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The Spark Python API (PySpark) exposes most of the Spark features available in the Scala version to Python. To learn the basics of Spark, we recommend reading through the Scala programming guide first; it should be easy to follow even if you don't know Scala. This guide will show how to use the Spark features described there in Python.
Key Differences in the Python API
There are a few key differences between the Python and Scala APIs:
- Python is dynamically typed, so RDDs can hold objects of different types.
- PySpark does not currently support the following Spark features:
- Accumulators
- Special functions on RDDs of doubles, such as
mean
andstdev
lookup
persist
at storage levels other thanMEMORY_ONLY
sample
sort
In PySpark, RDDs support the same methods as their Scala counterparts but take Python functions and return Python collection types.
Short functions can be passed to RDD methods using Python's lambda
syntax:
{% highlight python %} logData = sc.textFile(logFile).cache() errors = logData.filter(lambda s: 'ERROR' in s.split()) {% endhighlight %}
You can also pass functions that are defined using the def
keyword; this is useful for more complicated functions that cannot be expressed using lambda
:
{% highlight python %} def is_error(line): return 'ERROR' in line.split() errors = logData.filter(is_error) {% endhighlight %}
Functions can access objects in enclosing scopes, although modifications to those objects within RDD methods will not be propagated to other tasks:
{% highlight python %} error_keywords = ["Exception", "Error"] def is_error(line): words = line.split() return any(keyword in words for keyword in error_keywords) errors = logData.filter(is_error) {% endhighlight %}
PySpark will automatically ship these functions to workers, along with any objects that they reference. Instances of classes will be serialized and shipped to workers by PySpark, but classes themselves cannot be automatically distributed to workers. The Standalone Use section describes how to ship code dependencies to workers.
Installing and Configuring PySpark
PySpark requires Python 2.6 or higher.
PySpark jobs are executed using a standard cPython interpreter in order to support Python modules that use C extensions.
We have not tested PySpark with Python 3 or with alternative Python interpreters, such as PyPy or Jython.
By default, PySpark's scripts will run programs using python
; an alternate Python executable may be specified by setting the PYSPARK_PYTHON
environment variable in conf/spark-env.sh
.
All of PySpark's library dependencies, including Py4J, are bundled with PySpark and automatically imported.
Standalone PySpark jobs should be run using the pyspark
script, which automatically configures the Java and Python environment using the settings in conf/spark-env.sh
.
The script automatically adds the pyspark
package to the PYTHONPATH
.
Interactive Use
The pyspark
script launches a Python interpreter that is configured to run PySpark jobs.
When run without any input files, pyspark
launches a shell that can be used explore data interactively, which is a simple way to learn the API:
{% highlight python %}
words = sc.textFile("/usr/share/dict/words") words.filter(lambda w: w.startswith("spar")).take(5) [u'spar', u'sparable', u'sparada', u'sparadrap', u'sparagrass'] {% endhighlight %}
By default, the pyspark
shell creates SparkContext that runs jobs locally.
To connect to a non-local cluster, set the MASTER
environment variable.
For example, to use the pyspark
shell with a standalone Spark cluster:
{% highlight shell %} $ MASTER=spark://IP:PORT ./pyspark {% endhighlight %}
Standalone Use
PySpark can also be used from standalone Python scripts by creating a SparkContext in your script and running the script using pyspark
.
The Quick Start guide includes a complete example of a standalone Python job.
Code dependencies can be deployed by listing them in the pyFiles
option in the SparkContext constructor:
{% highlight python %} from pyspark import SparkContext sc = SparkContext("local", "Job Name", pyFiles=['MyFile.py', 'lib.zip', 'app.egg']) {% endhighlight %}
Files listed here will be added to the PYTHONPATH
and shipped to remote worker machines.
Code dependencies can be added to an existing SparkContext using its addPyFile()
method.
Where to Go from Here
PySpark includes several sample programs using the Python API in python/examples
.
You can run them by passing the files to the pyspark
script -- for example ./pyspark python/examples/wordcount.py
.
Each example program prints usage help when run without any arguments.
We currently provide API documentation for the Python API as Epydoc. Many of the RDD method descriptions contain doctests that provide additional usage examples.