Observability

Two separate things answer two separate questions here. Telemetry explains what happened and why, and it expires. The usage_event ledger records what work was done and it does not. This page covers the ledger and the commands you actually run when something is stuck. Telemetry covers the traces, metrics and logs. Both assume the service list from Deployment topology.

  signals ─▶ containers ─▶ alloy ─▶ loki, tempo, victoriametrics ─▶ grafana (expires)
                │
  usage ────────┴─▶ PostgreSQL ─▶ usage_event (durable, never expires)

Starting the stack

--profile observability starts the collector, the three stores and Grafana. observability-init runs first and once, as root with only CHOWN, DAC_OVERRIDE and FOWNER, to create each store’s directory for the unprivileged user that owns it. This step is needed even for named volumes, because dropping all capabilities leaves those users unable to fix a root-owned directory.

AIZK_GRAFANA_ADMIN_PASSWORD=
AIZK_DOCKER_GID="$(stat -c %g /var/run/docker.sock)"
docker compose --profile observability --env-file .env -f src/deploy/docker-compose.yml up -d

Grafana on 127.0.0.1:3003 is the only host port anything in the Compose file publishes. Reach it locally, forward it over SSH, or use the gated console at admin.phvv.me/grafana.

Keep the observability stack off the network

Never expose Grafana, Loki, Tempo, VictoriaMetrics, Alloy or the Docker socket. Alloy reads the socket read-only, which still means broad visibility into every container’s metadata and logs, so treat it as host infrastructure rather than an application.

Durable usage

UsageAccountingJob in src/aizk/usage.py appends one row per successful operation to the immutable usage_event table, through PgQueuer on the aizk_usage_event entrypoint. Enqueue is transactional and the handler is idempotent, so a job PgQueuer reclaims after a late acknowledgement is stored once.

Each event carries the authenticated actor, the exact target scope IDs, request bytes, response bytes, the item count and the capture time. A multi-scope event is attributed to every target, because each organization took part in it, which means actor totals are the nonduplicated view and scope totals deliberately are not.

Storage reporting keeps two numbers apart on purpose. Per scope-set you get artifact revision count and logical original bytes. Globally you get unique physical blobs, original bytes, stored bytes and bytes saved by compression. A blob shared by two organizations counts twice logically and once physically, and pretending otherwise would either overstate the disk or understate who used it.

The five second overview

docker compose --env-file .env -f src/deploy/docker-compose.yml exec -T worker aizk admin health

ops.health() fans out concurrently over the migration head, the RLS verifier, row counts, the queue overview, per scope-set corpus progress, usage totals and the four model endpoints, then runs one real recall. Endpoint probes time out at 2 seconds and the recall at 3.5, so the whole report is bounded.

Run it in worker, never in server. The public process has no owner credential by design.

A healthy report has an up-to-date migration, no RLS violations, Logto identity mode, all four endpoints reachable with matched true, no retained queue failures, processed chunks catching up with stored chunks, and a recall block with candidates and no error.

A stuck queue

The doctor is read only, exits nonzero when there are current blockers, and never changes state.

docker compose --env-file .env -f src/deploy/docker-compose.yml exec -T worker \
  aizk admin queue doctor

Its JSON groups current retained failures by entrypoint and a safe error fingerprint, then reports stale picked leases, long-running live leases, recent exception aggregates, durable failed conversions, and conversions whose durable active state points at a job the queue has already finished. That last class is the usual cause of a conversion that looks busy forever. Complete counts stay separate from the bounded detail lists, and error messages are redacted by default because an upstream exception can quote source text.

Defaults are 15 stale minutes, 60 long-running minutes, a 24 hour history window and 50 detail rows. Widen them when the workload is unusual, and opt into messages only as a trusted operator.

aizk admin queue doctor --stale-minutes 30 --history-hours 72 --limit 100
aizk admin queue doctor --show-error-messages

Fix the reported cause before retrying, then requeue the class you repaired.

aizk admin queue retry conversion --limit 100
aizk admin queue retry graph --limit 100
aizk admin queue retry profile --limit 100

Retrying only requeues retained failures. It does not repair an invalid source, an ontology mismatch, abandoned durable state or a converter bug.

Read-only extraction diagnosis

To see why one chunk produced nothing, run extraction and grounding over it without writing.

docker compose --env-file .env -f src/deploy/docker-compose.yml run --rm --no-deps worker \
  admin graph diagnose-extraction 019f6bf4-ec29-72c5-93d2-59f791ae42d0

This needs owner access to read an arbitrary stored chunk, so it runs in worker. It does not mark the chunk processed and writes no graph rows. The JSON holds the proposed extraction, a rejection reason per fact, and the grounded subset with acceptance counts. missing_quote, unsupported_quote, unresolved_endpoint, self_relation and generic_relation are deliberate evidence failures rather than transport errors, and Grounding and consolidation explains each one.

Next

• Telemetry follows one query across every service it touched.
• The job system explains what the queue is running.
• Grounding and consolidation decodes the rejection reasons.
• Upgrades covers the health check’s place in a deployment.
• The release gate lists what must be green before traffic.