spark教程(二)-shell操作

spark 支持 shell 操作

shell 主要用于调试，所以简单介绍用法即可

支持多种语言的 shell

包括 scala shell、python shell、R shell、SQL shell 等

spark-shell 用于在 scala 的 shell 模式下操作 spark

pyspark 用于在 python 的 shell 模式下操作 spark

spark-sql 用于在 spark-sql 模式下运行 sql，后续会讲 sparkSQL

支持 3 种模式的 shell

local 模式、 standalone 模式、yarn模式

不同的模式需要指定 master

python 模式的 shell 命令

master 参数指定了运行模式

[root@hadoop10 spark]# bin/pyspark --help
Usage: ./bin/pyspark [options]

Options:
--master MASTER_URL spark://host:port, mesos://host:port, yarn, # 设定 master，即在哪里运行 spark，
# mesos://host:port一般不用；yarn需要把spark部署到yarn上
k8s://https://host:port, or local (Default: local[*]). # local 本地模式，local 表示单线程，local[num]表示num个进程，
# local[*]表示服务器cpu是几核就是几个进程
--deploy-mode DEPLOY_MODE Whether to launch the driver program locally ("client") or
on one of the worker machines inside the cluster ("cluster")
(Default: client).
--class CLASS_NAME Your application's main class (for Java / Scala apps). # 要执行的 class 类名
--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 # 逗号隔开的 maven 列表，给 当前会话 添加依赖
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 # 逗号隔开的 zip.文件列表，替代 PYTHONPATH 的作用，
on the PYTHONPATH for Python apps. # 也就是说如果不设置 PYTHONPATH，就需要这个参数，才能导入 文件中的模块
--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).
--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).

--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.
--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
(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.

进入 python shell 模式

[root@hadoop10 spark]# bin/pyspark 
Python 2.7.12 (default, Oct  2 2019, 19:43:15) 
[GCC 4.4.7 20120313 (Red Hat 4.4.7-4)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
19/10/09 18:10:53 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /__ / .__/\_,_/_/ /_/\_\   version 2.4.4
      /_/

Using Python version 2.7.12 (default, Oct  2 2019 19:43:15)
SparkSession available as 'spark'.  # 自带 spark

shell 模式可以通过 http://192.168.10.10:4040 查看任务

shell 操作语法与脚本相同，示例如下

>>> distFile = sc.textFile('README.md')
>>> distFile.map(lambda x: len(x)).reduce(lambda a, b: a + b)
3847                                                                            
>>> distFile.count()
105