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