dd-trace-py feat(vllm): add vLLM integration

Description

MLOB-4847

This PR adds Datadog tracing integration for vLLM V1 engine exclusively. V0 is deprecated and being removed (vLLM Q3 2025 Roadmap), so we're building for the future.

Request Flow and Instrumentation Points

The integration traces at the engine level rather than wrapping high-level APIs. This gives us a single integration point for all operations (completion, chat, embedding, classification) with complete access to internal metadata.

1. Engine Initialization (once per engine)

User creates vllm.LLM() / AsyncLLM()
    ↓
LLMEngine.__init__() / AsyncLLM.__init__()
    → WRAPPED: traced_engine_init()
        • Forces log_stats=True (needed for tokens/latency metrics)
        • Captures model name from engine.model_config.model
        • Injects into output_processor._dd_model_name

2. Request Submission (per request)

User calls llm.generate() / llm.chat() / llm.embed()
    ↓
Processor.process_inputs(trace_headers=...)
    → WRAPPED: traced_processor_process_inputs()
        • Extracts active Datadog trace context
        • Injects headers into trace_headers dict
        • Propagates through engine automatically

3. Output Processing (when request finishes)

Engine completes → OutputProcessor.process_outputs()
    → WRAPPED: traced_output_processor_process_outputs()
        • BEFORE calling original:
            - Capture req_state data (prompt, params, stats, trace_headers)
        • Call original (removes req_state from memory)
        • AFTER original returns:
            - Create span with parent context from trace_headers
            - Tag with LLMObs metadata (model, tokens, params)
            - Set latency metrics (queue, prefill, decode, TTFT)
            - Finish span

The key insight: OutputProcessor.process_outputs has everything in one place: request metadata, output data, and parent context. We wrap three specific points because each serves a distinct purpose: __init__ for setup, process_inputs for context injection, process_outputs for span creation.

Version Support

Requires vLLM >= 0.10.2 for V1 support. Version 0.10.2 includes vLLM PR #20372 which added trace_headers for context propagation.

No V0 support. It's deprecated and being removed. The integration includes a version check that gracefully skips instrumentation on older versions with a warning.

Metadata Captured

Request: prompt, input tokens, sampling params (temperature, top_p, max_tokens, etc.)
Response: output text, output tokens, finish reason, cached tokens
Latency metrics: TTFT, queue time, prefill, decode, inference (mirrors vLLM's OpenTelemetry do_tracing)
Model: name, provider, LoRA adapter (if used)
Embeddings: dimension, count

For chat requests where vLLM only stores token IDs, we decode back to text using the tokenizer to ensure input_messages are captured correctly.

Chat Template Parsing

For chat completions, vLLM applies Jinja2 templates to format messages. We parse the formatted prompt back into structured input_messages for LLMObs.

Supported formats: Llama 3/4, ChatML/Qwen, Phi, DeepSeek, Gemma, Granite, MiniMax, TeleFLM, Inkbot, Alpaca, Falcon. Chosen because they're visible as examples in vLLM repos. Fallback: raw prompt.

Parser uses quick marker detection before regex patterns, avoiding unnecessary regex execution. Prompts decoded with skip_special_tokens=False to preserve chat template markers (vLLM defaults strip them).

Not perfect, but simple enough that adding new templates isn't painful.

FastAPI Pickle Fix for Ray Serve Compatibility

Problem

vLLM's distributed inference (via Ray Serve) serializes FastAPI app components using pickle. When dd-trace-py instruments FastAPI with wrapt.FunctionWrapper, these wrapped objects become unpicklable because wrapt doesn't implement __reduce_ex__() by default.

Solution

We register custom pickle reducers for wrapt proxy types in fastapi/patch.py:

During pickle: _reduce_wrapt_proxy() unwraps the object
During unpickle: _identity() returns the unwrapped object
Result: Instrumentation is stripped across pickle boundaries

This is acceptable because distributed vLLM workers independently instrument their FastAPI instances when dd-trace-py is imported. The registration is guarded by _WRAPT_REDUCERS_REGISTERED flag (only runs once globally).

Why This Works

Ray Serve's @serve.ingress(app) decorator pickles the FastAPI app
cloudpickle encounters wrapt.FunctionWrapper objects (ddtrace wrappers)
wrapt raises NotImplementedError for __reduce_ex__()
copyreg intercepts via dispatch table and uses our reducer
Reducer returns unwrapped function → pickle succeeds
On Ray worker, ddtrace re-patches when imported → tracing works

Reproducer

Without the fix, this crashes with ddtrace-run:

#!/usr/bin/env python3
"""Minimal reproducer for Ray Serve + ddtrace serialization failure."""

from fastapi import FastAPI
from ray import serve


def main():
    app = FastAPI()

    @app.get("/v1/models")
    def list_models():
        return {"data": [{"id": "dummy"}]}

    print("Applying @serve.ingress(app), which triggers pickle internally…")

    @serve.ingress(app)
    class Ingress:
        pass

    print("Pickle succeeded!")
    return Ingress


if __name__ == "__main__":
    main()

Run with ddtrace-run python repro.py → crashes without fix, works with fix.

Testing

Tests run on GPU hardware using gpu:a10-amd64 runner tag in GitLab CI (GPU Runners docs). Cannot be run locally on Macs. Requires actual GPU hardware. During dev, I used a g6.8xlarge EC2 instance.

Coverage:

Unit tests validate LLMObs events for all operations: completion, chat, embedding, classification, scoring, rewards
Integration test validates RAG scenario with parent-child spans and context propagation across async engines

Tests converge on same instrumentation points (as shown in request flow), so current coverage should be solid for first release.

Infrastructure notes:

Runners take ~5-10 minutes to start on CI (slow iterations)
Module-scoped fixtures cache LLM instances to reduce test time
Kubernetes memory increased to 12 Gi to handle caching pressure
Tests run in ~1 min on EC2 instance

Risks

V1 maturity: V1 is production-ready but still evolving toward vLLM 1.0. Our instrumentation points (process_inputs, process_outputs) are core to V1's design and unlikely to change significantly.

No V0 support: Customers on V0 won't get tracing. However, V0 is deprecated and most production deployments have migrated (V0 doesn't support pooling models anymore).

Version requirement: Requiring 0.10.2+ may exclude some users, but trace header propagation is essential to a maintainable design.

High span burst in RAG scenarios: RAG apps indexing large document collections generate significant span volumes (e.g., 1000 docs = 1000 embedding spans). This is expected behavior but may impact trace readability and ingestion costs. Could add DD_VLLM_TRACE_EMBEDDINGS=false config later if needed, but let's monitor customer feedback first rather than over-engineer.

Additional Notes

Main Files

patch.py: Wraps vLLM engine methods
extractors.py: Extracts request/response data from vLLM structures
utils.py: Span creation, context injection, metrics utilities
llmobs/_integrations/vllm.py: LLMObs-specific tagging and event building

Sep 30 '25 12:09 PROFeNoM

CODEOWNERS have been resolved as:

.riot/requirements/12263ee.txt                                          @DataDog/apm-python
.riot/requirements/122cffd.txt                                          @DataDog/apm-python
.riot/requirements/12ee49d.txt                                          @DataDog/apm-python
.riot/requirements/1317b0e.txt                                          @DataDog/apm-python
.riot/requirements/162f3ce.txt                                          @DataDog/apm-python
.riot/requirements/1c5afd9.txt                                          @DataDog/apm-python
.riot/requirements/1ce3960.txt                                          @DataDog/apm-python
.riot/requirements/c663307.txt                                          @DataDog/apm-python
ddtrace/contrib/internal/vllm/__init__.py                               @DataDog/ml-observability
ddtrace/contrib/internal/vllm/_constants.py                             @DataDog/ml-observability
ddtrace/contrib/internal/vllm/extractors.py                             @DataDog/ml-observability
ddtrace/contrib/internal/vllm/patch.py                                  @DataDog/ml-observability
ddtrace/contrib/internal/vllm/utils.py                                  @DataDog/ml-observability
ddtrace/llmobs/_integrations/vllm.py                                    @DataDog/ml-observability
docker-compose.gpu.yml                                                  @DataDog/apm-core-python
releasenotes/notes/add-vllm-integration-b93a517daeb45f61.yaml           @DataDog/apm-python
tests/contrib/vllm/__init__.py                                          @DataDog/ml-observability
tests/contrib/vllm/_utils.py                                            @DataDog/ml-observability
tests/contrib/vllm/api_app.py                                           @DataDog/ml-observability
tests/contrib/vllm/conftest.py                                          @DataDog/ml-observability
tests/contrib/vllm/test_api_app.py                                      @DataDog/ml-observability
tests/contrib/vllm/test_extractors.py                                   @DataDog/ml-observability
tests/contrib/vllm/test_vllm_llmobs.py                                  @DataDog/ml-observability
tests/snapshots/tests.contrib.vllm.test_api_app.test_rag_parent_child.json  @DataDog/ml-observability
tests/snapshots/tests.contrib.vllm.test_vllm_llmobs.test_llmobs_basic.json  @DataDog/ml-observability
tests/snapshots/tests.contrib.vllm.test_vllm_llmobs.test_llmobs_chat.json  @DataDog/ml-observability
tests/snapshots/tests.contrib.vllm.test_vllm_llmobs.test_llmobs_classify.json  @DataDog/ml-observability
tests/snapshots/tests.contrib.vllm.test_vllm_llmobs.test_llmobs_embed.json  @DataDog/ml-observability
tests/snapshots/tests.contrib.vllm.test_vllm_llmobs.test_llmobs_reward.json  @DataDog/ml-observability
tests/snapshots/tests.contrib.vllm.test_vllm_llmobs.test_llmobs_score.json  @DataDog/ml-observability
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ddtrace/_monkey.py                                                      @DataDog/apm-core-python
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ddtrace/llmobs/_constants.py                                            @DataDog/ml-observability
ddtrace/llmobs/_integrations/base.py                                    @DataDog/ml-observability
docs/integrations.rst                                                   @DataDog/python-guild
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tests/contrib/fastapi/test_fastapi.py                                   @DataDog/apm-core-python @DataDog/apm-idm-python
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.riot/requirements/f781048.txt                                          @DataDog/apm-python

Sep 30 '25 12:09 github-actions[bot]

Bootstrap import analysis

Comparison of import times between this PR and base.

Summary

The average import time from this PR is: 249 ± 2 ms.

The average import time from base is: 251 ± 2 ms.

The import time difference between this PR and base is: -2.0 ± 0.1 ms.

Import time breakdown

The following import paths have shrunk:

ddtrace.auto 2.643 ms (1.06%)

ddtrace 1.353 ms (0.54%)

ddtrace._logger 0.674 ms (0.27%)

ddtrace.internal.telemetry 0.674 ms (0.27%)

ddtrace.internal.telemetry.writer 0.674 ms (0.27%)

ddtrace.internal.utils.version 0.674 ms (0.27%)

ddtrace.version 0.674 ms (0.27%)

ddtrace.internal._unpatched 0.028 ms (0.01%)

json 0.028 ms (0.01%)

json.decoder 0.028 ms (0.01%)

re 0.028 ms (0.01%)

enum 0.028 ms (0.01%)

types 0.028 ms (0.01%)

ddtrace.bootstrap.sitecustomize 1.290 ms (0.52%)

ddtrace.bootstrap.preload 1.290 ms (0.52%)

ddtrace.internal.remoteconfig.client 0.619 ms (0.25%)

Sep 30 '25 12:09 github-actions[bot]

Performance SLOs

Comparing candidate alex/feat/vllm (e6051c73) with baseline main (c6edb37e)

📈 Performance Regressions (3 suites)

📈 iastaspects - 118/118

✅ add_aspect

Time: ✅ 17.929µs (SLO: <20.000µs 📉 -10.4%) vs baseline: 📈 +20.9%

Memory: ✅ 42.566MB (SLO: <43.250MB 🟡 -1.6%) vs baseline: +4.0%

✅ add_inplace_aspect

Time: ✅ 14.971µs (SLO: <20.000µs 📉 -25.1%) vs baseline: -0.2%

Memory: ✅ 42.684MB (SLO: <43.250MB 🟡 -1.3%) vs baseline: +4.0%

✅ add_inplace_noaspect

Time: ✅ 0.337µs (SLO: <10.000µs 📉 -96.6%) vs baseline: -0.4%

Memory: ✅ 42.723MB (SLO: <43.500MB 🟡 -1.8%) vs baseline: +4.9%

✅ add_noaspect

Time: ✅ 0.542µs (SLO: <10.000µs 📉 -94.6%) vs baseline: -0.7%

Memory: ✅ 42.782MB (SLO: <43.500MB 🟡 -1.7%) vs baseline: +5.1%

✅ bytearray_aspect

Time: ✅ 17.903µs (SLO: <30.000µs 📉 -40.3%) vs baseline: ~same

Memory: ✅ 42.625MB (SLO: <43.500MB -2.0%) vs baseline: +4.7%

✅ bytearray_extend_aspect

Time: ✅ 23.921µs (SLO: <30.000µs 📉 -20.3%) vs baseline: +0.6%

Memory: ✅ 42.605MB (SLO: <43.500MB -2.1%) vs baseline: +3.9%

✅ bytearray_extend_noaspect

Time: ✅ 2.737µs (SLO: <10.000µs 📉 -72.6%) vs baseline: -0.2%

Memory: ✅ 42.644MB (SLO: <43.500MB 🟡 -2.0%) vs baseline: +4.6%

✅ bytearray_noaspect

Time: ✅ 1.483µs (SLO: <10.000µs 📉 -85.2%) vs baseline: +0.3%

Memory: ✅ 42.605MB (SLO: <43.500MB -2.1%) vs baseline: +4.5%

✅ bytes_aspect

Time: ✅ 16.593µs (SLO: <20.000µs 📉 -17.0%) vs baseline: -0.5%

Memory: ✅ 42.625MB (SLO: <43.500MB -2.0%) vs baseline: +4.3%

✅ bytes_noaspect

Time: ✅ 1.404µs (SLO: <10.000µs 📉 -86.0%) vs baseline: -1.7%

Memory: ✅ 42.664MB (SLO: <43.500MB 🟡 -1.9%) vs baseline: +4.8%

✅ bytesio_aspect

Time: ✅ 55.236µs (SLO: <70.000µs 📉 -21.1%) vs baseline: -0.9%

Memory: ✅ 42.526MB (SLO: <43.500MB -2.2%) vs baseline: +4.5%

✅ bytesio_noaspect

Time: ✅ 3.244µs (SLO: <10.000µs 📉 -67.6%) vs baseline: -0.3%

Memory: ✅ 42.546MB (SLO: <43.500MB -2.2%) vs baseline: +4.4%

✅ capitalize_aspect

Time: ✅ 14.701µs (SLO: <20.000µs 📉 -26.5%) vs baseline: -0.2%

Memory: ✅ 42.605MB (SLO: <43.500MB -2.1%) vs baseline: +3.8%

✅ capitalize_noaspect

Time: ✅ 2.595µs (SLO: <10.000µs 📉 -74.0%) vs baseline: -0.2%

Memory: ✅ 42.644MB (SLO: <43.500MB 🟡 -2.0%) vs baseline: +4.8%

✅ casefold_aspect

Time: ✅ 14.622µs (SLO: <20.000µs 📉 -26.9%) vs baseline: -0.5%

Memory: ✅ 42.762MB (SLO: <43.500MB 🟡 -1.7%) vs baseline: +5.2%

✅ casefold_noaspect

Time: ✅ 3.180µs (SLO: <10.000µs 📉 -68.2%) vs baseline: +0.9%

Memory: ✅ 42.743MB (SLO: <43.500MB 🟡 -1.7%) vs baseline: +4.9%

✅ decode_aspect

Time: ✅ 15.530µs (SLO: <30.000µs 📉 -48.2%) vs baseline: -0.6%

Memory: ✅ 42.625MB (SLO: <43.500MB -2.0%) vs baseline: +4.5%

✅ decode_noaspect

Time: ✅ 1.601µs (SLO: <10.000µs 📉 -84.0%) vs baseline: +0.3%

Memory: ✅ 42.703MB (SLO: <43.500MB 🟡 -1.8%) vs baseline: +5.0%

✅ encode_aspect

Time: ✅ 18.182µs (SLO: <30.000µs 📉 -39.4%) vs baseline: 📈 +21.8%

Memory: ✅ 42.585MB (SLO: <43.500MB -2.1%) vs baseline: +4.3%

✅ encode_noaspect

Time: ✅ 1.495µs (SLO: <10.000µs 📉 -85.1%) vs baseline: ~same

Memory: ✅ 42.585MB (SLO: <43.500MB -2.1%) vs baseline: +4.8%

✅ format_aspect

Time: ✅ 171.293µs (SLO: <200.000µs 📉 -14.4%) vs baseline: +0.2%

Memory: ✅ 42.841MB (SLO: <43.250MB 🟡 -0.9%) vs baseline: +4.4%

✅ format_map_aspect

Time: ✅ 191.033µs (SLO: <200.000µs -4.5%) vs baseline: ~same

Memory: ✅ 42.762MB (SLO: <43.500MB 🟡 -1.7%) vs baseline: +3.9%

✅ format_map_noaspect

Time: ✅ 3.775µs (SLO: <10.000µs 📉 -62.3%) vs baseline: -0.8%

Memory: ✅ 42.585MB (SLO: <43.250MB 🟡 -1.5%) vs baseline: +4.5%

✅ format_noaspect

Time: ✅ 3.159µs (SLO: <10.000µs 📉 -68.4%) vs baseline: +0.4%

Memory: ✅ 42.762MB (SLO: <43.250MB 🟡 -1.1%) vs baseline: +5.0%

✅ index_aspect

Time: ✅ 15.318µs (SLO: <20.000µs 📉 -23.4%) vs baseline: ~same

Memory: ✅ 42.762MB (SLO: <43.250MB 🟡 -1.1%) vs baseline: +4.6%

✅ index_noaspect

Time: ✅ 0.463µs (SLO: <10.000µs 📉 -95.4%) vs baseline: -0.2%

Memory: ✅ 42.762MB (SLO: <43.500MB 🟡 -1.7%) vs baseline: +5.0%

✅ join_aspect

Time: ✅ 16.980µs (SLO: <20.000µs 📉 -15.1%) vs baseline: -0.1%

Memory: ✅ 42.566MB (SLO: <43.500MB -2.1%) vs baseline: +4.2%

✅ join_noaspect

Time: ✅ 1.555µs (SLO: <10.000µs 📉 -84.5%) vs baseline: +0.4%

Memory: ✅ 42.762MB (SLO: <43.250MB 🟡 -1.1%) vs baseline: +5.1%

✅ ljust_aspect

Time: ✅ 20.882µs (SLO: <30.000µs 📉 -30.4%) vs baseline: +0.2%

Memory: ✅ 42.684MB (SLO: <43.250MB 🟡 -1.3%) vs baseline: +4.4%

✅ ljust_noaspect

Time: ✅ 2.712µs (SLO: <10.000µs 📉 -72.9%) vs baseline: +0.2%

Memory: ✅ 42.644MB (SLO: <43.250MB 🟡 -1.4%) vs baseline: +4.9%

✅ lower_aspect

Time: ✅ 17.879µs (SLO: <30.000µs 📉 -40.4%) vs baseline: -0.8%

Memory: ✅ 42.841MB (SLO: <43.500MB 🟡 -1.5%) vs baseline: +4.8%

✅ lower_noaspect

Time: ✅ 2.411µs (SLO: <10.000µs 📉 -75.9%) vs baseline: -1.4%

Memory: ✅ 42.644MB (SLO: <43.250MB 🟡 -1.4%) vs baseline: +4.6%

✅ lstrip_aspect

Time: ✅ 17.576µs (SLO: <20.000µs 📉 -12.1%) vs baseline: -0.2%

Memory: ✅ 42.703MB (SLO: <43.250MB 🟡 -1.3%) vs baseline: +4.1%

✅ lstrip_noaspect

Time: ✅ 1.874µs (SLO: <10.000µs 📉 -81.3%) vs baseline: ~same

Memory: ✅ 42.526MB (SLO: <43.500MB -2.2%) vs baseline: +4.8%

✅ modulo_aspect

Time: ✅ 166.680µs (SLO: <200.000µs 📉 -16.7%) vs baseline: +0.2%

Memory: ✅ 42.900MB (SLO: <43.500MB 🟡 -1.4%) vs baseline: +4.2%

✅ modulo_aspect_for_bytearray_bytearray

Time: ✅ 179.954µs (SLO: <200.000µs 📉 -10.0%) vs baseline: +2.8%

Memory: ✅ 42.782MB (SLO: <43.500MB 🟡 -1.7%) vs baseline: +3.7%

✅ modulo_aspect_for_bytes

Time: ✅ 169.024µs (SLO: <200.000µs 📉 -15.5%) vs baseline: +0.2%

Memory: ✅ 42.880MB (SLO: <43.500MB 🟡 -1.4%) vs baseline: +4.8%

✅ modulo_aspect_for_bytes_bytearray

Time: ✅ 172.232µs (SLO: <200.000µs 📉 -13.9%) vs baseline: +0.1%

Memory: ✅ 42.821MB (SLO: <43.500MB 🟡 -1.6%) vs baseline: +3.9%

✅ modulo_noaspect

Time: ✅ 3.663µs (SLO: <10.000µs 📉 -63.4%) vs baseline: +0.5%

Memory: ✅ 42.782MB (SLO: <43.500MB 🟡 -1.7%) vs baseline: +5.4%

✅ replace_aspect

Time: ✅ 211.626µs (SLO: <300.000µs 📉 -29.5%) vs baseline: -0.2%

Memory: ✅ 42.762MB (SLO: <44.000MB -2.8%) vs baseline: +4.6%

✅ replace_noaspect

Time: ✅ 2.905µs (SLO: <10.000µs 📉 -70.9%) vs baseline: -0.5%

Memory: ✅ 42.684MB (SLO: <43.500MB 🟡 -1.9%) vs baseline: +4.6%

✅ repr_aspect

Time: ✅ 1.415µs (SLO: <10.000µs 📉 -85.8%) vs baseline: +0.1%

Memory: ✅ 42.703MB (SLO: <43.500MB 🟡 -1.8%) vs baseline: +4.6%

✅ repr_noaspect

Time: ✅ 0.524µs (SLO: <10.000µs 📉 -94.8%) vs baseline: +0.4%

Memory: ✅ 42.703MB (SLO: <43.500MB 🟡 -1.8%) vs baseline: +4.7%

✅ rstrip_aspect

Time: ✅ 18.970µs (SLO: <30.000µs 📉 -36.8%) vs baseline: ~same

Memory: ✅ 42.605MB (SLO: <43.500MB -2.1%) vs baseline: +4.1%

✅ rstrip_noaspect

Time: ✅ 2.017µs (SLO: <10.000µs 📉 -79.8%) vs baseline: +4.6%

Memory: ✅ 42.723MB (SLO: <43.500MB 🟡 -1.8%) vs baseline: +5.0%

✅ slice_aspect

Time: ✅ 15.945µs (SLO: <20.000µs 📉 -20.3%) vs baseline: +0.2%

Memory: ✅ 42.585MB (SLO: <43.500MB -2.1%) vs baseline: +4.6%

✅ slice_noaspect

Time: ✅ 0.600µs (SLO: <10.000µs 📉 -94.0%) vs baseline: +0.6%

Memory: ✅ 42.684MB (SLO: <43.500MB 🟡 -1.9%) vs baseline: +5.0%

✅ stringio_aspect

Time: ✅ 54.378µs (SLO: <80.000µs 📉 -32.0%) vs baseline: -0.3%

Memory: ✅ 42.625MB (SLO: <43.500MB -2.0%) vs baseline: +4.7%

✅ stringio_noaspect

Time: ✅ 3.591µs (SLO: <10.000µs 📉 -64.1%) vs baseline: -1.7%

Memory: ✅ 42.625MB (SLO: <43.500MB -2.0%) vs baseline: +5.1%

✅ strip_aspect

Time: ✅ 17.623µs (SLO: <20.000µs 📉 -11.9%) vs baseline: +0.7%

Memory: ✅ 42.644MB (SLO: <43.500MB 🟡 -2.0%) vs baseline: +4.1%

✅ strip_noaspect

Time: ✅ 1.860µs (SLO: <10.000µs 📉 -81.4%) vs baseline: -1.1%

Memory: ✅ 42.723MB (SLO: <43.500MB 🟡 -1.8%) vs baseline: +4.8%

✅ swapcase_aspect

Time: ✅ 18.412µs (SLO: <30.000µs 📉 -38.6%) vs baseline: -0.4%

Memory: ✅ 42.782MB (SLO: <43.500MB 🟡 -1.7%) vs baseline: +5.1%

✅ swapcase_noaspect

Time: ✅ 2.800µs (SLO: <10.000µs 📉 -72.0%) vs baseline: -0.7%

Memory: ✅ 42.585MB (SLO: <43.500MB -2.1%) vs baseline: +4.7%

✅ title_aspect

Time: ✅ 18.259µs (SLO: <20.000µs -8.7%) vs baseline: -0.2%

Memory: ✅ 42.841MB (SLO: <43.000MB 🟡 -0.4%) vs baseline: +4.7%

✅ title_noaspect

Time: ✅ 2.690µs (SLO: <10.000µs 📉 -73.1%) vs baseline: +0.7%

Memory: ✅ 42.841MB (SLO: <43.500MB 🟡 -1.5%) vs baseline: +5.2%

✅ translate_aspect

Time: ✅ 24.355µs (SLO: <30.000µs 📉 -18.8%) vs baseline: 📈 +18.5%

Memory: ✅ 42.625MB (SLO: <43.500MB -2.0%) vs baseline: +4.7%

✅ translate_noaspect

Time: ✅ 4.322µs (SLO: <10.000µs 📉 -56.8%) vs baseline: ~same

Memory: ✅ 42.684MB (SLO: <43.500MB 🟡 -1.9%) vs baseline: +4.7%

✅ upper_aspect

Time: ✅ 17.887µs (SLO: <30.000µs 📉 -40.4%) vs baseline: -0.9%

Memory: ✅ 42.684MB (SLO: <43.500MB 🟡 -1.9%) vs baseline: +4.1%

✅ upper_noaspect

Time: ✅ 2.422µs (SLO: <10.000µs 📉 -75.8%) vs baseline: -0.7%

Memory: ✅ 42.644MB (SLO: <43.500MB 🟡 -2.0%) vs baseline: +4.9%

📈 iastaspectsospath - 24/24

✅ ospathbasename_aspect

Time: ✅ 5.222µs (SLO: <10.000µs 📉 -47.8%) vs baseline: 📈 +22.6%

Memory: ✅ 41.465MB (SLO: <43.500MB -4.7%) vs baseline: +5.1%

✅ ospathbasename_noaspect

Time: ✅ 4.277µs (SLO: <10.000µs 📉 -57.2%) vs baseline: -1.1%

Memory: ✅ 41.425MB (SLO: <43.500MB -4.8%) vs baseline: +5.1%

✅ ospathjoin_aspect

Time: ✅ 6.212µs (SLO: <10.000µs 📉 -37.9%) vs baseline: -0.2%

Memory: ✅ 41.445MB (SLO: <43.500MB -4.7%) vs baseline: +5.0%

✅ ospathjoin_noaspect

Time: ✅ 6.291µs (SLO: <10.000µs 📉 -37.1%) vs baseline: -0.1%

Memory: ✅ 41.445MB (SLO: <43.500MB -4.7%) vs baseline: +4.9%

✅ ospathnormcase_aspect

Time: ✅ 3.579µs (SLO: <10.000µs 📉 -64.2%) vs baseline: +0.2%

Memory: ✅ 41.406MB (SLO: <43.500MB -4.8%) vs baseline: +4.8%

✅ ospathnormcase_noaspect

Time: ✅ 3.635µs (SLO: <10.000µs 📉 -63.7%) vs baseline: ~same

Memory: ✅ 41.406MB (SLO: <43.500MB -4.8%) vs baseline: +4.9%

✅ ospathsplit_aspect

Time: ✅ 4.876µs (SLO: <10.000µs 📉 -51.2%) vs baseline: -0.9%

Memory: ✅ 41.445MB (SLO: <43.500MB -4.7%) vs baseline: +4.8%

✅ ospathsplit_noaspect

Time: ✅ 5.013µs (SLO: <10.000µs 📉 -49.9%) vs baseline: +1.1%

Memory: ✅ 41.406MB (SLO: <43.500MB -4.8%) vs baseline: +5.0%

✅ ospathsplitdrive_aspect

Time: ✅ 3.756µs (SLO: <10.000µs 📉 -62.4%) vs baseline: -0.3%

Memory: ✅ 41.504MB (SLO: <43.500MB -4.6%) vs baseline: +5.2%

✅ ospathsplitdrive_noaspect

Time: ✅ 0.745µs (SLO: <10.000µs 📉 -92.6%) vs baseline: -0.6%

Memory: ✅ 41.484MB (SLO: <43.500MB -4.6%) vs baseline: +5.1%

✅ ospathsplitext_aspect

Time: ✅ 4.638µs (SLO: <10.000µs 📉 -53.6%) vs baseline: +0.4%

Memory: ✅ 41.366MB (SLO: <43.500MB -4.9%) vs baseline: +4.6%

✅ ospathsplitext_noaspect

Time: ✅ 4.622µs (SLO: <10.000µs 📉 -53.8%) vs baseline: -1.0%

Memory: ✅ 41.347MB (SLO: <43.500MB -5.0%) vs baseline: +4.8%

📈 telemetryaddmetric - 30/30

✅ 1-count-metric-1-times

Time: ✅ 3.385µs (SLO: <20.000µs 📉 -83.1%) vs baseline: 📈 +13.4%

Memory: ✅ 34.937MB (SLO: <35.500MB 🟡 -1.6%) vs baseline: +4.9%

✅ 1-count-metrics-100-times

Time: ✅ 202.379µs (SLO: <220.000µs -8.0%) vs baseline: +1.6%

Memory: ✅ 34.898MB (SLO: <35.500MB 🟡 -1.7%) vs baseline: +5.1%

✅ 1-distribution-metric-1-times

Time: ✅ 3.350µs (SLO: <20.000µs 📉 -83.3%) vs baseline: +0.4%

Memory: ✅ 34.937MB (SLO: <35.500MB 🟡 -1.6%) vs baseline: +5.0%

✅ 1-distribution-metrics-100-times

Time: ✅ 216.566µs (SLO: <230.000µs -5.8%) vs baseline: +0.7%

Memory: ✅ 34.859MB (SLO: <35.500MB 🟡 -1.8%) vs baseline: +4.7%

✅ 1-gauge-metric-1-times

Time: ✅ 2.167µs (SLO: <20.000µs 📉 -89.2%) vs baseline: -2.3%

Memory: ✅ 34.878MB (SLO: <35.500MB 🟡 -1.8%) vs baseline: +5.1%

✅ 1-gauge-metrics-100-times

Time: ✅ 136.551µs (SLO: <150.000µs -9.0%) vs baseline: -0.2%

Memory: ✅ 34.918MB (SLO: <35.500MB 🟡 -1.6%) vs baseline: +4.8%

✅ 1-rate-metric-1-times

Time: ✅ 3.150µs (SLO: <20.000µs 📉 -84.3%) vs baseline: +0.2%

Memory: ✅ 34.859MB (SLO: <35.500MB 🟡 -1.8%) vs baseline: +4.8%

✅ 1-rate-metrics-100-times

Time: ✅ 214.103µs (SLO: <250.000µs 📉 -14.4%) vs baseline: +0.6%

Memory: ✅ 34.878MB (SLO: <35.500MB 🟡 -1.8%) vs baseline: +5.0%

✅ 100-count-metrics-100-times

Time: ✅ 20.006ms (SLO: <22.000ms -9.1%) vs baseline: +0.8%

Memory: ✅ 34.859MB (SLO: <35.500MB 🟡 -1.8%) vs baseline: +5.0%

✅ 100-distribution-metrics-100-times

Time: ✅ 2.231ms (SLO: <2.550ms 📉 -12.5%) vs baseline: ~same

Memory: ✅ 34.918MB (SLO: <35.500MB 🟡 -1.6%) vs baseline: +4.9%

✅ 100-gauge-metrics-100-times

Time: ✅ 1.401ms (SLO: <1.550ms -9.6%) vs baseline: +0.3%

Memory: ✅ 34.898MB (SLO: <35.500MB 🟡 -1.7%) vs baseline: +5.0%

✅ 100-rate-metrics-100-times

Time: ✅ 2.171ms (SLO: <2.550ms 📉 -14.8%) vs baseline: ~same

Memory: ✅ 34.898MB (SLO: <35.500MB 🟡 -1.7%) vs baseline: +4.8%

✅ flush-1-metric

Time: ✅ 4.536µs (SLO: <20.000µs 📉 -77.3%) vs baseline: ~same

Memory: ✅ 35.134MB (SLO: <35.500MB 🟡 -1.0%) vs baseline: +4.6%

✅ flush-100-metrics

Time: ✅ 173.803µs (SLO: <250.000µs 📉 -30.5%) vs baseline: +0.3%

Memory: ✅ 35.271MB (SLO: <35.500MB 🟡 -0.6%) vs baseline: +5.2%

✅ flush-1000-metrics

Time: ✅ 2.176ms (SLO: <2.500ms 📉 -12.9%) vs baseline: ~same

Memory: ✅ 35.979MB (SLO: <36.500MB 🟡 -1.4%) vs baseline: +4.6%

🟡 Near SLO Breach (14 suites)

🟡 coreapiscenario - 10/10 (1 unstable)

⚠️ context_with_data_listeners

Time: ⚠️ 13.261µs (SLO: <20.000µs 📉 -33.7%) vs baseline: -0.1%

Memory: ✅ 34.918MB (SLO: <35.500MB 🟡 -1.6%) vs baseline: +5.0%

✅ context_with_data_no_listeners

Time: ✅ 3.250µs (SLO: <10.000µs 📉 -67.5%) vs baseline: -0.6%

Memory: ✅ 34.878MB (SLO: <35.500MB 🟡 -1.8%) vs baseline: +4.9%

✅ get_item_exists

Time: ✅ 0.584µs (SLO: <10.000µs 📉 -94.2%) vs baseline: +0.5%

Memory: ✅ 34.957MB (SLO: <35.500MB 🟡 -1.5%) vs baseline: +5.3%

✅ get_item_missing

Time: ✅ 0.639µs (SLO: <10.000µs 📉 -93.6%) vs baseline: -1.4%

Memory: ✅ 34.760MB (SLO: <35.500MB -2.1%) vs baseline: +4.7%

✅ set_item

Time: ✅ 24.442µs (SLO: <30.000µs 📉 -18.5%) vs baseline: +1.1%

Memory: ✅ 34.839MB (SLO: <35.500MB 🟡 -1.9%) vs baseline: +5.0%

🟡 djangosimple - 30/30

✅ appsec

Time: ✅ 19.599ms (SLO: <22.300ms 📉 -12.1%) vs baseline: +0.4%

Memory: ✅ 68.302MB (SLO: <70.500MB -3.1%) vs baseline: +4.9%

✅ exception-replay-enabled

Time: ✅ 1.359ms (SLO: <1.450ms -6.2%) vs baseline: +0.1%

Memory: ✅ 66.500MB (SLO: <67.500MB 🟡 -1.5%) vs baseline: +5.0%

✅ iast

Time: ✅ 19.614ms (SLO: <22.250ms 📉 -11.8%) vs baseline: -0.4%

Memory: ✅ 68.243MB (SLO: <70.000MB -2.5%) vs baseline: +4.6%

✅ profiler

Time: ✅ 14.669ms (SLO: <16.550ms 📉 -11.4%) vs baseline: -0.4%

Memory: ✅ 56.154MB (SLO: <57.500MB -2.3%) vs baseline: +4.9%

✅ resource-renaming

Time: ✅ 19.481ms (SLO: <21.750ms 📉 -10.4%) vs baseline: ~same

Memory: ✅ 68.321MB (SLO: <70.500MB -3.1%) vs baseline: +5.1%

✅ span-code-origin

Time: ✅ 19.943ms (SLO: <28.200ms 📉 -29.3%) vs baseline: +0.5%

Memory: ✅ 68.269MB (SLO: <71.000MB -3.8%) vs baseline: +4.8%

✅ tracer

Time: ✅ 19.514ms (SLO: <21.750ms 📉 -10.3%) vs baseline: -0.2%

Memory: ✅ 68.380MB (SLO: <70.000MB -2.3%) vs baseline: +4.9%

✅ tracer-and-profiler

Time: ✅ 20.912ms (SLO: <23.500ms 📉 -11.0%) vs baseline: ~same

Memory: ✅ 69.340MB (SLO: <71.000MB -2.3%) vs baseline: +4.8%

✅ tracer-dont-create-db-spans

Time: ✅ 19.621ms (SLO: <21.500ms -8.7%) vs baseline: -0.2%

Memory: ✅ 68.410MB (SLO: <70.000MB -2.3%) vs baseline: +5.0%

✅ tracer-minimal

Time: ✅ 16.798ms (SLO: <17.500ms -4.0%) vs baseline: -0.4%

Memory: ✅ 68.104MB (SLO: <70.000MB -2.7%) vs baseline: +4.7%

✅ tracer-native

Time: ✅ 19.445ms (SLO: <21.750ms 📉 -10.6%) vs baseline: -0.2%

Memory: ✅ 68.380MB (SLO: <72.500MB -5.7%) vs baseline: +5.0%

✅ tracer-no-caches

Time: ✅ 17.630ms (SLO: <19.650ms 📉 -10.3%) vs baseline: +0.3%

Memory: ✅ 68.213MB (SLO: <70.000MB -2.6%) vs baseline: +4.7%

✅ tracer-no-databases

Time: ✅ 19.144ms (SLO: <20.100ms -4.8%) vs baseline: ~same

Memory: ✅ 67.977MB (SLO: <70.000MB -2.9%) vs baseline: +4.8%

✅ tracer-no-middleware

Time: ✅ 19.300ms (SLO: <21.500ms 📉 -10.2%) vs baseline: ~same

Memory: ✅ 68.252MB (SLO: <70.000MB -2.5%) vs baseline: +4.7%

✅ tracer-no-templates

Time: ✅ 19.487ms (SLO: <22.000ms 📉 -11.4%) vs baseline: +0.9%

Memory: ✅ 68.292MB (SLO: <70.500MB -3.1%) vs baseline: +4.8%

🟡 errortrackingdjangosimple - 6/6

✅ errortracking-enabled-all

Time: ✅ 16.299ms (SLO: <19.850ms 📉 -17.9%) vs baseline: +0.1%

Memory: ✅ 69.887MB (SLO: <70.000MB 🟡 -0.2%) vs baseline: +4.8%

✅ errortracking-enabled-user

Time: ✅ 16.393ms (SLO: <19.400ms 📉 -15.5%) vs baseline: +0.6%

Memory: ✅ 69.795MB (SLO: <70.000MB 🟡 -0.3%) vs baseline: +4.8%

✅ tracer-enabled

Time: ✅ 16.317ms (SLO: <19.450ms 📉 -16.1%) vs baseline: +0.1%

Memory: ✅ 69.894MB (SLO: <70.000MB 🟡 -0.2%) vs baseline: +4.9%

🟡 errortrackingflasksqli - 6/6

✅ errortracking-enabled-all

Time: ✅ 2.064ms (SLO: <2.300ms 📉 -10.3%) vs baseline: ~same

Memory: ✅ 55.915MB (SLO: <56.500MB 🟡 -1.0%) vs baseline: +4.9%

✅ errortracking-enabled-user

Time: ✅ 2.082ms (SLO: <2.250ms -7.5%) vs baseline: +0.6%

Memory: ✅ 55.935MB (SLO: <56.500MB 🟡 -1.0%) vs baseline: +4.9%

✅ tracer-enabled

Time: ✅ 2.064ms (SLO: <2.300ms 📉 -10.2%) vs baseline: ~same

Memory: ✅ 55.817MB (SLO: <56.500MB 🟡 -1.2%) vs baseline: +4.7%

🟡 flasksimple - 18/18

✅ appsec-get

Time: ✅ 3.373ms (SLO: <4.750ms 📉 -29.0%) vs baseline: ~same

Memory: ✅ 55.869MB (SLO: <66.500MB 📉 -16.0%) vs baseline: +4.8%

✅ appsec-post

Time: ✅ 2.852ms (SLO: <6.750ms 📉 -57.8%) vs baseline: -0.2%

Memory: ✅ 55.969MB (SLO: <66.500MB 📉 -15.8%) vs baseline: +5.1%

✅ appsec-telemetry

Time: ✅ 3.403ms (SLO: <4.750ms 📉 -28.4%) vs baseline: +0.9%

Memory: ✅ 55.916MB (SLO: <66.500MB 📉 -15.9%) vs baseline: +5.1%

✅ debugger

Time: ✅ 1.871ms (SLO: <2.000ms -6.5%) vs baseline: ~same

Memory: ✅ 47.826MB (SLO: <49.500MB -3.4%) vs baseline: +4.8%

✅ iast-get

Time: ✅ 1.853ms (SLO: <2.000ms -7.4%) vs baseline: -0.4%

Memory: ✅ 44.759MB (SLO: <49.000MB -8.7%) vs baseline: +5.0%

✅ profiler

Time: ✅ 1.861ms (SLO: <2.100ms 📉 -11.4%) vs baseline: -0.1%

Memory: ✅ 48.733MB (SLO: <50.000MB -2.5%) vs baseline: +4.9%

✅ resource-renaming

Time: ✅ 3.351ms (SLO: <3.650ms -8.2%) vs baseline: -0.3%

Memory: ✅ 55.791MB (SLO: <56.000MB 🟡 -0.4%) vs baseline: +4.7%

✅ tracer

Time: ✅ 3.357ms (SLO: <3.650ms -8.0%) vs baseline: -0.4%

Memory: ✅ 55.965MB (SLO: <56.500MB 🟡 -0.9%) vs baseline: +4.8%

✅ tracer-native

Time: ✅ 3.370ms (SLO: <3.650ms -7.7%) vs baseline: ~same

Memory: ✅ 55.830MB (SLO: <60.000MB -6.9%) vs baseline: +4.7%

🟡 flasksqli - 6/6

✅ appsec-enabled

Time: ✅ 2.062ms (SLO: <4.200ms 📉 -50.9%) vs baseline: +0.2%

Memory: ✅ 55.935MB (SLO: <66.000MB 📉 -15.3%) vs baseline: +4.9%

✅ iast-enabled

Time: ✅ 2.074ms (SLO: <2.800ms 📉 -25.9%) vs baseline: +0.2%

Memory: ✅ 55.896MB (SLO: <62.500MB 📉 -10.6%) vs baseline: +4.8%

✅ tracer-enabled

Time: ✅ 2.056ms (SLO: <2.250ms -8.6%) vs baseline: ~same

Memory: ✅ 55.896MB (SLO: <56.500MB 🟡 -1.1%) vs baseline: +4.8%

🟡 httppropagationextract - 60/60

✅ all_styles_all_headers

Time: ✅ 81.795µs (SLO: <100.000µs 📉 -18.2%) vs baseline: -0.2%

Memory: ✅ 34.977MB (SLO: <35.500MB 🟡 -1.5%) vs baseline: +4.9%

✅ b3_headers

Time: ✅ 14.381µs (SLO: <20.000µs 📉 -28.1%) vs baseline: +0.2%

Memory: ✅ 34.996MB (SLO: <35.500MB 🟡 -1.4%) vs baseline: +4.9%

✅ b3_single_headers

Time: ✅ 13.448µs (SLO: <20.000µs 📉 -32.8%) vs baseline: -0.2%

Memory: ✅ 34.996MB (SLO: <35.500MB 🟡 -1.4%) vs baseline: +4.9%

✅ datadog_tracecontext_tracestate_not_propagated_on_trace_id_no_match

Time: ✅ 64.147µs (SLO: <80.000µs 📉 -19.8%) vs baseline: ~same

Memory: ✅ 34.898MB (SLO: <35.500MB 🟡 -1.7%) vs baseline: +4.3%

✅ datadog_tracecontext_tracestate_propagated_on_trace_id_match

Time: ✅ 66.357µs (SLO: <80.000µs 📉 -17.1%) vs baseline: -0.4%

Memory: ✅ 34.859MB (SLO: <35.500MB 🟡 -1.8%) vs baseline: +4.6%

✅ empty_headers

Time: ✅ 1.614µs (SLO: <10.000µs 📉 -83.9%) vs baseline: +0.6%

Memory: ✅ 34.898MB (SLO: <35.500MB 🟡 -1.7%) vs baseline: +4.7%

✅ full_t_id_datadog_headers

Time: ✅ 22.720µs (SLO: <30.000µs 📉 -24.3%) vs baseline: ~same

Memory: ✅ 34.898MB (SLO: <35.500MB 🟡 -1.7%) vs baseline: +4.3%

✅ invalid_priority_header

Time: ✅ 6.508µs (SLO: <10.000µs 📉 -34.9%) vs baseline: -0.6%

Memory: ✅ 34.996MB (SLO: <35.500MB 🟡 -1.4%) vs baseline: +4.9%

✅ invalid_span_id_header

Time: ✅ 6.531µs (SLO: <10.000µs 📉 -34.7%) vs baseline: +0.5%

Memory: ✅ 34.977MB (SLO: <35.500MB 🟡 -1.5%) vs baseline: +4.7%

✅ invalid_tags_header

Time: ✅ 6.528µs (SLO: <10.000µs 📉 -34.7%) vs baseline: +0.4%

Memory: ✅ 34.957MB (SLO: <35.500MB 🟡 -1.5%) vs baseline: +4.9%

✅ invalid_trace_id_header

Time: ✅ 6.576µs (SLO: <10.000µs 📉 -34.2%) vs baseline: +0.4%

Memory: ✅ 34.937MB (SLO: <35.500MB 🟡 -1.6%) vs baseline: +4.5%

✅ large_header_no_matches

Time: ✅ 27.877µs (SLO: <30.000µs -7.1%) vs baseline: +0.3%

Memory: ✅ 35.016MB (SLO: <35.500MB 🟡 -1.4%) vs baseline: +5.2%

✅ large_valid_headers_all

Time: ✅ 28.978µs (SLO: <40.000µs 📉 -27.6%) vs baseline: ~same

Memory: ✅ 34.957MB (SLO: <35.500MB 🟡 -1.5%) vs baseline: +4.6%

✅ medium_header_no_matches

Time: ✅ 9.831µs (SLO: <20.000µs 📉 -50.8%) vs baseline: -0.2%

Memory: ✅ 34.937MB (SLO: <35.500MB 🟡 -1.6%) vs baseline: +4.7%

✅ medium_valid_headers_all

Time: ✅ 11.314µs (SLO: <20.000µs 📉 -43.4%) vs baseline: +0.3%

Memory: ✅ 34.937MB (SLO: <35.500MB 🟡 -1.6%) vs baseline: +4.5%

✅ none_propagation_style

Time: ✅ 1.707µs (SLO: <10.000µs 📉 -82.9%) vs baseline: -1.0%

Memory: ✅ 34.957MB (SLO: <35.500MB 🟡 -1.5%) vs baseline: +5.0%

✅ tracecontext_headers

Time: ✅ 34.947µs (SLO: <40.000µs 📉 -12.6%) vs baseline: +0.3%

Memory: ✅ 34.878MB (SLO: <35.500MB 🟡 -1.8%) vs baseline: +4.4%

✅ valid_headers_all

Time: ✅ 6.485µs (SLO: <10.000µs 📉 -35.2%) vs baseline: -0.4%

Memory: ✅ 35.016MB (SLO: <35.500MB 🟡 -1.4%) vs baseline: +5.2%

✅ valid_headers_basic

Time: ✅ 6.118µs (SLO: <10.000µs 📉 -38.8%) vs baseline: +0.4%

Memory: ✅ 35.036MB (SLO: <35.500MB 🟡 -1.3%) vs baseline: +4.8%

✅ wsgi_empty_headers

Time: ✅ 1.596µs (SLO: <10.000µs 📉 -84.0%) vs baseline: +0.2%

Memory: ✅ 34.996MB (SLO: <35.500MB 🟡 -1.4%) vs baseline: +4.8%

✅ wsgi_invalid_priority_header

Time: ✅ 6.583µs (SLO: <10.000µs 📉 -34.2%) vs baseline: +0.7%

Memory: ✅ 34.918MB (SLO: <35.500MB 🟡 -1.6%) vs baseline: +4.5%

✅ wsgi_invalid_span_id_header

Time: ✅ 1.605µs (SLO: <10.000µs 📉 -84.0%) vs baseline: ~same

Memory: ✅ 34.937MB (SLO: <35.500MB 🟡 -1.6%) vs baseline: +4.6%

✅ wsgi_invalid_tags_header

Time: ✅ 6.580µs (SLO: <10.000µs 📉 -34.2%) vs baseline: +0.7%

Memory: ✅ 34.918MB (SLO: <35.500MB 🟡 -1.6%) vs baseline: +4.7%

✅ wsgi_invalid_trace_id_header

Time: ✅ 6.590µs (SLO: <10.000µs 📉 -34.1%) vs baseline: -0.2%

Memory: ✅ 34.977MB (SLO: <35.500MB 🟡 -1.5%) vs baseline: +4.9%

✅ wsgi_large_header_no_matches

Time: ✅ 28.836µs (SLO: <40.000µs 📉 -27.9%) vs baseline: ~same

Memory: ✅ 34.918MB (SLO: <35.500MB 🟡 -1.6%) vs baseline: +4.5%

✅ wsgi_large_valid_headers_all

Time: ✅ 30.193µs (SLO: <40.000µs 📉 -24.5%) vs baseline: +0.5%

Memory: ✅ 34.937MB (SLO: <35.500MB 🟡 -1.6%) vs baseline: +4.7%

✅ wsgi_medium_header_no_matches

Time: ✅ 10.121µs (SLO: <20.000µs 📉 -49.4%) vs baseline: -0.4%

Memory: ✅ 34.918MB (SLO: <35.500MB 🟡 -1.6%) vs baseline: +4.7%

✅ wsgi_medium_valid_headers_all

Time: ✅ 11.506µs (SLO: <20.000µs 📉 -42.5%) vs baseline: -0.4%

Memory: ✅ 34.996MB (SLO: <35.500MB 🟡 -1.4%) vs baseline: +5.1%

✅ wsgi_valid_headers_all

Time: ✅ 6.562µs (SLO: <10.000µs 📉 -34.4%) vs baseline: +0.3%

Memory: ✅ 34.996MB (SLO: <35.500MB 🟡 -1.4%) vs baseline: +4.9%

✅ wsgi_valid_headers_basic

Time: ✅ 6.115µs (SLO: <10.000µs 📉 -38.8%) vs baseline: ~same

Memory: ✅ 34.957MB (SLO: <35.500MB 🟡 -1.5%) vs baseline: +5.0%

🟡 httppropagationinject - 16/16

✅ ids_only

Time: ✅ 22.047µs (SLO: <30.000µs 📉 -26.5%) vs baseline: +5.9%

Memory: ✅ 34.937MB (SLO: <35.500MB 🟡 -1.6%) vs baseline: +4.8%

✅ with_all

Time: ✅ 27.883µs (SLO: <40.000µs 📉 -30.3%) vs baseline: +0.4%

Memory: ✅ 35.016MB (SLO: <35.500MB 🟡 -1.4%) vs baseline: +5.2%

✅ with_dd_origin

Time: ✅ 24.718µs (SLO: <30.000µs 📉 -17.6%) vs baseline: +0.6%

Memory: ✅ 34.918MB (SLO: <35.500MB 🟡 -1.6%) vs baseline: +4.9%

✅ with_priority_and_origin

Time: ✅ 24.083µs (SLO: <40.000µs 📉 -39.8%) vs baseline: +0.8%

Memory: ✅ 34.878MB (SLO: <35.500MB 🟡 -1.8%) vs baseline: +4.9%

✅ with_sampling_priority

Time: ✅ 20.981µs (SLO: <30.000µs 📉 -30.1%) vs baseline: +0.1%

Memory: ✅ 34.957MB (SLO: <35.500MB 🟡 -1.5%) vs baseline: +5.0%

✅ with_tags

Time: ✅ 26.055µs (SLO: <40.000µs 📉 -34.9%) vs baseline: +0.5%

Memory: ✅ 34.996MB (SLO: <35.500MB 🟡 -1.4%) vs baseline: +5.2%

✅ with_tags_invalid

Time: ✅ 27.367µs (SLO: <40.000µs 📉 -31.6%) vs baseline: -0.1%

Memory: ✅ 34.918MB (SLO: <35.500MB 🟡 -1.6%) vs baseline: +5.1%

✅ with_tags_max_size

Time: ✅ 26.676µs (SLO: <40.000µs 📉 -33.3%) vs baseline: +0.6%

Memory: ✅ 34.918MB (SLO: <35.500MB 🟡 -1.6%) vs baseline: +4.9%

🟡 ratelimiter - 12/12

✅ defaults

Time: ✅ 2.351µs (SLO: <10.000µs 📉 -76.5%) vs baseline: +0.1%

Memory: ✅ 34.977MB (SLO: <35.500MB 🟡 -1.5%) vs baseline: +4.4%

✅ high_rate_limit

Time: ✅ 2.414µs (SLO: <10.000µs 📉 -75.9%) vs baseline: ~same

Memory: ✅ 35.075MB (SLO: <35.500MB 🟡 -1.2%) vs baseline: +4.7%

✅ long_window

Time: ✅ 2.367µs (SLO: <10.000µs 📉 -76.3%) vs baseline: +1.1%

Memory: ✅ 35.036MB (SLO: <35.500MB 🟡 -1.3%) vs baseline: +4.6%

✅ low_rate_limit

Time: ✅ 2.351µs (SLO: <10.000µs 📉 -76.5%) vs baseline: -0.5%

Memory: ✅ 35.173MB (SLO: <35.500MB 🟡 -0.9%) vs baseline: +4.9%

✅ no_rate_limit

Time: ✅ 0.822µs (SLO: <10.000µs 📉 -91.8%) vs baseline: +0.2%

Memory: ✅ 34.918MB (SLO: <35.500MB 🟡 -1.6%) vs baseline: +4.3%

✅ short_window

Time: ✅ 2.479µs (SLO: <10.000µs 📉 -75.2%) vs baseline: ~same

Memory: ✅ 35.173MB (SLO: <35.500MB 🟡 -0.9%) vs baseline: +4.8%

🟡 recursivecomputation - 8/8

✅ deep

Time: ✅ 308.201ms (SLO: <320.950ms -4.0%) vs baseline: ~same

Memory: ✅ 36.078MB (SLO: <36.500MB 🟡 -1.2%) vs baseline: +5.2%

✅ deep-profiled

Time: ✅ 315.015ms (SLO: <359.150ms 📉 -12.3%) vs baseline: -0.1%

Memory: ✅ 39.813MB (SLO: <40.500MB 🟡 -1.7%) vs baseline: +4.9%

✅ medium

Time: ✅ 6.991ms (SLO: <7.400ms -5.5%) vs baseline: +0.1%

Memory: ✅ 34.780MB (SLO: <35.500MB -2.0%) vs baseline: +4.4%

✅ shallow

Time: ✅ 0.944ms (SLO: <1.050ms 📉 -10.1%) vs baseline: +0.9%

Memory: ✅ 34.780MB (SLO: <35.500MB -2.0%) vs baseline: +4.9%

🟡 samplingrules - 8/8

✅ average_match

Time: ✅ 137.814µs (SLO: <290.000µs 📉 -52.5%) vs baseline: +0.7%

Memory: ✅ 34.878MB (SLO: <35.500MB 🟡 -1.8%) vs baseline: +4.9%

✅ high_match

Time: ✅ 173.877µs (SLO: <480.000µs 📉 -63.8%) vs baseline: -0.6%

Memory: ✅ 34.898MB (SLO: <35.500MB 🟡 -1.7%) vs baseline: +5.2%

✅ low_match

Time: ✅ 99.056µs (SLO: <120.000µs 📉 -17.5%) vs baseline: -0.6%

Memory: ✅ 603.620MB (SLO: <700.000MB 📉 -13.8%) vs baseline: +4.8%

✅ very_low_match

Time: ✅ 2.672ms (SLO: <8.500ms 📉 -68.6%) vs baseline: +0.4%

Memory: ✅ 71.219MB (SLO: <75.000MB -5.0%) vs baseline: +5.0%

🟡 sethttpmeta - 32/32

✅ all-disabled

Time: ✅ 10.582µs (SLO: <20.000µs 📉 -47.1%) vs baseline: -0.4%

Memory: ✅ 35.311MB (SLO: <36.000MB 🟡 -1.9%) vs baseline: +3.8%

✅ all-enabled

Time: ✅ 41.118µs (SLO: <50.000µs 📉 -17.8%) vs baseline: +2.6%

Memory: ✅ 35.429MB (SLO: <36.000MB 🟡 -1.6%) vs baseline: +4.0%

✅ collectipvariant_exists

Time: ✅ 40.918µs (SLO: <50.000µs 📉 -18.2%) vs baseline: ~same

Memory: ✅ 35.429MB (SLO: <36.000MB 🟡 -1.6%) vs baseline: +4.2%

✅ no-collectipvariant

Time: ✅ 40.204µs (SLO: <50.000µs 📉 -19.6%) vs baseline: +0.6%

Memory: ✅ 35.409MB (SLO: <36.000MB 🟡 -1.6%) vs baseline: +4.2%

✅ no-useragentvariant

Time: ✅ 38.889µs (SLO: <50.000µs 📉 -22.2%) vs baseline: -0.1%

Memory: ✅ 35.645MB (SLO: <36.000MB 🟡 -1.0%) vs baseline: +5.1%

✅ obfuscation-no-query

Time: ✅ 40.600µs (SLO: <50.000µs 📉 -18.8%) vs baseline: ~same

Memory: ✅ 35.409MB (SLO: <36.000MB 🟡 -1.6%) vs baseline: +4.2%

✅ obfuscation-regular-case-explicit-query

Time: ✅ 75.985µs (SLO: <90.000µs 📉 -15.6%) vs baseline: +0.2%

Memory: ✅ 35.684MB (SLO: <36.500MB -2.2%) vs baseline: +4.9%

✅ obfuscation-regular-case-implicit-query

Time: ✅ 76.484µs (SLO: <90.000µs 📉 -15.0%) vs baseline: -0.2%

Memory: ✅ 35.665MB (SLO: <36.500MB -2.3%) vs baseline: +4.6%

✅ obfuscation-send-querystring-disabled

Time: ✅ 154.616µs (SLO: <170.000µs -9.0%) vs baseline: ~same

Memory: ✅ 35.763MB (SLO: <36.500MB -2.0%) vs baseline: +5.3%

✅ obfuscation-worst-case-explicit-query

Time: ✅ 148.993µs (SLO: <160.000µs -6.9%) vs baseline: +0.2%

Memory: ✅ 35.665MB (SLO: <36.500MB -2.3%) vs baseline: +5.0%

✅ obfuscation-worst-case-implicit-query

Time: ✅ 155.408µs (SLO: <170.000µs -8.6%) vs baseline: ~same

Memory: ✅ 35.606MB (SLO: <36.500MB -2.5%) vs baseline: +4.5%

✅ useragentvariant_exists_1

Time: ✅ 39.714µs (SLO: <50.000µs 📉 -20.6%) vs baseline: ~same

Memory: ✅ 35.547MB (SLO: <36.000MB 🟡 -1.3%) vs baseline: +4.4%

✅ useragentvariant_exists_2

Time: ✅ 40.722µs (SLO: <50.000µs 📉 -18.6%) vs baseline: -0.2%

Memory: ✅ 35.311MB (SLO: <36.000MB 🟡 -1.9%) vs baseline: +3.8%

✅ useragentvariant_exists_3

Time: ✅ 40.258µs (SLO: <50.000µs 📉 -19.5%) vs baseline: -0.3%

Memory: ✅ 35.252MB (SLO: <36.000MB -2.1%) vs baseline: +3.4%

✅ useragentvariant_not_exists_1

Time: ✅ 39.794µs (SLO: <50.000µs 📉 -20.4%) vs baseline: +0.6%

Memory: ✅ 35.409MB (SLO: <36.000MB 🟡 -1.6%) vs baseline: +4.2%

✅ useragentvariant_not_exists_2

Time: ✅ 39.710µs (SLO: <50.000µs 📉 -20.6%) vs baseline: +0.4%

Memory: ✅ 35.330MB (SLO: <36.000MB 🟡 -1.9%) vs baseline: +3.8%

🟡 span - 26/26

✅ add-event

Time: ✅ 18.090ms (SLO: <22.500ms 📉 -19.6%) vs baseline: -0.2%

Memory: ✅ 36.994MB (SLO: <53.000MB 📉 -30.2%) vs baseline: +4.9%

✅ add-metrics

Time: ✅ 88.943ms (SLO: <93.500ms -4.9%) vs baseline: +1.0%

Memory: ✅ 41.141MB (SLO: <53.000MB 📉 -22.4%) vs baseline: +5.0%

✅ add-tags

Time: ✅ 142.453ms (SLO: <155.000ms -8.1%) vs baseline: -0.1%

Memory: ✅ 41.101MB (SLO: <53.000MB 📉 -22.5%) vs baseline: +4.8%

✅ get-context

Time: ✅ 16.928ms (SLO: <20.500ms 📉 -17.4%) vs baseline: -0.7%

Memory: ✅ 36.701MB (SLO: <53.000MB 📉 -30.8%) vs baseline: +4.7%

✅ is-recording

Time: ✅ 17.255ms (SLO: <20.500ms 📉 -15.8%) vs baseline: -0.2%

Memory: ✅ 36.799MB (SLO: <53.000MB 📉 -30.6%) vs baseline: +4.8%

✅ record-exception

Time: ✅ 36.607ms (SLO: <40.000ms -8.5%) vs baseline: ~same

Memory: ✅ 37.322MB (SLO: <53.000MB 📉 -29.6%) vs baseline: +4.8%

✅ set-status

Time: ✅ 18.608ms (SLO: <22.000ms 📉 -15.4%) vs baseline: -0.6%

Memory: ✅ 36.821MB (SLO: <53.000MB 📉 -30.5%) vs baseline: +4.8%

✅ start

Time: ✅ 17.277ms (SLO: <20.500ms 📉 -15.7%) vs baseline: +2.9%

Memory: ✅ 36.821MB (SLO: <53.000MB 📉 -30.5%) vs baseline: +5.1%

✅ start-finish

Time: ✅ 51.096ms (SLO: <52.500ms -2.7%) vs baseline: ~same

Memory: ✅ 34.819MB (SLO: <35.500MB 🟡 -1.9%) vs baseline: +4.8%

✅ start-finish-telemetry

Time: ✅ 52.261ms (SLO: <54.500ms -4.1%) vs baseline: +0.4%

Memory: ✅ 34.741MB (SLO: <35.500MB -2.1%) vs baseline: +4.5%

✅ start-finish-traceid128

Time: ✅ 53.894ms (SLO: <57.000ms -5.4%) vs baseline: -0.4%

Memory: ✅ 34.898MB (SLO: <35.500MB 🟡 -1.7%) vs baseline: +5.3%

✅ start-traceid128

Time: ✅ 17.312ms (SLO: <22.500ms 📉 -23.1%) vs baseline: -0.2%

Memory: ✅ 36.686MB (SLO: <53.000MB 📉 -30.8%) vs baseline: +4.6%

✅ update-name

Time: ✅ 17.274ms (SLO: <22.000ms 📉 -21.5%) vs baseline: +0.1%

Memory: ✅ 36.851MB (SLO: <53.000MB 📉 -30.5%) vs baseline: +4.8%

🟡 tracer - 6/6

✅ large

Time: ✅ 29.294ms (SLO: <32.950ms 📉 -11.1%) vs baseline: +0.5%

Memory: ✅ 35.999MB (SLO: <36.500MB 🟡 -1.4%) vs baseline: +4.9%

✅ medium

Time: ✅ 2.870ms (SLO: <3.200ms 📉 -10.3%) vs baseline: -0.5%

Memory: ✅ 34.760MB (SLO: <35.500MB -2.1%) vs baseline: +4.6%

✅ small

Time: ✅ 330.504µs (SLO: <370.000µs 📉 -10.7%) vs baseline: +1.5%

Memory: ✅ 34.800MB (SLO: <35.500MB 🟡 -2.0%) vs baseline: +5.1%

⚠️ Unstable Tests (1 suite)

⚠️ packagesupdateimporteddependencies - 24/24 (1 unstable)

✅ import_many

Time: ✅ 154.948µs (SLO: <170.000µs -8.9%) vs baseline: ~same

Memory: ✅ 39.438MB (SLO: <43.000MB -8.3%) vs baseline: +4.7%

✅ import_many_cached

Time: ✅ 121.539µs (SLO: <130.000µs -6.5%) vs baseline: +0.6%

Memory: ✅ 39.450MB (SLO: <43.000MB -8.3%) vs baseline: +5.4%

✅ import_many_stdlib

Time: ✅ 0.755ms (SLO: <1.750ms 📉 -56.9%) vs baseline: ~same

Memory: ✅ 39.576MB (SLO: <43.000MB -8.0%) vs baseline: +5.5%

⚠️ import_many_stdlib_cached

Time: ⚠️ 0.173ms (SLO: <1.100ms 📉 -84.3%) vs baseline: ~same

Memory: ✅ 39.338MB (SLO: <43.000MB -8.5%) vs baseline: +4.8%

✅ import_many_unknown

Time: ✅ 828.843µs (SLO: <890.000µs -6.9%) vs baseline: -0.4%

Memory: ✅ 39.840MB (SLO: <43.000MB -7.3%) vs baseline: +6.4%

✅ import_many_unknown_cached

Time: ✅ 792.589µs (SLO: <870.000µs -8.9%) vs baseline: -1.1%

Memory: ✅ 39.537MB (SLO: <43.000MB -8.1%) vs baseline: +4.8%

✅ import_one

Time: ✅ 19.684µs (SLO: <30.000µs 📉 -34.4%) vs baseline: +0.1%

Memory: ✅ 39.484MB (SLO: <43.000MB -8.2%) vs baseline: +5.0%

✅ import_one_cache

Time: ✅ 6.277µs (SLO: <10.000µs 📉 -37.2%) vs baseline: +0.3%

Memory: ✅ 39.528MB (SLO: <43.000MB -8.1%) vs baseline: +4.9%

✅ import_one_stdlib

Time: ✅ 18.826µs (SLO: <20.000µs -5.9%) vs baseline: +1.0%

Memory: ✅ 39.585MB (SLO: <43.000MB -7.9%) vs baseline: +5.1%

✅ import_one_stdlib_cache

Time: ✅ 6.262µs (SLO: <10.000µs 📉 -37.4%) vs baseline: -0.3%

Memory: ✅ 39.669MB (SLO: <43.000MB -7.7%) vs baseline: +5.6%

✅ import_one_unknown

Time: ✅ 45.500µs (SLO: <50.000µs -9.0%) vs baseline: +0.9%

Memory: ✅ 39.418MB (SLO: <43.000MB -8.3%) vs baseline: +5.3%

✅ import_one_unknown_cache

Time: ✅ 6.301µs (SLO: <10.000µs 📉 -37.0%) vs baseline: +0.5%

Memory: ✅ 39.435MB (SLO: <43.000MB -8.3%) vs baseline: +4.8%

✅ All Tests Passing (6 suites)

✅ iast_aspects - 40/40

✅ re_expand_aspect

Time: ✅ 37.243µs (SLO: <40.000µs -6.9%) vs baseline: +6.4%

Memory: ✅ 41.347MB (SLO: <43.500MB -5.0%) vs baseline: +4.6%

✅ re_expand_noaspect

Time: ✅ 35.155µs (SLO: <40.000µs 📉 -12.1%) vs baseline: +0.3%

Memory: ✅ 41.386MB (SLO: <43.500MB -4.9%) vs baseline: +4.7%

✅ re_findall_aspect

Time: ✅ 3.427µs (SLO: <10.000µs 📉 -65.7%) vs baseline: -0.2%

Memory: ✅ 41.484MB (SLO: <43.500MB -4.6%) vs baseline: +5.0%

✅ re_findall_noaspect

Time: ✅ 3.269µs (SLO: <10.000µs 📉 -67.3%) vs baseline: +0.3%

Memory: ✅ 41.445MB (SLO: <43.500MB -4.7%) vs baseline: +4.9%

✅ re_finditer_aspect

Time: ✅ 4.509µs (SLO: <10.000µs 📉 -54.9%) vs baseline: -1.0%

Memory: ✅ 41.406MB (SLO: <43.500MB -4.8%) vs baseline: +4.7%

✅ re_finditer_noaspect

Time: ✅ 3.297µs (SLO: <10.000µs 📉 -67.0%) vs baseline: -0.5%

Memory: ✅ 41.386MB (SLO: <43.500MB -4.9%) vs baseline: +4.8%

✅ re_fullmatch_aspect

Time: ✅ 2.789µs (SLO: <10.000µs 📉 -72.1%) vs baseline: -1.2%

Memory: ✅ 41.406MB (SLO: <43.500MB -4.8%) vs baseline: +4.7%

✅ re_fullmatch_noaspect

Time: ✅ 3.094µs (SLO: <10.000µs 📉 -69.1%) vs baseline: +0.6%

Memory: ✅ 41.445MB (SLO: <43.500MB -4.7%) vs baseline: +5.2%

✅ re_group_aspect

Time: ✅ 4.843µs (SLO: <10.000µs 📉 -51.6%) vs baseline: -0.9%

Memory: ✅ 41.406MB (SLO: <43.500MB -4.8%) vs baseline: +5.1%

✅ re_group_noaspect

Time: ✅ 4.903µs (SLO: <10.000µs 📉 -51.0%) vs baseline: -0.7%

Memory: ✅ 41.386MB (SLO: <43.500MB -4.9%) vs baseline: +4.9%

✅ re_groups_aspect

Time: ✅ 4.977µs (SLO: <10.000µs 📉 -50.2%) vs baseline: -0.6%

Memory: ✅ 41.347MB (SLO: <43.500MB -5.0%) vs baseline: +4.7%

✅ re_groups_noaspect

Time: ✅ 4.995µs (SLO: <10.000µs 📉 -50.0%) vs baseline: +0.4%

Memory: ✅ 41.347MB (SLO: <43.500MB -5.0%) vs baseline: +4.8%

✅ re_match_aspect

Time: ✅ 2.836µs (SLO: <10.000µs 📉 -71.6%) vs baseline: ~same

Memory: ✅ 41.406MB (SLO: <43.500MB -4.8%) vs baseline: +4.7%

✅ re_match_noaspect

Time: ✅ 3.102µs (SLO: <10.000µs 📉 -69.0%) vs baseline: +0.6%

Memory: ✅ 41.445MB (SLO: <43.500MB -4.7%) vs baseline: +5.0%

✅ re_search_aspect

Time: ✅ 2.649µs (SLO: <10.000µs 📉 -73.5%) vs baseline: ~same

Memory: ✅ 41.386MB (SLO: <43.500MB -4.9%) vs baseline: +4.8%

✅ re_search_noaspect

Time: ✅ 2.896µs (SLO: <10.000µs 📉 -71.0%) vs baseline: +0.2%

Memory: ✅ 41.425MB (SLO: <43.500MB -4.8%) vs baseline: +5.1%

✅ re_sub_aspect

Time: ✅ 3.567µs (SLO: <10.000µs 📉 -64.3%) vs baseline: +0.8%

Memory: ✅ 41.406MB (SLO: <43.500MB -4.8%) vs baseline: +4.7%

✅ re_sub_noaspect

Time: ✅ 3.960µs (SLO: <10.000µs 📉 -60.4%) vs baseline: ~same

Memory: ✅ 41.327MB (SLO: <43.500MB -5.0%) vs baseline: +4.6%

✅ re_subn_aspect

Time: ✅ 3.974µs (SLO: <10.000µs 📉 -60.3%) vs baseline: +4.4%

Memory: ✅ 41.445MB (SLO: <43.500MB -4.7%) vs baseline: +5.0%

✅ re_subn_noaspect

Time: ✅ 4.099µs (SLO: <10.000µs 📉 -59.0%) vs baseline: ~same

Memory: ✅ 41.465MB (SLO: <43.500MB -4.7%) vs baseline: +5.0%

✅ iastaspectssplit - 12/12

✅ rsplit_aspect

Time: ✅ 1.589µs (SLO: <10.000µs 📉 -84.1%) vs baseline: +3.8%

Memory: ✅ 41.484MB (SLO: <43.500MB -4.6%) vs baseline: +5.1%

✅ rsplit_noaspect

Time: ✅ 1.614µs (SLO: <10.000µs 📉 -83.9%) vs baseline: ~same

Memory: ✅ 41.406MB (SLO: <43.500MB -4.8%) vs baseline: +5.0%

✅ split_aspect

Time: ✅ 1.547µs (SLO: <10.000µs 📉 -84.5%) vs baseline: +0.7%

Memory: ✅ 41.465MB (SLO: <43.500MB -4.7%) vs baseline: +4.9%

✅ split_noaspect

Time: ✅ 1.605µs (SLO: <10.000µs 📉 -84.0%) vs baseline: -1.1%

Memory: ✅ 41.406MB (SLO: <43.500MB -4.8%) vs baseline: +4.9%

✅ splitlines_aspect

Time: ✅ 1.505µs (SLO: <10.000µs 📉 -85.0%) vs baseline: -0.5%

Memory: ✅ 41.465MB (SLO: <43.500MB -4.7%) vs baseline: +4.8%

✅ splitlines_noaspect

Time: ✅ 1.552µs (SLO: <10.000µs 📉 -84.5%) vs baseline: -0.2%

Memory: ✅ 41.425MB (SLO: <43.500MB -4.8%) vs baseline: +4.9%

✅ iastpropagation - 8/8

✅ no-propagation

Time: ✅ 48.644µs (SLO: <60.000µs 📉 -18.9%) vs baseline: -0.4%

Memory: ✅ 38.378MB (SLO: <42.000MB -8.6%) vs baseline: +5.1%

✅ propagation_enabled

Time: ✅ 137.030µs (SLO: <190.000µs 📉 -27.9%) vs baseline: +0.2%

Memory: ✅ 38.299MB (SLO: <42.000MB -8.8%) vs baseline: +4.9%

✅ propagation_enabled_100

Time: ✅ 1.579ms (SLO: <2.300ms 📉 -31.3%) vs baseline: -0.3%

Memory: ✅ 38.299MB (SLO: <42.000MB -8.8%) vs baseline: +4.6%

✅ propagation_enabled_1000

Time: ✅ 29.505ms (SLO: <34.550ms 📉 -14.6%) vs baseline: ~same

Memory: ✅ 38.437MB (SLO: <42.000MB -8.5%) vs baseline: +5.5%

✅ otelsdkspan - 24/24

✅ add-event

Time: ✅ 40.300ms (SLO: <42.000ms -4.0%) vs baseline: -0.7%

Memory: ✅ 37.611MB (SLO: <39.000MB -3.6%) vs baseline: +4.8%

✅ add-link

Time: ✅ 36.326ms (SLO: <38.550ms -5.8%) vs baseline: +0.1%

Memory: ✅ 37.650MB (SLO: <39.000MB -3.5%) vs baseline: +4.5%

✅ add-metrics

Time: ✅ 218.803ms (SLO: <232.000ms -5.7%) vs baseline: ~same

Memory: ✅ 37.591MB (SLO: <39.000MB -3.6%) vs baseline: +4.7%

✅ add-tags

Time: ✅ 212.310ms (SLO: <221.600ms -4.2%) vs baseline: +0.7%

Memory: ✅ 37.690MB (SLO: <39.000MB -3.4%) vs baseline: +5.0%

✅ get-context

Time: ✅ 29.031ms (SLO: <31.300ms -7.2%) vs baseline: -0.3%

Memory: ✅ 37.631MB (SLO: <39.000MB -3.5%) vs baseline: +4.5%

✅ is-recording

Time: ✅ 28.969ms (SLO: <31.000ms -6.6%) vs baseline: -0.7%

Memory: ✅ 37.670MB (SLO: <39.000MB -3.4%) vs baseline: +4.6%

✅ record-exception

Time: ✅ 63.179ms (SLO: <65.850ms -4.1%) vs baseline: ~same

Memory: ✅ 37.572MB (SLO: <39.000MB -3.7%) vs baseline: +4.5%

✅ set-status

Time: ✅ 31.757ms (SLO: <34.150ms -7.0%) vs baseline: -0.8%

Memory: ✅ 37.749MB (SLO: <39.000MB -3.2%) vs baseline: +5.0%

✅ start

Time: ✅ 29.272ms (SLO: <30.150ms -2.9%) vs baseline: +1.5%

Memory: ✅ 37.591MB (SLO: <39.000MB -3.6%) vs baseline: +4.8%

✅ start-finish

Time: ✅ 33.885ms (SLO: <35.350ms -4.1%) vs baseline: -0.6%

Memory: ✅ 37.768MB (SLO: <39.000MB -3.2%) vs baseline: +5.0%

✅ start-finish-telemetry

Time: ✅ 34.004ms (SLO: <35.450ms -4.1%) vs baseline: +0.1%

Memory: ✅ 37.709MB (SLO: <39.000MB -3.3%) vs baseline: +5.0%

✅ update-name

Time: ✅ 30.789ms (SLO: <33.400ms -7.8%) vs baseline: -2.0%

Memory: ✅ 37.591MB (SLO: <39.000MB -3.6%) vs baseline: +4.6%

✅ otelspan - 22/22

✅ add-event

Time: ✅ 40.172ms (SLO: <47.150ms 📉 -14.8%) vs baseline: -0.3%

Memory: ✅ 39.581MB (SLO: <47.000MB 📉 -15.8%) vs baseline: +5.1%

✅ add-metrics

Time: ✅ 259.416ms (SLO: <344.800ms 📉 -24.8%) vs baseline: -1.1%

Memory: ✅ 43.824MB (SLO: <47.500MB -7.7%) vs baseline: +4.5%

✅ add-tags

Time: ✅ 314.458ms (SLO: <321.000ms -2.0%) vs baseline: -0.7%

Memory: ✅ 43.862MB (SLO: <47.500MB -7.7%) vs baseline: +5.3%

✅ get-context

Time: ✅ 80.426ms (SLO: <92.350ms 📉 -12.9%) vs baseline: +0.3%

Memory: ✅ 39.971MB (SLO: <46.500MB 📉 -14.0%) vs baseline: +4.8%

✅ is-recording

Time: ✅ 37.966ms (SLO: <44.500ms 📉 -14.7%) vs baseline: +0.5%

Memory: ✅ 39.458MB (SLO: <47.500MB 📉 -16.9%) vs baseline: +4.7%

✅ record-exception

Time: ✅ 58.844ms (SLO: <67.650ms 📉 -13.0%) vs baseline: ~same

Memory: ✅ 39.923MB (SLO: <47.000MB 📉 -15.1%) vs baseline: +4.4%

✅ set-status

Time: ✅ 44.161ms (SLO: <50.400ms 📉 -12.4%) vs baseline: -0.6%

Memory: ✅ 39.485MB (SLO: <47.000MB 📉 -16.0%) vs baseline: +4.7%

✅ start

Time: ✅ 37.895ms (SLO: <43.450ms 📉 -12.8%) vs baseline: +2.0%

Memory: ✅ 39.447MB (SLO: <47.000MB 📉 -16.1%) vs baseline: +4.7%

✅ start-finish

Time: ✅ 82.902ms (SLO: <88.000ms -5.8%) vs baseline: ~same

Memory: ✅ 37.297MB (SLO: <46.500MB 📉 -19.8%) vs baseline: +4.9%

✅ start-finish-telemetry

Time: ✅ 84.114ms (SLO: <89.000ms -5.5%) vs baseline: -0.4%

Memory: ✅ 37.395MB (SLO: <46.500MB 📉 -19.6%) vs baseline: +4.9%

✅ update-name

Time: ✅ 38.800ms (SLO: <45.150ms 📉 -14.1%) vs baseline: ~same

Memory: ✅ 39.583MB (SLO: <47.000MB 📉 -15.8%) vs baseline: +4.9%

✅ packagespackageforrootmodulemapping - 4/4

✅ cache_off

Time: ✅ 341.905ms (SLO: <354.300ms -3.5%) vs baseline: -1.1%

Memory: ✅ 41.245MB (SLO: <43.500MB -5.2%) vs baseline: +4.7%

✅ cache_on

Time: ✅ 0.384µs (SLO: <10.000µs 📉 -96.2%) vs baseline: -0.1%

Memory: ✅ 39.575MB (SLO: <43.000MB -8.0%) vs baseline: +4.3%

ℹ️ Scenarios Missing SLO Configuration (26 scenarios)

The following scenarios exist in candidate data but have no SLO thresholds configured:

coreapiscenario-core_dispatch_listeners
coreapiscenario-core_dispatch_no_listeners
coreapiscenario-core_dispatch_with_results_listeners
coreapiscenario-core_dispatch_with_results_no_listeners
djangosimple-baseline
errortrackingdjangosimple-baseline
errortrackingflasksqli-baseline
flasksimple-baseline
flasksqli-baseline
sethttpmeta-obfuscation-disabled
startup-baseline
startup-baseline_django
startup-baseline_flask
startup-ddtrace_run
startup-ddtrace_run_appsec
startup-ddtrace_run_profiling
startup-ddtrace_run_runtime_metrics
startup-ddtrace_run_send_span
startup-ddtrace_run_telemetry_disabled
startup-ddtrace_run_telemetry_enabled
startup-import_ddtrace
startup-import_ddtrace_auto
startup-import_ddtrace_auto_django
startup-import_ddtrace_auto_flask
startup-import_ddtrace_django
startup-import_ddtrace_flask

Sep 30 '25 13:09 pr-commenter[bot]

@PROFeNoM probably worth updating the codeowners file as well to make llmobs the owner of this integration, will help require less people to review it (after the codeowners change is merged)

Oct 02 '25 18:10 brettlangdon

This pull request has been automatically closed after a period of inactivity. After this much time, it will likely be easier to open a new pull request with the same changes than to update this one from the base branch. Please comment or reopen if you think this pull request was closed in error.

Nov 18 '25 00:11 github-actions[bot]

@codex review

Dec 09 '25 08:12 PROFeNoM

Codex Review: Didn't find any major issues. What shall we delve into next?

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Dec 09 '25 09:12 chatgpt-codex-connector[bot]

@brettlangdon

I understand the concern about PR size. However, these components have dependencies that, I believe, make separate PRs truly impractical:

GPU CI setup is a prerequisite: I cannot run or validate vLLM tests without the GPU runner configuration. If split, I'd need to merge GPU CI first, then rebase vLLM onto it, losing the ability to iterate on both together. I'd anyway have to cherry-pick any new commit made on the hypothetical GPU CI setup PR to ensure proper behavior with the vLLM integration (which is its sole use case as of right now).
FastAPI pickle fix is required for testing: Without this fix in the same branch, I cannot (as easily, if at all) use the generated wheel and run local tests using Ray Serve. Splitting means cherry-picking any fixes between branches. I'd anyway have to cherry-pick any new commit made on the hypothetical fix PR to ensure proper behavior with the vLLM integration (which is its sole use case as of right now).
Revert coupling: If we ever need to revert either the GPU CI or pickle fix, we'd have to revert the vLLM integration too (it depends on both). And without vLLM, those infrastructure changes become dead code with no users.
Changes are isolated at file level — Each file contains changes for exactly one concern. There's no interleaved logic:
- .gitlab/*.yml, scripts/*, docker-compose.gpu.yml: GPU testing only
- fastapi/*: fastapi/wrapt/pickle fix only
- vllm/* and llmobs/*: vLLM integration only
- The other files are mostly just boilerplate, snapshots, requirements files etc.
CODEOWNERS: Are we really gonna do a separate PR for a 4 line change?

The cost of splitting (branch management, cherry-picks, rebases, reverts, time), imo, outweighs the benefit. I understand that splitting the PR might be prettier, but beauty is subjective.

Dec 17 '25 10:12 PROFeNoM