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Before learning any technology of spark, please correctly understand spark, you can refer to: correct understanding of spark

The following is a configuration of the environment for developing spark with python on the mac operating system

First, install python

Spark2.2.0 requires the version of python to be Python2.6+ or Python3.4+

Please refer to:

Http://jingyan.baidu.com/article/7908e85c78c743af491ad261.html

Download the spark compiler and configure the environment variables

1. On the official website: http://spark.apache.org/downloads.html download version: spark-2.2.0-bin-hadoop2.6.tgz package

Put it on a local disk and decompress it.

2. Set environment variables:

Cd ~

Vi .bash _ profile

Export SPARK_HOME=/Users/tangweiqun/Desktop/bigdata/spark/spark-2.2.0-bin-hadoop2.6

Export PATH=$PATH:$SCALA_HOME/bin:$M2_HOME/bin:$JAVA_HOME/bin:$SPARK_HOME/bin

Source .bash _ profile

3. You need to execute chmod 744. / * on the files in the bin directory under SPARK_HOME, otherwise an error of insufficient permissions will be reported.

Window machines should not have to do this.

Third, install PyCharm

1. Download it from the official website: https://www.jetbrains.com/pycharm/download/ and install it foolishly.

4. Write wordcount.py and run it successfully

1. Create a project

File-- > New Project

2. Configure PYTHONPATH for PyCharm

Run-- > Edit Configurations, the configuration is as follows

Click the "+" above, and then fill in:

PYTHONPATH=/Users/tangweiqun/Desktop/bigdata/spark/spark-2.1.0-bin-hadoop2.6/python/:/Users/tangweiqun/Desktop/bigdata/spark/spark-2.1.0-bin-hadoop2.6/python/lib/py4j-0.10.4-src.zip

Add the python-related dependencies in the spark installation package

3. Py4j-some-version.zip and pyspark.zip are added to the project

In order to see the source code, we need to associate the project with the source code as follows:

Click + Add Content Root to add two zip packages under / Users/tangweiqun/Desktop/bigdata/spark/spark-2.1.0-bin-hadoop2.6/python/lib

4. Write spark word count and run it successfully

Create a python file, wordcount.py, with the following contents:

From pyspark import SparkContext, SparkConf

Import os

Import shutil

If _ name__ = = "_ _ main__":

Conf = SparkConf () .setAppName ("appName") .setMaster ("local")

Sc = SparkContext (conf=conf)

SourceDataRDD = sc.textFile ("file:///Users/tangweiqun/test.txt")

WordsRDD = sourceDataRDD.flatMap (lambda line: line.split ())

KeyValueWordsRDD = wordsRDD.map (lambda s: (s, 1))

WordCountRDD = keyValueWordsRDD.reduceByKey (lambda a, b: a + b)

OutputPath = "/ Users/tangweiqun/wordcount"

If os.path.exists (outputPath):

Shutil.rmtree (outputPath)

WordsRDD.saveAsTextFile ("file://" + outputPath)

Print wordCountRDD.collect ()

Right click to run successfully

Detailed and systematic understanding of spark core RDD-related Api can be referred to: detailed explanation of spark core RDD api principle

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