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Commit ac55510

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dmelikyandmelikyan
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version 1.0.0
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‎.gitignore

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.DS_Store
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._
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._.*
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conf
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*.pyc
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build
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.bin
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log/*
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logs/*
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!.gitkeep
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tmp
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.tmp
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dist
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stackimpact.egg-info
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__pycache__

‎LICENSE.txt

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Copyright (c) 2017, StackImpact GmbH. All rights reserved.
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions are met:
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* Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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* Redistributions in binary form must reproduce the above copyright
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notice, this list of conditions and the following disclaimer in the
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documentation and/or other materials provided with the distribution.
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* Neither the name of the StackImpact GmbH nor the
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names of its contributors may be used to endorse or promote products
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derived from this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL <COPYRIGHT HOLDER> BE LIABLE FOR ANY
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DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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‎README.md

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# StackImpact Python Agent
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## Overview
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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](https://stackimpact.com/).
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![dashboard](https://stackimpact.com/wp-content/uploads/2017/06/hotspots-cpu-1.4-python.png)
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#### Features
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* Automatic hot spot profiling for CPU, memory allocations, blocking calls
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* Automatic bottleneck tracing for HTTP handlers and other libraries
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* Exception monitoring
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* Health monitoring including CPU, memory, garbage collection and other runtime metrics
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* Anomaly alerts on most important metrics
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* Multiple account users for team collaboration
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Learn more on the [features](https://stackimpact.com/features/) page (with screenshots).
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#### Documentation
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See full [documentation](https://stackimpact.com/docs/) for reference.
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## Requirements
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* Linux, OS X or Windows. Python version 2.7, 3.4 or higher.
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* Memorly allocation profiler and some GC metrics are only available for Python 3.
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* CPU and Time profilers only supports Linux and OS X.
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* Time (blocking call) profiler supports threads and gevent.
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## Getting started
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#### Create StackImpact account
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Sign up for a free account at [stackimpact.com](https://stackimpact.com/).
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#### Installing the agent
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Install the Go agent by running
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```
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pip install stackimpact
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```
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And import the package in your application
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```python
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import stackimpact
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```
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#### Configuring the agent
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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.
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```python
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agent = stackimpact.start(
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agent_key = 'agent key here',
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app_name = 'MyPythonApp',
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```
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Other initialization options:
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* `app_version` (Optional) Sets application version, which can be used to associate profiling information with the source code release.
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* `app_environment` (Optional) Used to differentiate applications in different environments.
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* `host_name` (Optional) By default, host name will be the OS hostname.
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* `debug` (Optional) Enables debug logging.
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#### Analyzing performance data in the Dashboard
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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](https://dashboard.stackimpact.com/).
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#### Troubleshooting
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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.
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## Overhead
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The agent overhead is measured to be less than 1% for applications under high load.

‎README.rst

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StackImpact Python Agent
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========================
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Overview
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--------
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StackImpact is a performance profiler for production applications. It
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gives developers continuous and historical view of application
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performance with line-of-code precision, which includes CPU, memory
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allocation and blocking call hot spots as well as execution bottlenecks,
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errors and runtime metrics. Learn more at
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`stackimpact.com <https://stackimpact.com/>`__.
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.. figure:: https://stackimpact.com/wp-content/uploads/2017/06/hotspots-cpu-1.4-python.png
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:alt: dashboard
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dashboard
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Features
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^^^^^^^^
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- Automatic hot spot profiling for CPU, memory allocations, blocking
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calls
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- Automatic bottleneck tracing for HTTP handlers and other libraries
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- Exception monitoring
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- Health monitoring including CPU, memory, garbage collection and other
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runtime metrics
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- Anomaly alerts on most important metrics
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- Multiple account users for team collaboration
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Learn more on the `features <https://stackimpact.com/features/>`__ page
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(with screenshots).
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Documentation
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^^^^^^^^^^^^^
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See full `documentation <https://stackimpact.com/docs/>`__ for
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reference.
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Requirements
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------------
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- Linux, OS X or Windows. Python version 2.7, 3.4 or higher.
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- Memorly allocation profiler and some GC metrics are only available
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for Python 3.
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- CPU and Time profilers only supports Linux and OS X.
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- Time (blocking call) profiler supports threads and gevent.
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Getting started
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---------------
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Create StackImpact account
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^^^^^^^^^^^^^^^^^^^^^^^^^^
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Sign up for a free account at
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`stackimpact.com <https://stackimpact.com/>`__.
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Installing the agent
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^^^^^^^^^^^^^^^^^^^^
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Install the Go agent by running
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::
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pip install stackimpact
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And import the package in your application
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.. code:: python
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import stackimpact
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Configuring the agent
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^^^^^^^^^^^^^^^^^^^^^
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Start the agent in the main thread by specifying the agent key and
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application name. The agent key can be found in your account's
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Configuration section.
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.. code:: python
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agent = stackimpact.start(
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agent_key = 'agent key here',
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app_name = 'MyPythonApp',
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Other initialization options:
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- ``app_version`` (Optional) Sets application version, which can be
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used to associate profiling information with the source code release.
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- ``app_environment`` (Optional) Used to differentiate applications in
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different environments.
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- ``host_name`` (Optional) By default, host name will be the OS
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hostname.
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- ``debug`` (Optional) Enables debug logging.
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Analyzing performance data in the Dashboard
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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Once your application is restarted, you can start observing regular and
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anomaly-triggered CPU, memory, I/O, and other hot spot profiles,
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execution bottlenecks as well as process metrics in the
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`Dashboard <https://dashboard.stackimpact.com/>`__.
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Troubleshooting
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^^^^^^^^^^^^^^^
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To enable debug logging, add ``debug = True`` to startup options. If the
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debug log doesn't give you any hints on how to fix a problem, please
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report it to our support team in your account's Support section.
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Overhead
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--------
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The agent overhead is measured to be less than 1% for applications under
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high load.

‎examples/flask_app.py

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# export AGENT_KEY=agent_key_here
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# gunicorn --workers 2 --bind 127.0.0.1:5010 flask_app:app
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# gunicorn --workers 2 -k gevent --bind 127.0.0.1:5010 flask_app:app
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from __future__ import print_function
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import os
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import time
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import sys
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import threading
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import subprocess
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import collections
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import random
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import traceback
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from flask import Flask
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try:
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# python 2
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from urllib2 import urlopen
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except ImportError:
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# python 3
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from urllib.request import urlopen
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import stackimpact
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# StackImpact agent initialization
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agent = stackimpact.start(
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agent_key = os.environ['AGENT_KEY'],
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app_name = 'ExamplePythonFlaskApp',
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app_version = '1.0.0',
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debug = True)
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# Simulate CPU intensive work
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def simulate_cpu():
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duration = 10 * 60 * 60
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usage = 20
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while True:
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for j in range(0, duration):
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for i in range(0, usage * 20000):
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text = "text1" + str(i)
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text = text + "text2"
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time.sleep(1 - usage/100)
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t = threading.Thread(target=simulate_cpu)
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t.start()
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# Simulate memory leak
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def simulate_mem_leak():
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while True:
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mem1 = []
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for j in range(0, 1800):
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mem2 = []
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for i in range(0, 1000):
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obj1 = {'v': random.randint(0, 1000000)}
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mem1.append(obj1)
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obj2 = {'v': random.randint(0, 1000000)}
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mem2.append(obj2)
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time.sleep(1)
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t = threading.Thread(target=simulate_mem_leak)
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t.start()
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# Simulate lock
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def simulate_lock():
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lock = threading.Lock()
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def lock_wait():
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lock.acquire()
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lock.release()
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while True:
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lock.acquire()
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t = threading.Thread(target=lock_wait)
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t.start()
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time.sleep(1)
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lock.release()
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time.sleep(1)
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t = threading.Thread(target=simulate_lock)
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t.start()
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# Simulate exceptions
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def simulate_exceptions():
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while True:
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try:
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raise ValueError('some error')
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except:
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traceback.print_exc()
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pass
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time.sleep(2)
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t = threading.Thread(target=simulate_exceptions)
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t.start()
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# Simulate http server
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def simulate_http_traffic():
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while True:
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try:
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urlopen('http://localhost:5010', timeout=10)
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time.sleep(2)
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except:
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traceback.print_exc()
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pass
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t = threading.Thread(target=simulate_http_traffic)
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t.start()
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def cpu_work():
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for i in range(0, 1000000):
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text = "text1" + str(i)
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text = text + "text2"
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app = Flask(__name__)
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@app.route('/')
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def hello_world():
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time.sleep(0.5)
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cpu_work()
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return 'Hello'
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