ddtrace 2.19.0

on Python PyPI

New Features

• ASM

  • Introduces "Standalone SCA billing", opting out for APM billing and applying to only SCA. Enable this by setting these two environment variables: DD_APPSEC_SCA_ENABLED and DD_EXPERIMENTAL_APPSEC_STANDALONE_ENABLED

• Code Security

  • Introduces stack trace reports for Code Security.

• Profiling

  • Adds an experimental integration with the PyTorch profiler which can be enabled by setting DD_PROFILING_PYTORCH_ENABLED=true. This feature instruments the PyTorch profiler API (https://pytorch.org/docs/stable/_modules/torch/profiler/profiler.html) so that GPU profiling data can be sent to Datadog for visualization. This feature supports torch version >= 1.8.1.

• Tracing

  • azure_functions: Introduces support for Azure Functions.

Upgrade Notes

• Makes the library compatible with Python 3.13

Bug Fixes

• ASM

  • Resolves an issue where AppSec was using a patched request and builtins functions, creating telemetry errors.

• Code Security

  • Adds more modules to the IAST patching denylist to improve startup time

• Lib-Injection

  • Fixes missing lib-injection telemetry for common abort scenarios.

• LLM Observability

  • Resolves an issue where LLMObs.enable() ignored global patch configurations, specifically
    the DD_TRACE_<INTEGRATION>_ENABLED and DD_PATCH_MODULES environment variables.

• Telemetry

  • library: Resolves deadlocks that could occur when sending instrumentation telemetry data after an unhandled exception is raised.

• Tracing

  • ASGI: This fix resolves an issue parsing response cookies in FastAPI and awsgi
  • asyncio: Resolves an issue where asyncio event loops fail to register when ddtrace-run/import ddtrace.auto is used and gevent is installed.
  • datastreams: Logs at warning level for Kinesis errors that break the Data Streams Monitoring map.