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_ENABLEDandDD_EXPERIMENTAL_APPSEC_STANDALONE_ENABLED
- Introduces "Standalone SCA billing", opting out for APM billing and applying to only SCA. Enable this by setting these two environment variables:
Code Security
- Introduces stack trace reports for 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.
- Adds an experimental integration with the PyTorch profiler which can be enabled by setting
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.
- Resolves an issue where AppSec was using a patched request and builtins functions, creating telemetry errors.
LLM Observability
- Resolves an issue where
LLMObs.enable()ignored global patch configurations, specifically theDD_TRACE_<INTEGRATION>_ENABLEDandDD_PATCH_MODULESenvironment variables.
- Resolves an issue where
Telemetry
- Resolves deadlocks that could occur when sending instrumentation telemetry data after an unhandled exception is raised.
- Resolves deadlocks that could occur when sending instrumentation telemetry data after an unhandled exception is raised.
Tracing
datastreams: Logs at warning level for Kinesis errors that break the Data Streams Monitoring map.