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=================
Debugging PySpark
=================

PySpark uses Spark as an engine. PySpark uses `Py4J <https://www.py4j.org/>`_ to leverage Spark to submit and computes the jobs.

On the driver side, PySpark communicates with the driver on JVM by using `Py4J <https://www.py4j.org/>`_.
When :class:`pyspark.sql.SparkSession` or :class:`pyspark.SparkContext` is created and initialized, PySpark launches a JVM
to communicate.

On the executor side, Python workers execute and handle Python native functions or data. They are not launched if
a PySpark application does not require interaction between Python workers and JVMs. They are lazily launched only when
Python native functions or data have to be handled, for example, when you execute pandas UDFs or
PySpark RDD APIs.

This page focuses on debugging Python side of PySpark on both driver and executor sides instead of focusing on debugging
with JVM. Profiling and debugging JVM is described at `Useful Developer Tools <https://spark.apache.org/developer-tools.html>`_.

Note that,

- If you are running locally, you can directly debug the driver side via using your IDE without the remote debug feature. Setting PySpark with IDEs is documented `here <setting_ide.rst#pycharm>`__.
- *There are many other ways of debugging PySpark applications*. For example, you can remotely debug by using the open source `Remote Debugger <https://www.pydev.org/manual_adv_remote_debugger.html>`_ instead of using PyCharm Professional documented here.


Remote Debugging (PyCharm Professional)
---------------------------------------

This section describes remote debugging on both driver and executor sides within a single machine to demonstrate easily.
The ways of debugging PySpark on the executor side is different from doing in the driver. Therefore, they will be demonstrated respectively.
In order to debug PySpark applications on other machines, please refer to the full instructions that are specific
to PyCharm, documented `here <https://www.jetbrains.com/help/pycharm/remote-debugging-with-product.html>`_.

Firstly, choose **Edit Configuration...** from the *Run* menu. It opens the **Run/Debug Configurations dialog**.
You have to click ``+`` configuration on the toolbar, and from the list of available configurations, select **Python Debug Server**.
Enter the name of this new configuration, for example, ``MyRemoteDebugger`` and also specify the port number, for example ``12345``.

.. image:: ../../../../docs/img/pyspark-remote-debug1.png
    :alt: PyCharm remote debugger setting

| After that, you should install the corresponding version of the ``pydevd-pycharm`` package in all the machines which will connect to your PyCharm debugger. In the previous dialog, it shows the command to install.

.. code-block:: text

    pip install pydevd-pycharm~=<version of PyCharm on the local machine>

Driver Side
~~~~~~~~~~~

To debug on the driver side, your application should be able to connect to the debugging server. Copy and paste the codes
with ``pydevd_pycharm.settrace`` to the top of your PySpark script. Suppose the script name is ``app.py``:

.. code-block:: bash

    echo "#======================Copy and paste from the previous dialog===========================
    import pydevd_pycharm
    pydevd_pycharm.settrace('localhost', port=12345, stdoutToServer=True, stderrToServer=True)
    #========================================================================================
    # Your PySpark application codes:
    from pyspark.sql import SparkSession
    spark = SparkSession.builder.getOrCreate()
    spark.range(10).show()" > app.py

Start to debug with your ``MyRemoteDebugger``.

.. image:: ../../../../docs/img/pyspark-remote-debug2.png
    :alt: PyCharm run remote debugger

| After that, submit your application. This will connect to your PyCharm debugging server and enable you to debug on the driver side remotely.

.. code-block:: bash

    spark-submit app.py

Executor Side
~~~~~~~~~~~~~

To debug on the executor side, prepare a Python file as below in your current working directory.

.. code-block:: bash

    echo "from pyspark import daemon, worker
    def remote_debug_wrapped(*args, **kwargs):
        #======================Copy and paste from the previous dialog===========================
        import pydevd_pycharm
        pydevd_pycharm.settrace('localhost', port=12345, stdoutToServer=True, stderrToServer=True)
        #========================================================================================
        worker.main(*args, **kwargs)
    daemon.worker_main = remote_debug_wrapped
    if __name__ == '__main__':
        daemon.manager()" > remote_debug.py

You will use this file as the Python worker in your PySpark applications by using the ``spark.python.daemon.module`` configuration.
Run the ``pyspark`` shell with the configuration below:

.. code-block:: bash

    pyspark --conf spark.python.daemon.module=remote_debug

Now you're ready to remotely debug. Start to debug with your ``MyRemoteDebugger``.

.. image:: ../../../../docs/img/pyspark-remote-debug2.png
    :alt: PyCharm run remote debugger

| After that, run a job that creates Python workers, for example, as below:

.. code-block:: python

    spark.range(10).repartition(1).rdd.map(lambda x: x).collect()


Checking Resource Usage (``top`` and ``ps``)
--------------------------------------------

The Python processes on the driver and executor can be checked via typical ways such as ``top`` and ``ps`` commands.

Driver Side
~~~~~~~~~~~

On the driver side, you can get the process id from your PySpark shell easily as below to know the process id and resources.

.. code-block:: python

    >>> import os; os.getpid()
    18482

.. code-block:: bash

    ps -fe 18482

.. code-block:: text

    UID   PID  PPID   C STIME  TTY           TIME CMD
    000 18482 12345   0 0:00PM ttys001    0:00.00 /.../python

Executor Side
~~~~~~~~~~~~~

To check on the executor side, you can simply ``grep`` them to figure out the process
ids and relevant resources because Python workers are forked from ``pyspark.daemon``.

.. code-block:: bash

    ps -fe | grep pyspark.daemon

.. code-block:: text

    000 12345     1   0  0:00PM ttys000    0:00.00 /.../python -m pyspark.daemon
    000 12345     1   0  0:00PM ttys000    0:00.00 /.../python -m pyspark.daemon
    000 12345     1   0  0:00PM ttys000    0:00.00 /.../python -m pyspark.daemon
    000 12345     1   0  0:00PM ttys000    0:00.00 /.../python -m pyspark.daemon
    ...


Profiling Memory Usage (Memory Profiler)
----------------------------------------

`memory_profiler <https://github.com/pythonprofilers/memory_profiler>`_ is one of the profilers that allow you to
check the memory usage line by line. This method documented here *only works for the driver side*.

Unless you are running your driver program in another machine (e.g., YARN cluster mode), this useful tool can be used
to debug the memory usage on driver side easily. Suppose your PySpark script name is ``profile_memory.py``.
You can profile it as below.

.. code-block:: bash

    echo "from pyspark.sql import SparkSession
    #===Your function should be decorated with @profile===
    from memory_profiler import profile
    @profile
    #=====================================================
    def my_func():
        session = SparkSession.builder.getOrCreate()
        df = session.range(10000)
        return df.collect()
    if __name__ == '__main__':
        my_func()" > profile_memory.py

.. code-block:: bash

    python -m memory_profiler profile_memory.py

.. code-block:: text

    Filename: profile_memory.py

    Line #    Mem usage    Increment   Line Contents
    ================================================
    ...
         6                             def my_func():
         7     51.5 MiB      0.6 MiB       session = SparkSession.builder.getOrCreate()
         8     51.5 MiB      0.0 MiB       df = session.range(10000)
         9     54.4 MiB      2.8 MiB       return df.collect()


Identifying Hot Loops (Python Profilers)
----------------------------------------

`Python Profilers <https://docs.python.org/3/library/profile.html>`_ are useful built-in features in Python itself. These
provide deterministic profiling of Python programs with a lot of useful statistics. This section describes how to use it on
both driver and executor sides in order to identify expensive or hot code paths.

Driver Side
~~~~~~~~~~~

To use this on driver side, you can use it as you would do for regular Python programs because PySpark on driver side is a
regular Python process unless you are running your driver program in another machine (e.g., YARN cluster mode).

.. code-block:: bash

    echo "from pyspark.sql import SparkSession
    spark = SparkSession.builder.getOrCreate()
    spark.range(10).show()" > app.py

.. code-block:: bash

    python -m cProfile app.py

.. code-block:: text

    ...
         129215 function calls (125446 primitive calls) in 5.926 seconds

       Ordered by: standard name

       ncalls  tottime  percall  cumtime  percall filename:lineno(function)
     1198/405    0.001    0.000    0.083    0.000 <frozen importlib._bootstrap>:1009(_handle_fromlist)
          561    0.001    0.000    0.001    0.000 <frozen importlib._bootstrap>:103(release)
          276    0.000    0.000    0.000    0.000 <frozen importlib._bootstrap>:143(__init__)
          276    0.000    0.000    0.002    0.000 <frozen importlib._bootstrap>:147(__enter__)
    ...

Executor Side
~~~~~~~~~~~~~

To use this on executor side, PySpark provides remote `Python Profilers <https://docs.python.org/3/library/profile.html>`_ for
executor side, which can be enabled by setting ``spark.python.profile`` configuration to ``true``.

.. code-block:: bash

    pyspark --conf spark.python.profile=true


.. code-block:: python

    >>> rdd = sc.parallelize(range(100)).map(str)
    >>> rdd.count()
    100
    >>> sc.show_profiles()
    ============================================================
    Profile of RDD<id=1>
    ============================================================
             728 function calls (692 primitive calls) in 0.004 seconds

       Ordered by: internal time, cumulative time

       ncalls  tottime  percall  cumtime  percall filename:lineno(function)
           12    0.001    0.000    0.001    0.000 serializers.py:210(load_stream)
           12    0.000    0.000    0.000    0.000 {built-in method _pickle.dumps}
           12    0.000    0.000    0.001    0.000 serializers.py:252(dump_stream)
           12    0.000    0.000    0.001    0.000 context.py:506(f)
    ...

This feature is supported only with RDD APIs.
