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Chapter DBatch API labPage 6 of 8

Batch API lab

Add observability and tests

Batch API lab is production work only when one frozen failure can be reproduced, one measurable gate can stop a release, and one operator can safely reverse it.

~14 minObservability

Before you start

Why this matters

Read this incident aloud: a provider partially completes a batch, retries callbacks, and returns results out of input order. In two minutes, write the earliest deterministic check that should fail, the telemetry signal you would inspect, and the action that must not happen automatically. Compare your answer with this chapter's boundary: batch payloads exclude restricted documents; signed result URLs expire and workers have write access only to the staging index.

1Learn the idea

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Instrument metrics, logs, and traces

Instrumentation should answer four questions from one correlation ID: what release ran, which stage failed, how long each stage took, and what decision was made. Emit structured events, not interpolated prose. A useful event includes event, request_id, trace_id, release, stage, outcome, latency_ms, and a small set of bounded labels. Record embedding_batch_completion_ratio as a counter or ratio with stable dimensions; never use request IDs, user text, or error messages as metric labels.

Trace the transaction using a root span and child spans around meaningful boundaries. Add counts, versions, finish reasons, and safe IDs. Do not attach full prompts, retrieved passages, secrets, or tool arguments. Logs explain discrete decisions, metrics detect population changes, and traces reconstruct one request; none replaces the others. Sampling may reduce ordinary success traces, but always retain errors and policy denials under the approved retention policy.

Create an operational test that sends a synthetic request tagged probe=true, then checks that the metric increments, the trace has required child spans, and the log can be found by trace ID. An alert is useful only if it links to a runbook and has a clear action. For this lab, the release rule remains at least 99.5% completion, zero duplicate vectors, and reconciliation within 30 minutes, and the first response is to reconcile by custom_id, retry only failed items, verify the staging index, then atomically swap its alias.

For observability, test telemetry as a product interface. Assert required span names and log keys, cardinality-safe dimensions, redaction, and alert routing using synthetic events.

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Focused implementation artifact

def emit_decision(metrics, logger, *, trace_id, release, outcome, latency_ms):
    metrics.counter("embedding_batch_completion_ratio").add(1, {"release": release, "outcome": outcome})
    logger.info("batch_index_20260718", extra={
        "trace_id": trace_id, "release": release,
        "outcome": outcome, "latency_ms": latency_ms,
        "payload": "[REDACTED]",
    })

def test_telemetry(fake_metrics, fake_logger):
    emit_decision(fake_metrics, fake_logger, trace_id="t-1", release="canary", outcome="deny", latency_ms=18)
    assert fake_logger.last["payload"] == "[REDACTED]"

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Verify the telemetry path

Create a synthetic request that reaches every normal stage and another that reproduces a retried completion webhook inserts the same chunk twice and silently increases index size. The success trace must contain a root span and ordered child spans; the failure trace must mark the first broken boundary and skip spans for actions that never ran. Assert context propagation rather than relying on visually similar timestamps.

Emit embedding_batch_completion_ratio with release, outcome, and a bounded domain slice. Keep request IDs in logs and traces, never metric labels. A structured log should include trace ID, stage, decision, latency, and policy or model version. Redaction happens before export and is tested against nested secrets and identifiers. Sampling may drop ordinary successes, but it must retain errors and policy denials under the documented retention cap.

Test the alert using synthetic events until it opens the runbook with the correct query. The alert condition derives from successful unique chunks per dollar and at least 99.5% completion, zero duplicate vectors, and reconciliation within 30 minutes; include window and minimum sample so one request cannot page an entire team. The first response is to reconcile by custom_id, retry only failed items, verify the staging index, then atomically swap its alias.

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