Metadata-Version: 1.2
Name: stackimpact
Version: 1.2.7
Summary: StackImpact Python Profiler
Home-page: https://stackimpact.com
Author: StackImpact
Author-email: devops@stackimpact.com
License: BSD
Description: StackImpact Python Profiler
        ===========================
        
        Overview
        --------
        
        StackImpact is a production-grade performance profiler built for both
        production and development environments. It gives developers continuous
        and historical code-level view of application performance that is
        essential for locating CPU, memory allocation and I/O hot spots as well
        as latency bottlenecks. Included runtime metrics and error monitoring
        complement profiles for extensive performance analysis. Learn more at
        `stackimpact.com <https://stackimpact.com/>`__.
        
        .. figure:: https://stackimpact.com/img/readme/hotspots-cpu-1.4-python.png
           :alt: dashboard
        
           dashboard
        
        Features
        ^^^^^^^^
        
        -  Continuous hot spot profiling of CPU usage, memory allocation and
           blocking calls.
        -  Error and exception monitoring.
        -  Health monitoring including CPU, memory, garbage collection and other
           runtime metrics.
        -  Alerts on profile anomalies.
        -  Team access.
        
        Learn more on the `features <https://stackimpact.com/features/>`__ page
        (with screenshots).
        
        How it works
        ^^^^^^^^^^^^
        
        The StackImpact profiler agent is imported into a program and used as a
        normal package. When the program runs, various sampling profilers are
        started and stopped automatically by the agent and/or programmatically
        using the agent methods. The agent periodically reports recorded
        profiles and metrics to the StackImpact Dashboard. The agent can also
        operate in manual mode, which should be used in development only.
        
        Documentation
        ^^^^^^^^^^^^^
        
        See full `documentation <https://stackimpact.com/docs/>`__ for
        reference.
        
        Supported environment
        ---------------------
        
        -  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.
        
        Getting started
        ---------------
        
        Create StackImpact account
        ^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        Sign up for a free trial account at
        `stackimpact.com <https://stackimpact.com>`__ (also with GitHub login).
        
        Installing the agent
        ^^^^^^^^^^^^^^^^^^^^
        
        Install the Python agent by running
        
        ::
        
            pip install stackimpact
        
        And import the package in your application
        
        .. code:: python
        
            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.
        
        .. code:: python
        
            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, 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.
        -  ``auto_profiling`` (Optional) If set to ``False``, disables automatic
           profiling and reporting. Focused or manual profiling should be used
           instead. Useful for environments without support for timers or
           background tasks.
        -  ``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`` (Optional) Set to ``True`` to not exclude
           agent stack frames from profile call graphs.
        -  ``auto_destroy`` (Optional) Set to ``False`` to disable agent's exit
           handlers. If necessary, call ``destroy()`` to gracefully shutdown the
           agent.
        
        Focused profiling
        ^^^^^^^^^^^^^^^^^
        
        Use ``agent.profile(name)`` to instruct the agent when to start and stop
        profiling. The agent decides if and which profiler is activated.
        Normally, this method should be used in repeating code, such as request
        or event handlers. In addition to more precise profiling, timing
        information will also be reported for the profiled spans. Usage example:
        
        .. code:: python
        
            span = agent.profile('span1');
        
            # your code here
        
            span.stop();
        
        Alternatively, a ``with`` statement can be used:
        
        .. code:: python
        
            with agent.profile('span1'):
                # your code ehere
        
        Manual profiling
        ^^^^^^^^^^^^^^^^
        
        *Manual profiling should not be used in production!*
        
        By default, the agent starts and stops profiling automatically. Manual
        profiling allows to start and stop profilers directly. It is suitable
        for profiling short-lived programs and should not be used for
        long-running production applications. Automatic profiling should be
        disabled with ``auto_profiling: False``.
        
        .. code:: python
        
            # Start CPU profiler.
            agent.start_cpu_profiler();
        
        .. code:: python
        
            # Stop CPU profiler and report the recorded profile to the Dashboard.
            agent.stop_cpu_profiler();
        
        .. code:: python
        
            # Start blocking call profiler.
            agent.start_block_profiler();
        
        .. code:: python
        
            # Stop blocking call profiler and report the recorded profile to the Dashboard.
            agent.stop_block_profiler();
        
        .. code:: python
        
            # Start heap allocation profiler.
            agent.start_allocation_profiler();
        
        .. code:: python
        
            # Stop heap allocation profiler and report the recorded profile to the Dashboard.
            agent.stop_allocation_profiler();
        
        Analyzing performance data in the Dashboard
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        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 <https://dashboard.stackimpact.com/>`__.
        
        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.
        
Keywords: cpu profiler,memory profiler,blocking call profilererror monitoringhealth metricsgarbage collection metrics
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Web Environment
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: System Administrators
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Software Development
Classifier: Topic :: System :: Monitoring
Requires-Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, <4
