diff --git a/CHANGELOG.md b/CHANGELOG.md index 2039d597f9a..89ee0832b61 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,33 @@ Changelogs for versions not listed here can be found at https://github.com/DataDog/dd-trace-py/releases +--- + +## 2.19.0 + +### New Features + +- azure_functions: This introduces support for Azure Functions. +- ASM: This 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` +- 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 () so that GPU profiling data can be sent to Datadog for visualization. This feature supports torch version \>= 1.8.1. +- Code Security: This introduces stack trace reports for Code Security. +### Upgrade Notes + +- Makes the library compatible with Python 3.13 +### Bug Fixes + +- ASGI: This fix resolves an issue parsing response cookies in FastAPI and awsgi- lib-injection: Fix missing lib-injection telemetry for common abort scenarios. + +- LLM Observability: This fix resolves an issue where `LLMObs.enable()` ignored global patch configurations, specifically + the `DD_TRACE__ENABLED` and `DD_PATCH_MODULES` environment variables. + +- ASM: This fix resolves an issue where AppSec was using a patched request and builtins functions, creating telemetry errors. + +- datastreams: Logs at warning level for Kinesis errors that break the Data Streams Monitoring map. + +- library: Resolves deadlocks that could occur when sending instrumentation telemetry data after an unhandled exception is raised. + + --- ## 2.18.1