Python Application Monitoring

Python monitoring provides code-level visibility into the health and performance of your services, allowing you to quickly troubleshoot any issue—whether it's related to coroutines, asynchronous tasks, or runtime metrics. A Python monitoring solution can also continuously profile your code and seamlessly correlate profiles with all distributed request traces and other telemetry data for faster debugging and root cause analysis.

Monitor distributed Python applications

Find and fix regressions by tracking and comparing hits, errors and latency of every deployed version of your Python services
Analyze concurrently executed Python asynchronous tasks and identify which code paths are slow or producing errors
Effectively prioritize investigation of Python application errors and their stack traces by automatically grouping them into issues

Support for all common Python frameworks

see our docs

Optimize your code with Python monitoring

Capture profiles from every Python service to reduce time spent on parked threads, garbage collection, and context switches
Detect CPU, memory, and lock inefficiencies in your Python code—without the high overhead of running cProfile
Improve the performance of your Python methods by comparing code profiles across versions and instances

リソース

official docs

Tracing Python Applications

BLOG

Tracing asynchronous Python code with Datadog APM

integration

APM

BLOG

Python logging formats: How to collect and centralize Python logs

best practices

log management

BLOG

Instrument your Python applications with Datadog and OpenTelemetry

tutorial

APM

Python performance insights with
minimal engineering effort

With automatic code instrumentation, machine learning-based alerts, out-of-the-box dashboards, and seamless correlation of telemetry data, Datadog helps you quickly start monitoring Python applications and reducing your compute costs.