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- 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.
- CPU and Time profilers only support Linux and OS X.
- Time (blocking call) profiler supports threads and gevent.
Installing the agent
Install the Python agent by running
pip install stackimpact
And import the package in your application
Configuring the agent
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')
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, deployable unit or executable module 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.
error_profiler_disabled(Optional) Disables respective profiler when
include_system_frames(Optional) Set to
Trueto not exclude agent and/or system stack frames from profiles.
auto_destroy(Optional) Set to
Falseto disable agent’s exit handlers. If necessary, call
destroy()to gracefully shutdown the agent.
Analyzing performance data in the Dashboard
Once your application is restarted, you can start observing continuously recorded 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.