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0223383d94
* fix(e2e): make the datadog read-back find what DataDog actually indexes Live verification of the merged #33604 against real DataDog (us5) exposed three read-back defects that the local-sink tests could never see; all three fixes are verified against the real API: - Marker search: DataDog consumes the shipped JSON message into the event's attributes and leaves the indexed message EMPTY, so the full-text '"marker"' query matched nothing and every test failed with zero events. The query is now '*:*marker*', which scans all attributes (the marker sits in messages.content); verified to return exactly the event for the call. - Rate limit: the Logs Search API budget is 2 requests per 10s org-wide (x-ratelimit-name logs_public_search_api). Polling at POLL_INTERVAL=5s sat exactly at the limit and the reader hard-failed on the first 429. Searches now pace at DD_SEARCH_INTERVAL (10s default) and a 429 backs off and retries up to 5 times; only non-429 failures stay hard fails. - Envelope status: DataDog re-derives the indexed event status from the parsed payload's status attribute ('success') and normalizes it to its OK severity, so the assertion expects 'ok', not the shipped 'info'. Live run: chat_completions and responses pass every assertion including the exact response-cost cross-check; messages red-pins the LIT-4447 duplicate for real (one call -> two sync-sweep copies + one async batch copy, same request id, confirmed in proxy debug logs). The duplicate is race-dependent, so the pin flickers until #33589 lands. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * test(e2e): datadog log delivery for streamed chat, messages, and responses Rewritten from the dd-sink version (original #33566) to judge delivery on what real DataDog ingested, matching the merged #33604 conversion: the dd_logs reader searches events back through the Logs Search API and the assertions validate the indexed envelope (source:litellm tag, ok status) and the StandardLoggingPayload fields under the event's attributes. Each streamed test drives one STREAMED call per route, asserts the stream actually streamed (event-stream content type, >0 chunks, no upstream error event), then pins exactly one DataDog event whose payload records stream=true, the aggregated token count, and a response_cost equal to the /spend/logs row for the call - a stream's headers ship before its cost exists, so the spend row is the cross-check anchor, and the spend row and DataDog event must also agree on total_tokens. Coverage registry: adds logging.datadog.stream.exports_metric exercised on chat_completions, messages, and responses. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * Update test_datadog_log_e2e.py --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
25 lines
6.2 KiB
YAML
25 lines
6.2 KiB
YAML
# Logging integration delivery (behavior features). Grounded in litellm/integrations/.
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- {id: logging.s3.success.writes_object, module: logging, tier: P0, event: success, assertions: [writes_object], exercised_on: [chat_completions, messages, embeddings], source: "integrations/s3_v2.py", rationale: "Primary audit trail; batch flush no-drop"}
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- {id: logging.s3.failure.writes_object, module: logging, tier: P0, event: failure, assertions: [writes_object], exercised_on: [chat_completions, messages], source: "integrations/s3_v2.py", rationale: "Failed calls persisted for compliance"}
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- {id: logging.gcs_bucket.success.writes_object, module: logging, tier: P0, event: success, assertions: [writes_object], exercised_on: [chat_completions, messages, embeddings], source: "integrations/gcs_bucket/gcs_bucket.py", rationale: "GCS parallel to S3"}
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- {id: logging.datadog.success.exports_metric, module: logging, tier: P0, event: success, assertions: [exports_metric], exercised_on: [chat_completions, messages, responses, embeddings], source: "integrations/datadog/datadog.py", rationale: "Powers dashboards/alerts; cardinality regressions common"}
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- {id: logging.datadog.stream.exports_metric, module: logging, tier: P0, event: stream, assertions: [exports_metric], exercised_on: [chat_completions, messages, responses], source: "integrations/datadog/datadog.py", rationale: "Streaming aggregates usage after the last chunk; delivery and cost must survive that path"}
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- {id: logging.datadog.failure.exports_metric, module: logging, tier: P0, event: failure, assertions: [exports_metric], exercised_on: [chat_completions], source: "integrations/datadog/datadog.py", rationale: "Failure metrics for alerting/SLO"}
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- {id: logging.prometheus.success.exports_metric, module: logging, tier: P0, event: success, assertions: [exports_metric], exercised_on: [chat_completions, messages, embeddings], source: "integrations/prometheus.py", rationale: "Standard OSS metrics; per-key cardinality (existing e2e)"}
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- {id: logging.otel.success.exports_metric, module: logging, tier: P0, event: success, assertions: [exports_metric], exercised_on: [chat_completions, messages, responses, embeddings], source: "integrations/otel/logger.py", rationale: "OTEL spans on every call path"}
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- {id: logging.otel.stream.exports_metric, module: logging, tier: P0, event: stream, assertions: [exports_metric], exercised_on: [chat_completions, messages, responses], source: "integrations/otel/logger.py", rationale: "Streaming closes the LLM span from the stream path; historically prone to duplicate/orphaned spans"}
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- {id: logging.otel.stream.records_ttft, module: logging, tier: P1, event: stream, assertions: [records_ttft], exercised_on: [chat_completions, messages, responses], source: "integrations/otel/mappers/genai.py", rationale: "TTFT is the streaming latency SLI; a zero or span-length value silently corrupts dashboards"}
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- {id: logging.otel.failure.exports_metric, module: logging, tier: P0, event: failure, assertions: [exports_metric], exercised_on: [chat_completions, messages], source: "integrations/otel/logger.py", rationale: "Error spans for observability continuity"}
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- {id: logging.braintrust.success.logs_spend, module: logging, tier: P1, event: success, assertions: [logs_spend], exercised_on: [chat_completions, messages], source: "integrations/braintrust_logging.py", rationale: "Evals platform spend"}
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- {id: logging.langsmith.success.logs_spend, module: logging, tier: P1, event: success, assertions: [logs_spend], exercised_on: [chat_completions, messages], source: "integrations/langsmith.py", rationale: "LangChain ecosystem"}
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- {id: logging.arize.success.logs_spend, module: logging, tier: P1, event: success, assertions: [logs_spend], exercised_on: [chat_completions, embeddings], source: "integrations/arize/arize.py", rationale: "ML-ops observability"}
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- {id: logging.mlflow.success.logs_spend, module: logging, tier: P1, event: success, assertions: [logs_spend], exercised_on: [chat_completions], source: "integrations/mlflow.py", rationale: "Experiment tracking cost/run"}
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- {id: logging.opik.success.logs_spend, module: logging, tier: P1, event: success, assertions: [logs_spend], exercised_on: [chat_completions], source: "integrations/opik/opik.py", rationale: "Eval platform spend/case"}
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- {id: logging.openmeter.success.exports_metric, module: logging, tier: P1, event: success, assertions: [exports_metric], exercised_on: [chat_completions, messages, embeddings], source: "integrations/openmeter.py", rationale: "Usage metering for billing"}
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- {id: logging.literal_ai.success.logs_spend, module: logging, tier: P1, event: success, assertions: [logs_spend], exercised_on: [chat_completions, messages], source: "integrations/literal_ai.py", rationale: "Tracing platform spend"}
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- {id: logging.posthog.success.exports_metric, module: logging, tier: P1, event: success, assertions: [exports_metric], exercised_on: [chat_completions, messages], source: "integrations/posthog.py", rationale: "Product analytics batching"}
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- {id: logging.azure_storage.success.writes_object, module: logging, tier: P1, event: success, assertions: [writes_object], exercised_on: [chat_completions, messages], source: "integrations/azure_storage/azure_storage.py", rationale: "Azure blob for enterprise"}
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- {id: logging.cloudzero.success.logs_spend, module: logging, tier: P1, event: success, assertions: [logs_spend], exercised_on: [chat_completions], source: "integrations/cloudzero/cloudzero.py", rationale: "Cost ops correlation"}
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- {id: logging.focus.success.writes_object, module: logging, tier: P1, event: success, assertions: [writes_object], exercised_on: [chat_completions, messages], source: "integrations/focus/focus_logger.py", rationale: "Cost mgmt multi-destination export"}
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- {id: logging.niche_integrations.success.logs_spend, module: logging, tier: P2, event: success, assertions: [logs_spend], exercised_on: [chat_completions], source: grammar, rationale: "SMOKE cohort: athina/galileo/deepeval/langtrace/weave/lunary/humanloop/traceloop/helicone/argilla/newrelic/sqs/supabase/dynamodb/agentops/lago/etc"}
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- {id: logging.niche_integrations.failure.logs_spend, module: logging, tier: P2, event: failure, assertions: [logs_spend], exercised_on: [chat_completions], source: grammar, rationale: "SMOKE niche failure path"}
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