalability. The compute nodes can be stateless - they need not store any data with them and act upon the data stored in s3. This enables you to scale the number of compute nodes up and down based on the query requirements. You can take this one step further by enabling intelligent caching - to cache frequently accessed data on the compute nodes itself.  If this sounds interesting to you, feel free to drop us a mail at support@ctrlb.ai and we promise to cut down your observability bills like you never imagined before! --- --- title: "hello world" description: "hello world from Ctrlb" canonical: "https://ctrlb.ai/blogs/hello-world" publishedTime: "2024-01-03" modifiedTime: "2024-05-07T09:02:42+0000" author: "Adarsh Srivastava" tags: ["adarsh"] --- # hello world Hi folks, Just wanted to type << hello world >> and announce that we are building something very interesting to help you cut down your observability bills. Stay tuned! --- --- title: "Why are legacy observability solutions so expensive?" description: "In the realm of modern system management, observability has emerged as a crucial concept, enabling organizations to gain deep insights into the performance and behavior of their applications and infrastructure. However, despite its undeniable benefits, it often comes with a hefty price tag. Today…" canonical: "https://ctrlb.ai/blogs/why-are-legacy-observability-solutions-so-expensiv" publishedTime: "2024-01-02" modifiedTime: "2026-03-27T12:28:09+0000" author: "Adarsh Srivastava" tags: ["mayank","Data","Observability","Storage"] --- # Why are legacy observability solutions so expensive? In the realm of modern system management, observability has emerged as a crucial concept, enabling organizations to gain deep insights into the performance and behavior of their applications and infrastructure. However, despite its undeniable benefits, it often comes with a hefty price tag. Today you find companies tripping over each other trying to come up with the next great incremental innovation in pricing models and overall cost reduction techniques for storing logs, metrics, and traces. So what happened over the years, that contributed to this exponential rise in observability cost? The breakdown of monolithic architectures and the adoption of microservices, along with the rise of complex cloud infrastructures, have significantly heightened the demand for very detailed observability. Microservices, by their nature, present substantial challenges in implementation and debugging. Without a lot of logs, metrics and traces, debugging failures within these systems and identifying their root causes is difficult. Over the last two decades, Infrastructure as a Service (IaaS) providers and open-source technologies have progressively simplified the generation of large volumes of telemetry. The biggest problem of all is over 90% of telemetry data never gets queried. Observability vendors have been associating cost to data volume rather than data value. We at CtrlB are changing things fundamentally how different data gets stored based on its value. Visit our website at https://ctrlb.ai to get a glimpse of what we are building. --- --- title: "Tag: Cost" canonical: "https://ctrlb.ai/blogs/tag/Cost" --- # Blog posts tagged “Cost” Canonical listing: https://ctrlb.ai/blogs/tag/Cost ## Articles - [Tips to save egress cloud costs](https://ctrlb.ai/blogs/tips-to-save-egress-cloud-costs.md) --- --- title: "Tag: Data" canonical: "https://ctrlb.ai/blogs/tag/Data" --- # Blog posts tagged “Data” Canonical listing: https://ctrlb.ai/blogs/tag/Data ## Articles - [How to route Kubernetes logs using FluentD?](https://ctrlb.ai/blogs/how-to-route-kubernetes-logs-using-fluentd.md) - [Power of Observability Pipelines: Understanding Their Role and Impact on Organisational Efficiency](https://ctrlb.ai/blogs/power-of-observability-pipelines.md) - [Cracking the Code: Understanding High Cardinality in Metrics](https://ctrlb.ai/blogs/understanding-high-cardinality-in-metrics.md) - [When should you prefer CtrlB over self-hosting Elastic or Loki?](https://ctrlb.ai/blogs/when-should-you-prefer-ctrlb-over-self-hosting-ela.md) - [Why archiving old data is a bad idea?](https://ctrlb.ai/blogs/why-archiving-cold-data-doesnt-work.md) - [Separating storage and compute - holy grail to cut down costs](https://ctrlb.ai/blogs/separating-storage-and-compute---holy-grail-to-cut.md) - [Why are legacy observability solutions so expensive?](https://ctrlb.ai/blogs/why-are-legacy-observability-solutions-so-expensiv.md) --- --- title: "Tag: kubernetes" canonical: "https://ctrlb.ai/blogs/tag/kubernetes" --- # Blog posts tagged “kubernetes” Canonical listing: https://ctrlb.ai/blogs/tag/kubernetes ## Articles - [How to route Kubernetes logs using FluentD?](https://ctrlb.ai/blogs/how-to-route-kubernetes-logs-using-fluentd.md) --- --- title: "Tag: Observability" canonical: "https://ctrlb.ai/blogs/tag/Observability" --- # Blog posts tagged “Observability” Canonical listing: https://ctrlb.ai/blogs/tag/Observability ## Articles - [Tips to save egress cloud costs](https://ctrlb.ai/blogs/tips-to-save-egress-cloud-costs.md) - [How to route Kubernetes logs using FluentD?](https://ctrlb.ai/blogs/how-to-route-kubernetes-logs-using-fluentd.md) - [Unraveling the Mysteries of Digital Footprints: Understanding Traces in the Digital World](https://ctrlb.ai/blogs/understanding-traces-in-the-digital-world.md) - [Cracking the Code: Understanding High Cardinality in Metrics](https://ctrlb.ai/blogs/understanding-high-cardinality-in-metrics.md) - [The High Costs of Observability: Uncovering the Burden on Modern Systems](https://ctrlb.ai/blogs/the-high-costs-of-observability.md) - [When should you prefer CtrlB over self-hosting Elastic or Loki?](https://ctrlb.ai/blogs/when-should-you-prefer-ctrlb-over-self-hosting-ela.md) - [Separating storage and compute - holy grail to cut down costs](https://ctrlb.ai/blogs/separating-storage-and-compute---holy-grail-to-cut.md) - [Why are legacy observability solutions so expensive?](https://ctrlb.ai/blogs/why-are-legacy-observability-solutions-so-expensiv.md) --- --- title: "Tag: simran" canonical: "https://ctrlb.ai/blogs/tag/simran" --- # Blog posts tagged “simran” Canonical listing: https://ctrlb.ai/blogs/tag/simran ## Articles - [Detecting Node-Hopping Attackers: Correlating Traces and Logs at Sub-Second Speed](https://ctrlb.ai/blogs/detecting-node-hopping-attackers-correlating-trace.md) --- --- title: "Tag: Simran" canonical: "https://ctrlb.ai/blogs/tag/Simran" --- # Blog posts tagged “Simran” Canonical listing: https://ctrlb.ai/blogs/tag/Simran ## Articles - [Cost-Effective Telemetry Scaling with CtrlB’s Cloud Object Storage](https://ctrlb.ai/blogs/cost-effective-telemetry-scaling.md) - [Optimizing Observability for Edge Computing Environments](https://ctrlb.ai/blogs/optimizing-observability-for-edge-computing-enviro.md) --- --- title: "Tag: Storage" canonical: "https://ctrlb.ai/blogs/tag/Storage" --- # Blog posts tagged “Storage” Canonical listing: https://ctrlb.ai/blogs/tag/Storage ## Articles - [Tips to save egress cloud costs](https://ctrlb.ai/blogs/tips-to-save-egress-cloud-costs.md) - [How to route Kubernetes logs using FluentD?](https://ctrlb.ai/blogs/how-to-route-kubernetes-logs-using-fluentd.md) - [Cracking the Code: Understanding High Cardinality in Metrics](https://ctrlb.ai/blogs/understanding-high-cardinality-in-metrics.md) - [Why archiving old data is a bad idea?](https://ctrlb.ai/blogs/why-archiving-cold-data-doesnt-work.md) - [Separating storage and compute - holy grail to cut down costs](https://ctrlb.ai/blogs/separating-storage-and-compute---holy-grail-to-cut.md) - [Why are legacy observability solutions so expensive?](https://ctrlb.ai/blogs/why-are-legacy-observability-solutions-so-expensiv.md) --- --- title: "Adarsh Srivastava" canonical: "https://ctrlb.ai/blogs/author/adarsh-srivastava" role: "Co-Founder / CEO" --- # Adarsh Srivastava Building CtrlB — an observability control plane for telemetry at scale. [LinkedIn](https://www.linkedin.com/in/adarsh-srivastava-016795114/) Canonical profile: https://ctrlb.ai/blogs/author/adarsh-srivastava ## Articles - [Power of Observability Pipelines: Understanding Their Role and Impact on Organisational Efficiency](https://ctrlb.ai/blogs/power-of-observability-pipelines.md) - [Why archiving old data is a bad idea?](https://ctrlb.ai/blogs/why-archiving-cold-data-doesnt-work.md) - [hello world](https://ctrlb.ai/blogs/hello-world.md) --- --- title: "Adarsh Srivastava" canonical: "https://ctrlb.ai/blogs/author/adarsh-srivastava" role: "Co-Founder / CEO" --- # Adarsh Srivastava Building CtrlB — an observability control plane for telemetry at scale. [LinkedIn](https://www.linkedin.com/in/adarsh-srivastava-016795114/) Canonical profile: https://ctrlb.ai/blogs/author/adarsh-srivastava ## Articles - [Tips to save egress cloud costs](https://ctrlb.ai/blogs/tips-to-save-egress-cloud-costs.md) - [How to route Kubernetes logs using FluentD?](https://ctrlb.ai/blogs/how-to-route-kubernetes-logs-using-fluentd.md) - [When should you prefer CtrlB over self-hosting Elastic or Loki?](https://ctrlb.ai/blogs/when-should-you-prefer-ctrlb-over-self-hosting-ela.md) - [Separating storage and compute - holy grail to cut down costs](https://ctrlb.ai/blogs/separating-storage-and-compute---holy-grail-to-cut.md) - [Why are legacy observability solutions so expensive?](https://ctrlb.ai/blogs/why-are-legacy-observability-solutions-so-expensiv.md) --- --- title: "Pranav Rastogi" canonical: "https://ctrlb.ai/blogs/author/pranav-rastogi" role: "Chief Architect" --- # Pranav Rastogi Architecture and systems design across CtrlB’s data and query layers. [LinkedIn](https://www.linkedin.com/in/pranav-rastogi/) Canonical profile: https://ctrlb.ai/blogs/author/pranav-rastogi ## Articles --- --- title: "Pradyuman" canonical: "https://ctrlb.ai/blogs/author/pradyuman" role: "Senior Software Engineer" --- # Pradyuman Engineering CtrlB’s ingestion, pipelines, and developer experience. [LinkedIn](https://www.linkedin.com/in/ppradyu/) Canonical profile: https://ctrlb.ai/blogs/author/pradyuman ## Articles - [Cold Storage Doesn’t Have to Be Cold: How CtrlB Keeps Your Old Logs Lively](https://ctrlb.ai/blogs/cold-storage-doesnt-have-to-be-cold-how-ctrlb-keep.md) - [Control Plane: One Plane To Control Them All](https://ctrlb.ai/blogs/control-plane-one-plane-to-control-them-all.md) - [Unraveling the Mysteries of Digital Footprints: Understanding Traces in the Digital World](https://ctrlb.ai/blogs/understanding-traces-in-the-digital-world.md) - [Cracking the Code: Understanding High Cardinality in Metrics](https://ctrlb.ai/blogs/understanding-high-cardinality-in-metrics.md) - [The High Costs of Observability: Uncovering the Burden on Modern Systems](https://ctrlb.ai/blogs/the-high-costs-of-observability.md) --- --- title: "Balasubramanian P" canonical: "https://ctrlb.ai/blogs/author/balasubramanian-p" role: "Co-Founder / CTO" --- # Balasubramanian P Technical leadership across infrastructure, reliability, and platform. [LinkedIn](https://www.linkedin.com/in/balasubramanian-p-991059148/) Canonical profile: https://ctrlb.ai/blogs/author/balasubramanian-p ## Articles