Cribl

Cribl — observability pipeline for routing, shaping, and reducing log data.

MonitoringComplex

Stack Components

PipelineCribl Stream

Why

Filter and route logs before storage, reduce costs, transform between formats. OpenTelemetry became the CNCF instrumentation standard in 2024, used by 70%+ of cloud-native orgs.

Sentry and Datadog lead error tracking with 30k+ paying customers each.

When

Use for high-volume log processing. Avoid for small log volumes.

How

1. Deploy Cribl Stream 2. Configure sources and destinations 3. Add filters and transformations 4. Monitor pipeline

Example

// Route ERROR logs to Splunk, rest to S3 if (event.level === 'error') route('splunk'); else route('s3-archive'); // Cribl — usage example import { Cribl } from "mon2-cribl"; const result = await cribl({ input: "data" }); console.log(result);

Common Problems

⚠Pipeline throughput — add worker processes
⚠Circular routing — validate destination config
⚠Configuration drift between environments — use infrastructure-as-code (Terraform/Pulumi) to keep dev/staging/prod identical

Best Practices

✓Use pre-filtering to reduce volume early
✓Version pipeline configs in Git
✓Set up health checks and graceful shutdown — handle SIGTERM to drain in-flight requests before exit

FAQ

What is Cribl and what projects is it for?

Cribl — observability pipeline for routing, shaping, and reducing log data. It pairs layered technologies for end-to-end type safety and out-of-the-box deploys, fitting everything from MVPs to production SaaS.

How do I spin up a working demo with Cribl in 10 minutes?

Two steps: (1) Deploy Cribl Stream; (2) Configure sources and destinations. Get the local dev server running, then wire one real data source to verify the end-to-end loop.

What is the most common pitfall with Cribl and how do I avoid it?

The most common pitfall is: Pipeline throughput — add worker processes. Avoid it by adding the matching checklist to your PR template and running type-check + lint + unit tests in CI — three gates before merge.

What are the best practices for Cribl?

Best practices for Cribl include: Use pre-filtering to reduce volume early; Version pipeline configs in Git; Set up health checks and graceful shutdown — handle SIGTERM to drain in-flight requests before exit. The first one — "Use pre-filtering to reduce volume early" — should be set up at project init to avoid costly rework later.

When should I NOT choose Cribl?

Pick something else in three cases: (1) the team has zero experience with the core framework and the deadline is tight (learning-curve risk); (2) it's a pure content/marketing site (Astro is lighter); (3) you need very high throughput (>10k RPS) or strict-consistency microservices (Go/Rust). Cribl shines for mid-to-high complexity full-stack and API work — don't over-engineer simple static pages with it.