PySpark - 使用 Python 在 Spark 上编程

Python Programming Guide

The Spark Python API (PySpark) exposes the Spark programming model to Python. To learn the basics of Spark, we recommend reading through theScala 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 multiple types.

• PySpark does not yet support a few API calls, such as lookup and non-text input files, though these will be added in future releases.

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:

logData = sc.textFile(logFile).cache()
errors = logData.filter(lambda line: "ERROR" in line)

You can also pass functions that are defined with the def keyword; this is useful for longer functions that can’t be expressed using lambda:

def is_error(line):
    return "ERROR" in lineerrors = logData.filter(is_error)

Functions can access objects in enclosing scopes, although modifications to those objects within RDD methods will not be propagated back:

error_keywords = ["Exception", "Error"]

def is_error(line):
    return any(keyword in line for keyword in error_keywords)

errors = logData.filter(is_error)

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.

In addition, PySpark fully supports interactive use—simply run ./bin/pyspark to launch an interactive shell.

Installing and Configuring PySpark

PySpark requires Python 2.6 or higher. PySpark applications 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 requires python to be available on the system PATH and use it to run programs; an alternate Python executable may be specified by setting the PYSPARK_PYTHON environment variable in conf/spark-env.sh (or .cmd on Windows).

All of PySpark’s library dependencies, including Py4J, are bundled with PySpark and automatically imported.

Standalone PySpark applications should be run using the bin/pyspark script, which automatically configures the Java and Python environment using the settings in conf/spark-env.sh or .cmd. The script automatically adds the bin/pyspark package to the PYTHONPATH.

Interactive Use

The bin/pyspark script launches a Python interpreter that is configured to run PySpark applications. To use pyspark interactively, first build Spark, then launch it directly from the command line without any options:

$ sbt/sbt assembly
$ ./bin/pyspark

The Python shell can be used explore data interactively and is a simple way to learn the API:

>>> 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'']

>>> help(pyspark) # Show all pyspark functions

By default, the bin/pyspark shell creates SparkContext that runs applications locally on a single core. To connect to a non-local cluster, or use multiple cores, set the MASTER environment variable. For example, to use the bin/pyspark shell with a standalone Spark cluster:

$ MASTER=spark://IP:PORT ./bin/pyspark

Or, to use four cores on the local machine:

$ MASTER=local[4] ./bin/pyspark

IPython

注意：此部分已过时，请参考 http://www.jupyter.org/

It is also possible to launch PySpark in IPython, the enhanced Python interpreter. PySpark works with IPython 1.0.0 and later. To use IPython, set the IPYTHON variable to 1 when running bin/pyspark:

$ IPYTHON=1 ./bin/pyspark

Alternatively, you can customize the ipython command by setting IPYTHON_OPTS. For example, to launch the IPython Notebook with PyLab graphing support:

$ IPYTHON_OPTS="notebook --pylab inline" ./bin/pyspark

IPython also works on a cluster or on multiple cores if you set the MASTER environment variable.

Standalone Programs

PySpark can also be used from standalone Python scripts by creating a SparkContext in your script and running the script using bin/pyspark. The Quick Start guide includes a complete example of a standalone Python application.

Code dependencies can be deployed by listing them in the pyFiles option in the SparkContext constructor:

from pyspark import SparkContext
sc = SparkContext("local", "App Name", pyFiles=[''MyFile.py'', ''lib.zip'', ''app.egg''])

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.

You can set configuration properties by passing aSparkConf object to SparkContext:

from pyspark import SparkConf, SparkContext

conf = (SparkConf()
         .setMaster("local")
         .setAppName("My app")
         .set("spark.executor.memory", "1g"))
sc = SparkContext(conf = conf)

API Docs

API documentation for PySpark is available as Epydoc. Many of the methods also contain doctests that provide additional usage examples.

Libraries

MLlib is also available in PySpark. To use it, you’ll needNumPy version 1.7 or newer, and Python 2.7. The MLlib guide contains some example applications.

Where to Go from Here

PySpark also includes several sample programs in the python/examples folder. You can run them by passing the files to pyspark; e.g.:

./bin/pyspark python/examples/wordcount.py

Each program prints usage help when run without arguments.

PySpark Dataframe 将列转换为行

REC_DATA = spark.createDataFrame(
    [
      (''exercise'',''fiber'',''rice'',''male''),(''exercise'',''female''),''water'',''exercise'',],StructType(
        [
            StructField("1_rec",StringType(),False),StructField("2_rec",StructField("3_rec",StructField("sex",True),]
    )
)

import pyspark.sql.functions as F

REC_DATA = (REC_DATA
            .selectExpr("stack(3,''1_rec'',1_rec,''2_rec'',2_rec,''3_rec'',3_rec) (position,name)")
            .groupBy(''position'',''name'')
            .agg(F.count("*").alias(''count'')))
REC_DATA.show()

+--------+--------+-----+
|position|    name|count|
+--------+--------+-----+
|   1_rec|   water|    1|
|   2_rec|    rice|    2|
|   3_rec|exercise|    1|
|   3_rec|   fiber|    2|
|   2_rec|   water|    1|
|   3_rec|    rice|    1|
|   1_rec|exercise|    3|
|   2_rec|   fiber|    1|
+--------+--------+-----+

corr_temp_df[(''vacationdate'', ''date''), (''valueE'', ''string''), (''valueD'', ''string''), (''valueC'', ''string''), (''valueB'', ''string''), (''valueA'', ''string'')]

corr_temp_df.dtypes

import datetimefrom pyspark.sql import Rowfrom pyspark.sql.functions import colrow = Row("vacationdate")df = sc.parallelize([    row(datetime.date(2015, 10, 07)),    row(datetime.date(1971, 01, 01))]).toDF()

from pyspark.sql.functions import date_format(df   .select(date_format(col("vacationdate"), "dd-MM-YYYY")   .alias("date_string"))   .show())

df.registerTempTable("df")sqlContext.sql(    "SELECT date_format(vacationdate, ''dd-MM-YYYY'') AS date_string FROM df")

from pyspark.sql.functions import udf, litmy_date_format = udf(lambda d, fmt: d.strftime(fmt))df.select(    my_date_format(col("vacationdate"), lit("%d-%m-%Y")).alias("date_string")).show()