StackImpact is a performance profiler for production applications. It gives developers continuous and historical view of application performance with line-of-code precision, which includes CPU, memory allocation and blocking call hot spots as well as execution bottlenecks, errors and runtime metrics. Learn more at stackimpact.com.

• Automatic hot spot profiling for CPU, memory allocations, blocking calls
• Automatic bottleneck tracing for HTTP handlers and other libraries
• Exception monitoring
• Health monitoring including CPU, memory, garbage collection and other runtime metrics
• Anomaly alerts on most important metrics
• Multiple account users for team collaboration

Learn more on the features page (with screenshots).

See full documentation for reference.

• Linux, OS X or Windows. Python version 2.7, 3.4 or higher.
• Memory allocation profiler and some GC metrics are only available for Python 3.
• Profilers only support Linux and OS X.
• Time (blocking call) profiler supports threads and gevent.
• On unix systems the profilers use the following signals: SIGPROF, SIGALRM, SIGUSR2. Only SIGUSR2 is handled transparently, i.e. it should not conflict with previousely registered handlers.

Sign up for a free account (also with GitHub login).

Install the Python agent by running

pip install stackimpact

And import the package in your application

import stackimpact

Start the agent in the main thread by specifying the agent key and application name. The agent key can be found in your account's Configuration section.

agent = stackimpact.start(
    agent_key = 'agent key here',
    app_name = 'MyPythonApp')

Add the agent initialization to the worker code, e.g. wsgi.py, if applicable.

All initialization options:

• agent_key (Required) The access key for communication with the StackImpact servers.
• app_name (Required) A name to identify and group application data. Typically, a single codebase corresponds to one application.
• app_version (Optional) Sets application version, which can be used to associate profiling information with the source code release.
• app_environment (Optional) Used to differentiate applications in different environments.
• host_name (Optional) By default, host name will be the OS hostname.
• debug (Optional) Enables debug logging.
• cpu_profiler_disabled, allocation_profiler_disabled, block_profiler_disabled, error_profiler_disabled (Optional) Disables respective profiler when True.
• include_agent_frames, include_system_frames (Optional) Set to True to not exclude agent and/or system stack frames from profiles.

Once your application is restarted, you can start observing continuous CPU, memory, I/O, and other hot spot profiles, execution bottlenecks as well as process metrics in the Dashboard.

To enable debug logging, add debug = True to startup options. If the debug log doesn't give you any hints on how to fix a problem, please report it to our support team in your account's Support section.

The agent overhead is measured to be less than 1% for applications under high load.