Datadog Python Debugging

Debug Python Applications Faster

Detect, diagnose, and resolve issues impacting end users in seconds. Seamlessly correlate distributed traces with metrics, logs, browser sessions, code profiles, synthetics, and network performance data.

Why Datadog?

End-to-End Distributed Tracing

Set up in minutes, and instantly visualize your traces with full control over ingestion, retention, and costs

Code Hotspots And Thread Level Insights

Optimize the most resource consuming lines of production code with our low-overhead Continuous Profiler

Automatic Deployment Tracking

Requests, errors, and latency metrics for every code deploy including out-of-the-box code version comparisons

Superior Granularity & Retention

0-second OOTB high resolution, 1-second availability for critical metrics, 15-month retention, and no roll-up, ever

Product Benefits

Simplify Complexity with End-to-End Visibility

Monitor all aspects of your application architecture from the user interaction to back-end storage - in a single platform
Easily identify bottlenecks, errors, heavy traffic issues, slow-running queries, and more with end-to-end application tracing, latency breakdowns, continuous profiling, and real user monitoring
Automatically collect, monitor, and visualize high-granularity data and custom metrics in real time, including availability, response times, reliability, error rates, and throughput

Debug Python Issues Faster

Identify critical issues quickly with real-time service maps, AI-powered synthetic monitors, and alerts on latency, exceptions, code-level errors, log issues, and more
Resolve detected Python problems faster with distributed request traces, logs, and infrastructure metrics all within one platform
Test hypotheses in seconds by overlaying application events onto time-synchronized metric graphs

Optimize Python Performance

Detect the most resource-consuming methods or classes in your Python applications in seconds with a lightweight, next-generation profiler that’s easy-to-use and always-on
Determine the root cause of code issues with a breakdown of time spent by method on CPU, garbage collection, lock contention, and I/O; improve CPU utilization, memory allocation, and user latency
Surface runtime performance problems such as deadlocked threads, inefficient garbage collection, and memory leaks

Receive Alerts Only for the Issues that Matter and Eliminate False-Positives

Set up recommended alerts with 1 click for anomalies and outliers that account for daily, weekly, and seasonal fluctuations
Proactively prevent outages and errors in the future by alerting on metric forecasts
Combine alerts into composite alerts for greater granularity and stronger signal to reduce the noise
Automatically detect unanticipated outliers, anomalies, and errors with Watchdog

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Spend Less Time Scaling and Maintaining Your Tools

Save time scaling and maintaining your open source tools as complexity grows within your stack
Automatically instrument your applications for popular Python frameworks such as Django, Pyramid, Flask, and Tornado
Efficiently analyze issues from any perspective with automated tagging integrated throughout the entire platform