spark教程(六)-Python 编程与 spark-submit 命令

hadoop 是 java 开发的，原生支持 java；spark 是 scala 开发的，原生支持 scala；

spark 还支持 java、python、R，本文只介绍 python

spark 1.x 和 spark 2.x 用法略有不同，spark 1.x 的用法大部分也适用于 spark 2.x

Pyspark

它是 python 的一个库，python + spark，简单来说，想用 python 操作 spark，就必须用 pyspark 模块

编程逻辑

环境

首先需要配置 /etc/profile

# python can call pyspark directly

export PYTHONPATH=$SPARK_HOME/python:$SPARK_HOME/python/pyspark:$SPARK_HOME/python/lib/py4j-0.10.4-src.zip:$PYTHONPATH

python 的搜索路径，加上 spark 中 python 和 pyspark，以及 py4j-0.10.4-src.zip，他的作用是负责 python 和 java 之间的转换。

编程

第一步，创建 SparkSession 或者 SparkContext

在 spark1.x 中是创建 SparkContext

在 spark2.x 中创建 SparkSession，或者说在 sparkSQL 应用中创建 SparkSession

第二步，创建 RDD 并操作

完整示例

from __future__ import print_function

from pyspark import *

import os

print(os.environ['SPARK_HOME'])

print(os.environ['HADOOP_HOME'])

if __name__ == '__main__':

    sc = SparkContext("spark://hadoop10:7077")

    rdd = sc.parallelize("hello Pyspark world".split(' '))

    counts = rdd.map(lambda word: (word, 1)) \

        .reduceByKey(lambda a, b: a + b)

    counts.saveAsTextFile('/usr/lib/spark/out')

    counts.foreach(print)

    sc.stop()

运行方式

1. python 命令

2. spark 命令　　

bin/spark-submit test1.py

这里只是简单操作，下面会详细介绍 spark-submit 命令

任务监控

脚本模式通过 http://192.168.10.10:8080/ 查看任务

spark-submit

[root@hadoop10 hadoop-2.6.5]# spark-submit --help

Options:

--master MASTER_URL spark://host:port, mesos://host:port, yarn, 指定 spark 运行模式，即使在 代码里指定了 spark master，此处也需要重新指定

k8s://https://host:port, or local (Default: local[*]).

--deploy-mode DEPLOY_MODE Whether to launch the driver program locally ("client") or client 模式 or cluster 模式

on one of the worker machines inside the cluster ("cluster")

(Default: client).

--class CLASS_NAME Your application's main class (for Java / Scala apps).

--name NAME A name of your application.

--jars JARS Comma-separated list of jars to include on the driver

and executor classpaths.

--packages Comma-separated list of maven coordinates of jars to include

on the driver and executor classpaths. Will search the local

maven repo, then maven central and any additional remote

repositories given by --repositories. The format for the

coordinates should be groupId:artifactId:version.

--exclude-packages Comma-separated list of groupId:artifactId, to exclude while

resolving the dependencies provided in --packages to avoid

dependency conflicts.

--repositories Comma-separated list of additional remote repositories to

search for the maven coordinates given with --packages.

--py-files PY_FILES Comma-separated list of .zip, .egg, or .py files to place

on the PYTHONPATH for Python apps.

--files FILES Comma-separated list of files to be placed in the working

directory of each executor. File paths of these files

in executors can be accessed via SparkFiles.get(fileName).

--conf PROP=VALUE Arbitrary Spark configuration property.

--properties-file FILE Path to a file from which to load extra properties. If not

specified, this will look for conf/spark-defaults.conf.

--driver-memory MEM Memory for driver (e.g. 1000M, 2G) (Default: 1024M). 指定 driver 内存，

--driver-java-options Extra Java options to pass to the driver.

--driver-library-path Extra library path entries to pass to the driver.

--driver-class-path Extra class path entries to pass to the driver. Note that

jars added with --jars are automatically included in the

classpath.

--executor-memory MEM Memory per executor (e.g. 1000M, 2G) (Default: 1G). 指定 executor 内存

--proxy-user NAME User to impersonate when submitting the application.

This argument does not work with --principal / --keytab.

--help, -h Show this help message and exit. 查看所有参数

--verbose, -v Print additional debug output.

--version, Print the version of current Spark.

Cluster deploy mode only:

--driver-cores NUM Number of cores used by the driver, only in cluster mode 指定 cpu 个数

(Default: 1).

Spark standalone or Mesos with cluster deploy mode only:

--supervise If given, restarts the driver on failure.

--kill SUBMISSION_ID If given, kills the driver specified.

--status SUBMISSION_ID If given, requests the status of the driver specified.

Spark standalone and Mesos only:

--total-executor-cores NUM Total cores for all executors.

Spark standalone and YARN only:

--executor-cores NUM Number of cores per executor. (Default: 1 in YARN mode,

or all available cores on the worker in standalone mode)

YARN-only:

--queue QUEUE_NAME The YARN queue to submit to (Default: "default").

--num-executors NUM Number of executors to launch (Default: 2).

If dynamic allocation is enabled, the initial number of

executors will be at least NUM.

--archives ARCHIVES Comma separated list of archives to be extracted into the

working directory of each executor.

--principal PRINCIPAL Principal to be used to login to KDC, while running on

secure HDFS.

--keytab KEYTAB The full path to the file that contains the keytab for the

principal specified above. This keytab will be copied to

the node running the Application Master via the Secure

Distributed Cache, for renewing the login tickets and the

delegation tokens periodically.

注意参数写在前面，运行的文件写在后面，如下

spark-submit --master yarn-client  --driver-memory 512m  xx.py