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Have you heard? ClickHouse is winning the observability wars
13 days ago
- ClickHouse excels in observability at large scales (e.g., 10TB/day) while maintaining consistency and performance, unlike other tools like Elastic, LGTM, and Datadog that struggle.
- Observability built on columnar storage represents a fundamental improvement over the traditional three pillars model, offering better handling of high cardinality data and scalability.
- Many newer observability vendors use columnar storage but market themselves as cheaper alternatives to Datadog without highlighting the architectural advantages, obscuring innovation.
- The author argues that the value in observability comes from unified data (like traces or structured logs) rather than segmented pillars, which is threatened by Datadog's business model.
- ClickHouse's willingness to align with 'Observability 2.0' contrasts with other vendors, as they focus on solving customer problems rather than just competing on price.
Show HN: Observability for Coding Agents and LLM Applications
10 hours ago
- Telemetry.dev is an observability platform for AI and LLM apps, capturing every model call, tool step, and retrieval as OpenTelemetry traces with attached tokens, cost, latency, and errors.
- Built on OpenTelemetry, it supports any framework and language without lock-in; first trace can be set up in five minutes.
- Free plan offers 10,000 spans per month with no credit card required, 7-day retention, one project, and two seats.
- Real-time monitoring surfaces failures as they happen, with error type, status, and full trace attached.
- Key metrics include volume, cost, p95 latency, and errors, split by model, provider, and environment.
- Input and output capture is per-environment, with built-in secret redaction and custom redaction patterns.
- Traces can be searched by name, filtered by status, environment, framework, or session, and opened in full detail.
- Supports SDKs for TypeScript and Python, plus integrations with Vercel AI SDK, LangChain, and any OTLP/HTTP exporter.
- Cost is calculated server-side using real token usage against current pricing for 4,700+ models, with optional per-span override.
Let's break autovacuum in Postgres: reproducing failures to make it observable
10 hours ago
- Autovacuum prevents bloat and transaction ID wraparound, but failures are silent and hard to monitor.
- Using raw dead tuple count is misleading; compute 'autovacuum pressure' (dead tuples / threshold) for a normalized signal.
- Coroot detects problems by combining pressure >2x and at least 512MiB of dead data, then identifies root cause via system views.
- Five common failure modes: autovacuum disabled on table, long transaction holding horizon, replication slot pinning horizon, all workers busy, vacuum throttled.
- Each failure has distinct detection fingerprints: reloptions, xmin age by holder, worker count, wait event.
- The tool provides specific findings with table name, cause, and fix, reducing guessing.
- The approach uses existing Postgres system views (pg_stat_user_tables, pg_stat_activity, pg_stat_progress_vacuum, etc.) with smart aggregation.

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#observability

Have you heard? ClickHouse is winning the observability wars

Show HN: Observability for Coding Agents and LLM Applications

Let's break autovacuum in Postgres: reproducing failures to make it observable