StackImpact Python Agent
Project description
Overview
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.
Features
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).
Documentation
See full documentation for reference.
Requirements
Linux, OS X or Windows. Python version 2.7, 3.4 or higher.
Memorly allocation profiler and some GC metrics are only available for Python 3.
CPU and Time profilers only supports Linux and OS X.
Time (blocking call) profiler supports threads and gevent.
Getting started
Create StackImpact account
Sign up for a free account at stackimpact.com.
Installing the agent
Install the Go agent by running
pip install stackimpact
And import the package in your application
import stackimpact
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',
Other initialization options:
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.
Analyzing performance data in the Dashboard
Once your application is restarted, you can start observing regular and anomaly-triggered CPU, memory, I/O, and other hot spot profiles, execution bottlenecks as well as process metrics in the Dashboard.
Troubleshooting
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.
Overhead
The agent overhead is measured to be less than 1% for applications under high load.
Project details
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