hyperdx

Resolve production issues, fast. An open source observability platform unifying session replays, logs, metrics, traces and errors powered by ClickHouse and OpenTelemetry.

⭐ 9,478 stars on GitHub · 🍴 393 forks · 📜 License: mit · 💻 Language: TypeScript

What is hyperdx?

When your team is drowning in logs, traces, metrics, and frontend errors spread across multiple tools, HyperDX gives you a unified observability workspace built for fast incident investigation. Its differentiator is clear: it brings Kibana-style search and visualization to ClickHouse, with OpenTelemetry ingestion and session replay in the same flow.

Main components

• Unified search across logs, traces, metrics, errors, and browser session replays.
• ClickHouse-backed storage and querying for high-volume telemetry workloads.
• OpenTelemetry support for collecting data from Kubernetes, backend services, and common language runtimes.
• Full-text and property-based search syntax, with SQL available but not required.
• APM views for tracking HTTP requests, database queries, latency, and service health.
• Dashboards, anomaly/event delta analysis, live tailing, and click-driven alert setup.

Clear use cases

• Investigate production incidents by jumping from an error to related logs, traces, metrics, and replayed user sessions.
• Build an observability stack around an existing ClickHouse cluster without adopting a full SaaS monitoring platform.
• Give developers a simpler way to search production telemetry without requiring deep SQL or SRE-specific query language knowledge.
• Monitor Kubernetes and microservices environments using OpenTelemetry as the common instrumentation layer.
• Replace fragmented log search, APM, and frontend monitoring tools with one self-hosted workflow.

The biggest strength is correlation across telemetry types — HyperDX is designed around the actual debugging path, not around isolated data silos. You can start with a bad request, trace it through backend spans, inspect matching logs, check service metrics, and even replay what the user saw in the browser. That makes it more practical during incidents than stitching together Elasticsearch, Grafana, Jaeger, Sentry, and a replay tool by hand.

Commercial observability platforms like Datadog, New Relic, and Honeycomb are polished, but pricing can become painful once telemetry volume grows. HyperDX’s ClickHouse foundation is the key tradeoff: you take on more infrastructure responsibility, but you gain a path to store and query large volumes of events without sending every byte to a metered SaaS vendor. The interface also lowers the barrier for developers who need answers quickly but do not want to become query-language specialists.

Deployment is approachable for testing, with an all-in-one Docker image that includes the UI, API, OpenTelemetry collector, ClickHouse, and MongoDB through ClickStack. For production, you should still treat it like a real observability system: plan retention, resources, backups, network exposure, and ClickHouse operations carefully. The project recommends at least 4GB RAM and 2 cores for testing, which is reasonable, but serious workloads will depend heavily on ingest volume and query patterns.

Best for engineering teams, platform teams, and cost-conscious SaaS companies that want self-hosted, ClickHouse-powered observability without splitting incident response across five separate tools.

Topics: the project is tagged with popular topics:

• 🏷️ alerting
• 🏷️ analytics
• 🏷️ apm
• 🏷️ application-monitoring
• 🏷️ clickhouse
• 🏷️ dashboard
• 🏷️ frontend-monitoring
• 🏷️ kubernetes
• 🏷️ log-management
• 🏷️ logs

📸 Screenshots

Quick install

The project supports Docker Compose:

git clone https://github.com/hyperdxio/hyperdx.git
cd hyperdx
docker compose up -d

Check the README in the repo for required env variables.

Minimum system requirements

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Resources

• 🔗 GitHub: hyperdxio/hyperdx
• 🌐 Homepage: https://hyperdx.io/
• 📚 Official docs: see README in the repo
• 💬 Community: GitHub Issues + Discussions

Article compiled from GitHub data on 05/05/2026. Star/fork counts may have changed — see live numbers via the GitHub link.