feat(openfeature): emit server-side EVP flagevaluation by leoromanovsky · Pull Request #18613 · DataDog/dd-trace-py

leoromanovsky · 2026-06-14T12:20:53Z

Motivation

Server-side feature-flag evaluations need to be observable in the same backend track as the rest of the cross-SDK rollout so APM can judge rollout smoothness and time-to-land from real evaluation counts, not only app-local behavior or metric-side approximations. This contribution gives Python services backend-visible flagevaluation telemetry with bounded cardinality and batching, so customers and internal dogfooding can verify which flags evaluated, for which targeting keys, and how often, while preserving the existing feature_flag.evaluations metric path.

Changes

Adds a Python EVP flagevaluation writer behind DD_FLAGGING_EVALUATION_COUNTS_ENABLED; existing OTel feature_flag.evaluations metrics remain unchanged.
Registers an OpenFeature finally_after logging hook that captures a cheap evaluation snapshot and hands it to a background writer.
Renames the capture hook to FlagEvalEVPHook / _flag_eval_evp_hook.py so it is distinct from FlagEvalMetricsHook.
Posts batched payloads to the Agent EVP proxy route /evp_proxy/v2/api/v2/flagevaluation with X-Datadog-EVP-Subdomain: event-platform-intake.
Aggregates evaluations by schema-visible dimensions: flag key, variant key, allocation key, runtime-default state, error message, targeting key, and pruned evaluation context.
Flattens and prunes evaluation context before enqueue with deterministic 256-field / 256-character bounds.
Splits encoded flagEvaluations request bodies under the shared EVP payload limit before POSTing.
Degrades a single oversized full-tier row by dropping targeting_key and context; if the degraded row still cannot fit, the writer drops and logs it.
Uses evaluation-time metadata to populate first_evaluation and last_evaluation rather than flush time.
Keeps accepting the legacy DataDogProvider(initialization_timeout=...) keyword for compatibility, while async initialization no longer uses it.

Evaluation Capture And Flush

flowchart TD
  Eval["OpenFeature evaluation"] --> Metrics["FlagEvalMetricsHook\nexisting OTel metric"]
  Eval --> Logging["FlagEvalEVPHook\nfinally_after"]
  Logging --> Snapshot["Cheap scalar snapshot\nflag, variant, allocation,\ntargeting_key, context, eval time"]
  Snapshot --> Queue["Bounded non-blocking queue\nQueue size 4096"]
  Queue --> Drain["Background drain worker\n0.1s queue drain"]
  Drain --> Aggregate["FlagEvaluationWriter aggregation\nfull tier then degraded tier"]
  Aggregate --> Flush["Periodic flush\n10s interval"]
  Flush --> EVP["Agent EVP proxy\n/evp_proxy/v2/api/v2/flagevaluation"]

Payload Limit Handling

flowchart TD
  Events["Aggregated flagEvaluations"] --> Encode["Encode each event with compact JSON"]
  Encode --> FitsEvent{"Single event fits\ninside shared EVP limit?"}
  FitsEvent -- "yes" --> Batch["Add to current payload"]
  FitsEvent -- "no" --> Degrade["Drop targeting_key and context\nfor that event only"]
  Degrade --> FitsDegraded{"Degraded event fits?"}
  FitsDegraded -- "yes" --> Batch
  FitsDegraded -- "no" --> Drop["Drop and count\npayload_limit"]
  Batch --> FitsPayload{"Payload still <=\nDEFAULT_EVP_PAYLOAD_SIZE_LIMIT?"}
  FitsPayload -- "yes" --> Continue["Continue batching"]
  FitsPayload -- "no" --> Split["Close current payload\nstart next payload"]
  Split --> Continue
  Continue --> Post["POST each payload sequentially"]

Decisions

Keep this as a dual-path implementation: the EVP logging writer is additive and separately gated, while the existing OTel metric hook remains registered when the provider is enabled.
Use the existing Agent EVP proxy transport and the canonical flagevaluation backend route instead of adding a Python-specific transport or schema.
Keep the evaluation logging hook non-blocking on application evaluation threads: queueing is best-effort, bounded, and drop-counted under backpressure.
Do aggregation, JSON serialization, payload splitting, and POSTs only in the background writer path.
Split flagEvaluations payloads by encoded byte size using DEFAULT_EVP_PAYLOAD_SIZE_LIMIT; send split payloads sequentially from the writer thread.
Use a two-tier aggregation model: full buckets retain targeting_key and pruned context; degraded buckets retain only lower-cardinality schema dimensions.
Store targeting_key in the dedicated EVP field and remove targetingKey / targeting_key from context before building context keys or payloads.
Do not emit OpenFeature reason because it is not accepted by the canonical worker schema, and do not populate targeting_rule.key until Python exposes real targeting-rule metadata.
Keep openfeature-sdk as an application/test/lint dependency rather than adding it as a runtime dependency of ddtrace; the dogfooding app dependency floor is being tightened separately in https://github.com/DataDog/ffe-dogfooding/pull/88.

Validation Evidence

Dogfooding App

ffe-dogfooding app-python was run with local dd-trace-py artifacts and reached PROVIDER_READY.
The running dogfooding app reported openfeature-sdk=0.9.0.
Evaluated ffe-dogfooding-string-flag through the Python dogfooding app with 5 evaluations per targeting key over one persistent connection:
- python-batch-keepalive-20260623T004034Z-alpha
- python-batch-keepalive-20260623T004034Z-bravo
- python-batch-keepalive-20260623T004034Z-charlie
App-side result: all evaluations returned variant_2 with allocation allocation-override-392dd7c149f8.

System Tests

Companion draft PR: Enable EVP flagevaluation system tests for Python system-tests#7183

Staging End-To-End

Dogfooding ran without the local mock-intake EVP tee/proxy, so the Agent sent EVP traffic through the normal backend path.
Retriever staging query returned 3 backend flagevaluation rows for the exact targeting keys above.
Each targeting key returned one row with evaluation_count=5:
- python-batch-keepalive-20260623T004034Z-alpha
- python-batch-keepalive-20260623T004034Z-bravo
- python-batch-keepalive-20260623T004034Z-charlie
All rows had flag.key=ffe-dogfooding-string-flag, variant.key=variant_2, and allocation.key=allocation-override-392dd7c149f8.

…gator - Two-tier aggregation (full → degraded → drop-counted); no ultra-degraded tier - Canonical context key: sorted, type-tagged, length-delimited (NOT a hash) - Caps GLOBAL_CAP=131072 / PER_FLAG_CAP=10000 / DEGRADED_CAP=32768 - Context pruning: ≤256 fields / ≤256 chars per reviewer concern #1 - Non-blocking queue.Queue(4096) enqueue with observable drop counter - EVP POST to /evp_proxy/v2/api/v2/flagevaluations with X-Datadog-EVP-Subdomain - Unit tests: canonical key, two-tier overflow, prune, enqueue, payload shape

…ATION_COUNTS_ENABLED killswitch - FlagEvaluationHook(Hook).finally_after: cheap capture + non-blocking enqueue only - Eval-time from metadata["dd.eval.timestamp_ms"]; fallback to hook-fire time - runtime_default_used from absent/None variant (reviewer concern #5) - Provider stamps dd.eval.timestamp_ms at eval boundary (_resolve_details) - Killswitch DD_FLAGGING_EVALUATION_COUNTS_ENABLED (default on) gates EVP path only - OTel FlagEvalHook (_flageval_metrics.py) byte-for-byte unchanged (PRES-01) - Tests: hook lifecycle, killswitch gating, OTel non-regression, metadata extraction

…OVIDER_ERROR Root cause: initialize() raised ProviderNotReadyError after the 10s RC wait, causing the OpenFeature SDK to dispatch PROVIDER_ERROR and mark the provider as ERROR. In ffe-dogfooding, with no FFE flags configured in the org, RC never delivers FFE_FLAGS config, so every start ended in PROVIDER_ERROR and the container exited. Fix: on timeout, log a warning, set _status = READY, and return normally. The SDK dispatches PROVIDER_READY; evaluations degrade to (default_value, ERROR, PROVIDER_NOT_READY) via _resolve_details() until config eventually arrives. On_configuration_received() remains the authoritative path for making config available, unchanged. Update affected tests to reflect the new READY-on-timeout contract.

…ad of PROVIDER_ERROR" This reverts commit 114216b.

…icorn pre-fork workers Root cause: initialize() was blocking on _config_received.wait(timeout=10s) and raising ProviderNotReadyError on expiry. Under gunicorn (and uWSGI) pre-fork workers, the OpenFeature SDK runs initialize() in a background thread; that thread is killed by fork() in each child process, so every worker's initialize() either misses the RC notification (READY in master fires before worker registers via _register_provider) or times out waiting — causing PROVIDER_ERROR and container exit. Evidence (dogfooding logs): 22:44:31 PROVIDER_READY ← worker 1 took fast path (config in master) 22:44:41 PROVIDER_ERROR ← worker 2 timed out (10 s exactly) Fix: remove the blocking wait entirely. initialize() now returns immediately if no config is available yet, matching the original pre-#16650 contract. The async path via on_configuration_received() remains the authoritative READY transition for both the master process and each forked worker (worker RC subscriber replays FFE_FLAGS from the inherited connector queue, which calls _notify_providers_config_received() once the worker's provider is registered). The fast path (config already available at initialize() time) is preserved — this covers the case where the master received config before forking. Update tests: replace TestProviderInitializationBlocking with TestProviderInitializationAsync verifying the new non-blocking contract. Remove the fast_initialization_timeout conftest fixture (no longer needed since initialize() does not block).

…arks + e2e tests - Read DD_FLAGGING_EVALUATION_COUNTS_ENABLED through OpenFeatureConfig (ddtrace config var system) instead of raw os.environ; register it in supported-configurations.json. - Stamp eval timestamp at the provider resolution boundary so the finally_after hook receives accurate eval-time. - Normalize typing annotations (dict[...] generics) and mypy overrides for the flagevaluation writer/hook. - Add openfeature_flagevaluation microbenchmark suite (hook-enqueue / aggregate / hook-plus-drain, incl. a 2500-flag high-cardinality variant) wired into benchmarks/suitespec.yml. - Add end-to-end provider tests covering hook firing on the real eval path, all exit paths (success/error/runtime-default/disabled), and OTel non-regression.

Strip internal validation-gate markers and review-tracking references from test docstrings and comments; keep the substantive test descriptions intact.

cit-pr-commenter-54b7da · 2026-06-14T12:23:22Z

Codeowners resolved as

benchmarks/openfeature_flagevaluation/config.yaml                       @DataDog/apm-core-python
benchmarks/openfeature_flagevaluation/requirements_scenario.txt         @DataDog/apm-core-python
benchmarks/openfeature_flagevaluation/scenario.py                       @DataDog/apm-core-python
ddtrace/internal/evp_proxy/constants.py                                 @DataDog/apm-core-python
ddtrace/internal/openfeature/_flag_eval_evp_hook.py                     @DataDog/feature-flagging-and-experimentation-sdk
ddtrace/internal/openfeature/_flageval_metrics.py                       @DataDog/feature-flagging-and-experimentation-sdk
ddtrace/internal/openfeature/_flagevaluation_writer.py                  @DataDog/feature-flagging-and-experimentation-sdk
ddtrace/internal/openfeature/_provider.py                               @DataDog/feature-flagging-and-experimentation-sdk
ddtrace/internal/openfeature/writer.py                                  @DataDog/feature-flagging-and-experimentation-sdk
ddtrace/internal/settings/_supported_configurations.py                  @DataDog/apm-sdk-capabilities-python @DataDog/apm-python
ddtrace/internal/settings/openfeature.py                                @DataDog/feature-flagging-and-experimentation-sdk
mypy.ini                                                                @DataDog/python-guild @DataDog/apm-core-python
pyproject.toml                                                          @DataDog/python-guild
releasenotes/notes/openfeature-flagevaluation-evp-0f1a2b3c4d5e6f70.yaml  @DataDog/apm-python
supported-configurations.json                                           @DataDog/apm-sdk-capabilities-python @DataDog/apm-python
tests/openfeature/conftest.py                                           @DataDog/feature-flagging-and-experimentation-sdk
tests/openfeature/test_flag_eval_evp_hook.py                            @DataDog/feature-flagging-and-experimentation-sdk
tests/openfeature/test_flag_eval_metrics.py                             @DataDog/feature-flagging-and-experimentation-sdk
tests/openfeature/test_flagevaluation_e2e.py                            @DataDog/feature-flagging-and-experimentation-sdk
tests/openfeature/test_flagevaluation_writer.py                         @DataDog/feature-flagging-and-experimentation-sdk
tests/openfeature/test_provider.py                                      @DataDog/feature-flagging-and-experimentation-sdk
tests/openfeature/test_provider_status.py                               @DataDog/feature-flagging-and-experimentation-sdk

pr-commenter · 2026-06-14T12:50:17Z

Benchmarks

Benchmark execution time: 2026-07-14 19:29:10

Comparing candidate commit fddd338 in PR branch leo.romanovsky/ffl-2446-evp-flagevaluation-python with baseline commit b713d21 in branch main.

Found 0 performance improvements and 5 performance regressions! Performance is the same for 614 metrics, 10 unstable metrics.

scenario:iastaspects-index_aspect

🟥 execution_time [+13.438µs; +16.718µs] or [+10.418%; +12.960%]

scenario:iastaspects-ljust_aspect

🟥 execution_time [+73.176µs; +82.530µs] or [+12.460%; +14.053%]

scenario:iastaspects-swapcase_aspect

🟥 execution_time [+51.512µs; +58.797µs] or [+14.820%; +16.916%]

scenario:iastaspectsospath-ospathbasename_aspect

🟥 execution_time [+97.407µs; +105.972µs] or [+23.049%; +25.076%]

scenario:span-start

🟥 execution_time [+1.165ms; +1.358ms] or [+7.386%; +8.608%]

…hema

…tions-cross-sdk

…46-evp-flagevaluation-python

cit-pr-commenter-54b7da · 2026-07-06T04:19:18Z

Circular import analysis

⚠️ Existing circular imports

There are 2524 circular imports that already exist on the base branch and have not been changed by this PR.

Show existing cycles (showing 5 of 2524 shortest)

ddtrace.internal.datastreams -> ddtrace.internal.datastreams.google_cloud_pubsub -> ddtrace.internal.datastreams
ddtrace.internal.core -> ddtrace._trace.span -> ddtrace.internal.core
ddtrace.internal.datastreams -> ddtrace.internal.datastreams.botocore -> ddtrace.internal.datastreams
ddtrace.internal.datastreams -> ddtrace.internal.datastreams.kombu -> ddtrace.internal.datastreams
ddtrace.internal.datastreams -> ddtrace.internal.datastreams.aiokafka -> ddtrace.internal.datastreams

To see all cycles, download the cycles-base.json and cycles-pr.json artifacts from this CI job and run:
uv run --script scripts/import-analysis/cycles.py compare cycles-base.json cycles-pr.json

brettlangdon

benchmarks and release notes lgtm

note: the benchmarks aren't being run in CI, if you want that you'll need to add SLOs for them and then added to the suitespec for benchmarks.

…46-evp-flagevaluation-python # Conflicts: # ddtrace/internal/openfeature/_provider.py

dd-octo-sts-150931 · 2026-07-14T19:30:19Z

PR Shepherd is now watching this PR: fixing basic CI failures, rebasing when it falls behind, and re-queueing after transient merge-queue failures. To disable it, add the pr-shepherd:ignore label.

If you have any questions, reach the team in #ai-devx-flow.

## Motivation Server-side feature-flag evaluations need to be observable in the same backend track as the rest of the cross-SDK rollout so APM can judge rollout smoothness and time-to-land from real evaluation counts, not only app-local behavior or metric-side approximations. This contribution gives Python services backend-visible `flagevaluation` telemetry with bounded cardinality and batching, so customers and internal dogfooding can verify which flags evaluated, for which targeting keys, and how often, while preserving the existing `feature_flag.evaluations` metric path. ## Changes - Adds a Python EVP `flagevaluation` writer behind `DD_FLAGGING_EVALUATION_COUNTS_ENABLED`; existing OTel `feature_flag.evaluations` metrics remain unchanged. - Registers an OpenFeature `finally_after` logging hook that captures a cheap evaluation snapshot and hands it to a background writer. - Renames the capture hook to `FlagEvalEVPHook` / `_flag_eval_evp_hook.py` so it is distinct from `FlagEvalMetricsHook`. - Posts batched payloads to the Agent EVP proxy route `/evp_proxy/v2/api/v2/flagevaluation` with `X-Datadog-EVP-Subdomain: event-platform-intake`. - Aggregates evaluations by schema-visible dimensions: flag key, variant key, allocation key, runtime-default state, error message, targeting key, and pruned evaluation context. - Flattens and prunes evaluation context before enqueue with deterministic 256-field / 256-character bounds. - Splits encoded `flagEvaluations` request bodies under the shared EVP payload limit before POSTing. - Degrades a single oversized full-tier row by dropping `targeting_key` and `context`; if the degraded row still cannot fit, the writer drops and logs it. - Uses evaluation-time metadata to populate `first_evaluation` and `last_evaluation` rather than flush time. - Keeps accepting the legacy `DataDogProvider(initialization_timeout=...)` keyword for compatibility, while async initialization no longer uses it. ### Evaluation Capture And Flush ```mermaid flowchart TD Eval["OpenFeature evaluation"] --> Metrics["FlagEvalMetricsHook\nexisting OTel metric"] Eval --> Logging["FlagEvalEVPHook\nfinally_after"] Logging --> Snapshot["Cheap scalar snapshot\nflag, variant, allocation,\ntargeting_key, context, eval time"] Snapshot --> Queue["Bounded non-blocking queue\nQueue size 4096"] Queue --> Drain["Background drain worker\n0.1s queue drain"] Drain --> Aggregate["FlagEvaluationWriter aggregation\nfull tier then degraded tier"] Aggregate --> Flush["Periodic flush\n10s interval"] Flush --> EVP["Agent EVP proxy\n/evp_proxy/v2/api/v2/flagevaluation"] ``` ### Payload Limit Handling ```mermaid flowchart TD Events["Aggregated flagEvaluations"] --> Encode["Encode each event with compact JSON"] Encode --> FitsEvent{"Single event fits\ninside shared EVP limit?"} FitsEvent -- "yes" --> Batch["Add to current payload"] FitsEvent -- "no" --> Degrade["Drop targeting_key and context\nfor that event only"] Degrade --> FitsDegraded{"Degraded event fits?"} FitsDegraded -- "yes" --> Batch FitsDegraded -- "no" --> Drop["Drop and count\npayload_limit"] Batch --> FitsPayload{"Payload still <=\nDEFAULT_EVP_PAYLOAD_SIZE_LIMIT?"} FitsPayload -- "yes" --> Continue["Continue batching"] FitsPayload -- "no" --> Split["Close current payload\nstart next payload"] Split --> Continue Continue --> Post["POST each payload sequentially"] ``` ## Decisions - Keep this as a dual-path implementation: the EVP logging writer is additive and separately gated, while the existing OTel metric hook remains registered when the provider is enabled. - Use the existing Agent EVP proxy transport and the canonical `flagevaluation` backend route instead of adding a Python-specific transport or schema. - Keep the evaluation logging hook non-blocking on application evaluation threads: queueing is best-effort, bounded, and drop-counted under backpressure. - Do aggregation, JSON serialization, payload splitting, and POSTs only in the background writer path. - Split `flagEvaluations` payloads by encoded byte size using `DEFAULT_EVP_PAYLOAD_SIZE_LIMIT`; send split payloads sequentially from the writer thread. - Use a two-tier aggregation model: full buckets retain `targeting_key` and pruned context; degraded buckets retain only lower-cardinality schema dimensions. - Store `targeting_key` in the dedicated EVP field and remove `targetingKey` / `targeting_key` from context before building context keys or payloads. - Do not emit OpenFeature `reason` because it is not accepted by the canonical worker schema, and do not populate `targeting_rule.key` until Python exposes real targeting-rule metadata. - Keep `openfeature-sdk` as an application/test/lint dependency rather than adding it as a runtime dependency of `ddtrace`; the dogfooding app dependency floor is being tightened separately in https://github.com/DataDog/ffe-dogfooding/pull/88. ## Validation Evidence ### Dogfooding App - `ffe-dogfooding` `app-python` was run with local `dd-trace-py` artifacts and reached `PROVIDER_READY`. - The running dogfooding app reported `openfeature-sdk=0.9.0`. - Evaluated `ffe-dogfooding-string-flag` through the Python dogfooding app with 5 evaluations per targeting key over one persistent connection: - `python-batch-keepalive-20260623T004034Z-alpha` - `python-batch-keepalive-20260623T004034Z-bravo` - `python-batch-keepalive-20260623T004034Z-charlie` - App-side result: all evaluations returned `variant_2` with allocation `allocation-override-392dd7c149f8`. ### System Tests - Companion draft PR: DataDog/system-tests#7183 ### Staging End-To-End - Dogfooding ran without the local mock-intake EVP tee/proxy, so the Agent sent EVP traffic through the normal backend path. - Retriever staging query returned 3 backend `flagevaluation` rows for the exact targeting keys above. - Each targeting key returned one row with `evaluation_count=5`: - `python-batch-keepalive-20260623T004034Z-alpha` - `python-batch-keepalive-20260623T004034Z-bravo` - `python-batch-keepalive-20260623T004034Z-charlie` - All rows had `flag.key=ffe-dogfooding-string-flag`, `variant.key=variant_2`, and `allocation.key=allocation-override-392dd7c149f8`. Co-authored-by: leo.romanovsky <leo.romanovsky@datadoghq.com>

leoromanovsky added 8 commits June 12, 2026 15:41

Revert "fix(openfeature): degrade gracefully on RC init timeout inste…

1c088ac

…ad of PROVIDER_ERROR" This reverts commit 114216b.

chore(openfeature): remove internal planning annotations

00b8a45

Strip internal validation-gate markers and review-tracking references from test docstrings and comments; keep the substantive test descriptions intact.

chore(openfeature): complete flagevaluation benchmark scenario wiring

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leoromanovsky added 9 commits June 15, 2026 11:05

docs(openfeature): add release note for server-side EVP flagevaluation

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fix(openfeature): align EVP flagevaluation aggregation with worker sc…

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…hema

chore(openfeature): add riot lockfiles for flagevaluation tests

1821064

chore(openfeature): prune stale riot lockfiles

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fix(openfeature): avoid suitespec component collision

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fix(openfeature): drain flag evaluation queue before flush

e54af18

Configure Python flag evaluation flush interval

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Merge remote-tracking branch 'origin/main' into workspace/flag-evalua…

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Align flagevaluation EVP contract

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leoromanovsky added 3 commits June 19, 2026 21:19

fix(openfeature): sort flagevaluation writer imports

1b32062

fix(openfeature): keep flagevaluation flush interval internal

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Align flagevaluation EVP metadata

8539e96

leoromanovsky marked this pull request as ready for review June 23, 2026 00:38

leoromanovsky requested review from a team as code owners June 23, 2026 00:38

leoromanovsky added 3 commits June 23, 2026 19:57

refactor(openfeature): update flag eval EVP benchmark

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Merge remote-tracking branch 'origin/main' into leo.romanovsky/ffl-24…

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chore(openfeature): drop candidate-only flageval benchmark

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KowalskiThomas approved these changes Jun 24, 2026

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fix(openfeature): serialize flagevaluation context values

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chore(openfeature): keep shutdown typing local

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fix(openfeature): avoid bandit pass fallback

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Add OpenFeature flagevaluation benchmarks

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Merge remote-tracking branch 'origin/main' into leo.romanovsky/ffl-24…

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chore(openfeature): address flagevaluation review cleanup

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fix(openfeature): harden flagevaluation context handling

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test(openfeature): cover sdk ready before config

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…46-evp-flagevaluation-python # Conflicts: # ddtrace/internal/openfeature/_provider.py

perf(openfeature): isolate enqueue benchmark

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feat(openfeature): add agentless feature flag configuration source #19331

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feat(openfeature): parse observeFullEvaluationData and hash targeting_key in flagevaluations events #19554

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feat(openfeature): emit server-side EVP flagevaluation - #18613

feat(openfeature): emit server-side EVP flagevaluation#18613
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Motivation

Changes

Evaluation Capture And Flush

Payload Limit Handling

Decisions

Validation Evidence

Dogfooding App

System Tests

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Benchmarks

scenario:iastaspects-index_aspect

scenario:iastaspects-ljust_aspect

scenario:iastaspects-swapcase_aspect

scenario:iastaspectsospath-ospathbasename_aspect

scenario:span-start

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Circular import analysis

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cit-pr-commenter-54b7da Bot commented Jun 14, 2026 •

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pr-commenter Bot commented Jun 14, 2026 •

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cit-pr-commenter-54b7da Bot commented Jul 6, 2026 •

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