===== Datadog ddtrace ===== **ddtrace** is the Datadog APM (Application Performance Monitoring) Python library that enables tracing of your applications to monitor performance, latency, and errors in real-time. With ddtrace, you can gain detailed insights into your application's behavior, optimize its performance, and troubleshoot issues quickly. ==== Installation ==== To install `ddtrace`, you need to install the Python package via `pip`: pip install ddtrace Once installed, you can begin instrumenting your application. ==== Basic Usage ==== After installation, you need to import ddtrace and initialize it. The easiest way to start tracing is by adding this at the entry point of your application: from ddtrace import tracer # Example: tracing a simple function @tracer.wrap() def my_function(): # Function logic pass Alternatively, you can use ddtrace to automatically instrument various libraries, such as Flask, Django, Celery, and more, with minimal configuration. from ddtrace import patch_all patch_all() # This will automatically trace supported libraries ==== Configuration ==== The Datadog tracer can be configured with several options, such as setting the agent host, enabling/disabling certain integrations, and adjusting sampling rates. Here are some common configuration options: * Datadog Agent Host: * Set the Datadog Agent host to which traces should be sent. from ddtrace import tracer tracer.configure(hostname='localhost', port=8126) ==== Supported Libraries ==== ddtrace supports a wide range of libraries and frameworks out of the box. Some popular integrations include: * Flask: For tracing HTTP requests in Flask applications. * Django: For monitoring Django-based applications. * Celery: For tracing background tasks with Celery. * SQLAlchemy: For tracing database queries. * Redis: For tracing Redis interactions. To enable tracing for these frameworks, simply import the relevant module, and ddtrace will automatically instrument them.