Python Profiling Tools & Monitoring Solutions

Monitoring Python performance with AppDynamics allows you to collect critical runtime metrics, understand end-to-end transaction flows of your python code, and identify performance issues across highly distributed applications while running in a live production environment.

Types of profiling & monitoring tools: what you need

Effective application monitoring and management requires a comprehensive understanding of your application, infrastructure, and user experience. As the architecture of applications grow in complexity and obscures visibility, it adds to the challenge of efficient root cause analysis. Selecting the proper combination of Python profiling & monitoring tools can facilitate troubleshooting and allow for in-depth visibility that supports optimization efforts.

Review the following options for Python profiling:

Trace module

Tracing is a technique used by software developers that involves collecting information about a software execution through the use of logging. 

Python includes a trace module that records functions as they are executed to produce reports that help assess performance issues. The trace module can report on which lines of code were run or skipped, the relationships between functions that call each other, and determine which function is the caller.

Faulthandler module

The faulthandler module is slightly more complex than the trace module. The two modules are similar since they both have tracing abilities which can facilitate debugging Python code, but the faulthandler specifically dumps Python tracebacks if it encounters a fault, timeout, or a user signal. It can also be used in conjunction with other system fault handlers. 

The faulthandler module has limitations due to the fact that it can only use signal-safe functions and cannot allocate memory on the heap.

Application performance monitoring (APM tools)

APM tools offer a low overhead profiling method that monitors and manages your application in production without affecting performance. The development environment is limited in its ability to realistically recreate the often unpredictable conditions which may be encountered during production. 

Understanding your application’s performance in production is crucial to creating a seamless user experience, and APM can provide more clarity and insight into performance issues and user behavior throughout the entire life cycle of development and deployment.

Benefits of using AppDynamics for Python APM

Easier insight into complex environments

Improve the visibility of highly distributed and dynamic application architectures, such as cloud and microservices. Python performance monitoring provides greater insight into response times and application topology. This helps pinpoint the root cause of performance bottlenecks and quickly optimize code to increase efficiency while enhancing the user experience and business outcomes.

Identify and address performance issues

Python performance monitoring helps identify and solve performance issues by tracking metrics execution times, from the front-end all the way down to the back-end, and delivers user experiences and business impacts. This is done by injecting lightweight instrumentation into the Python program as it runs in production or during testing. The collected traces and data report back to a controller, where the data is stored, analyzed, and compiled.

Improve visibility with minimal overhead

Expedite the troubleshooting process and improve accuracy with real-time visibility of performance in production without burdening resources. AppDynamics consolidates the internal components of your Python application and infrastructure into a single view with process-level code visibility. Detailed data and proactive monitoring support more in-depth analysis, faster troubleshooting, and optimization of performance and capacity.

Tailor your APM with API functions

Add functions to your application code to monitor components that would not be automatically detected by the agent. Programmatically define custom business transactions or create custom exit calls to discover and monitor backend technologies.

Gain greater awareness through data

The AppDynamics Python agent reveals application errors within the business transaction traces, identifies resource-intensive code by generating call graphs for individual transactions, monitors critical runtime metrics, and provides a greater understanding of end-to-end transaction flows in complex application environments.

Python supported environments

AppDynamics provides Python profiling capabilities for CPython 2.6, 2.7, 3.4, 3.5, 3.6, and 3.7 on WSGI and
Tornado frameworks, including but not limited to: 

  • Bottle
  • PasteDeploy
  • CherryPy
  • Pyramid
  • Django
  • Flask

“It was simple to deploy. It took minutes, literally. On our first proof-of-concept, we saw results within an hour.” 

Eric Poon, Director of Operations Analytics, Nasdaq 

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