Product
Logs, metrics and traces, with the deployment attached.
ClueLake stores telemetry with the deployment context already attached, so an investigation starts from the change rather than ending at it.
Deployment context
Your telemetry already knows what happened. ClueLake keeps the context.
ClueLake makes deployment context a first-class part of the observability model. These fields are stamped onto every event as it arrives, by the platform that performed the deployment — not read from the payload, which the workload controls.
- workspace_id
- environment_id
- application
- deployment_id
- image_digest
- build_id
- commit_sha
- replica
Because the fields are on the event rather than reconstructed afterwards, correlating a regression with the change that caused it is a query rather than an investigation.
What changed
See the regression and the change around it in one place.
ClueLake lines up the incident with the infrastructure events on either side of it, so the sequence is visible rather than reconstructed from memory.
Timeline
- 13:58Healthyp99 2.1s · errors 0.2%
- 14:02Deploymentcheckout-production · build #482
- 14:03p99 increased2.1s → 8.4s
- 14:04Error rate increased0.2% → 4.2%
- 14:05Rolled backto sha256:731f01
The change
- Previous image
- sha256:731f01
- Current image
- sha256:9ab3c2
- Build
- #482
- Commit
- a1b2c3d — cache: drop the per-request client
- Actor
- ada@example.com
Unified telemetry
Logs. Metrics. Traces. One investigation.
Bring the signals you already have together in a workflow designed for investigation. Keep the tooling that works — ClueLake is the backend the evidence lands in.
- ERRORupstream request failed: payments timeout after <n>ms1,829
- WARNconnection pool exhausted, waiting for a free connection640
- INFOhandling POST /api/checkout25,104
Every log line carrying a trace id links to its trace; every trace carries the deployment that produced it. The service map is built from trace relationships, and from eBPF flow data where it is available.
Query
Know what a query will read before you run it.
PromQL for the supported query set, log and trace search, and read-only SQL for everything else. Each query is costed first, so an expensive one is a decision rather than a surprise.
histogram_quantile(
0.99,
sum by (le, app) (
rate(http_request_duration_seconds_bucket{
app="checkout", env="production"
}[5m])
)
)