pyspark系列2-linux安装pyspark

Dear 丶 2023-01-17 02:28 192阅读 0赞

### 文章目录 ###

*  一.安装Java和Scale
 *   *  1.1 安装java
     *  1.2 安装Scala
     *   *  1.2.1 安装
         *  1.2.2 配置
         *  1.2.3 启动
 *  二.安装Apache Spark
 *  三.pyspark案例
 *  参考:

# 一.安装Java和Scale #

## 1.1 安装java ##

因为我这个环境是CDH 6.3.1版本，已经安装了JDK，此次略过。

[root@hp1 ~]# javac -version
    javac 1.8.0_181

## 1.2 安装Scala ##

### 1.2.1 安装 ###

**代码:**

官网地址：https://www.scala-lang.org/download/
     
    wget https://downloads.lightbend.com/scala/2.13.1/scala-2.13.1.tgz
    tar -zxvf scala-2.13.1.tgz
    mv scala-2.13.1 scala

**测试地址:**

[root@hp1 local]# cd /home/
    [root@hp1 home]# ls
    backup  cloudera-host-monitor.bak3  cloudera-service-monitor.moved  csv  hdfs  shell
    [root@hp1 home]# mkdir software
    [root@hp1 home]# cd software/
    [root@hp1 software]# ls
    [root@hp1 software]# 
    [root@hp1 software]# pwd
    /home/software
    [root@hp1 software]# 
    [root@hp1 software]# https://www.scala-lang.org/download/^M
    : 没有那个文件或目录cala-lang.org/download/
    [root@hp1 software]# 
    [root@hp1 software]# 
    [root@hp1 software]# pwd
    /home/software
    [root@hp1 software]# 
    [root@hp1 software]# 
    [root@hp1 software]# wget https://downloads.lightbend.com/scala/2.13.1/scala-2.13.1.tgz
    --2021-04-08 10:37:47--  https://downloads.lightbend.com/scala/2.13.1/scala-2.13.1.tgz
    正在解析主机 downloads.lightbend.com (downloads.lightbend.com)... 13.35.121.34, 13.35.121.81, 13.35.121.50, ...
    正在连接 downloads.lightbend.com (downloads.lightbend.com)|13.35.121.34|:443... 已连接。
      发出 HTTP 请求，正在等待回应... 200 OK
    长度：19685743 (19M) [application/octet-stream]
    正在保存至: “scala-2.13.1.tgz”
    
    100%[================================================================================================================================================================>] 19,685,743  9.92MB/s 用时 1.9s   
    
    2021-04-08 10:37:50 (9.92 MB/s) - 已保存 “scala-2.13.1.tgz” [19685743/19685743])
    
    [root@hp1 software]# tar -zxvf scala-2.13.1.tgz
    scala-2.13.1/
    scala-2.13.1/lib/
    scala-2.13.1/lib/scala-compiler.jar
    scala-2.13.1/lib/scalap-2.13.1.jar
    scala-2.13.1/lib/scala-reflect.jar
    scala-2.13.1/lib/jansi-1.12.jar
    scala-2.13.1/lib/jline-2.14.6.jar
    scala-2.13.1/lib/scala-library.jar
    scala-2.13.1/doc/
    scala-2.13.1/doc/licenses/
    scala-2.13.1/doc/licenses/mit_jquery.txt
    scala-2.13.1/doc/licenses/bsd_scalacheck.txt
    scala-2.13.1/doc/licenses/bsd_asm.txt
    scala-2.13.1/doc/licenses/apache_jansi.txt
    scala-2.13.1/doc/licenses/bsd_jline.txt
    scala-2.13.1/doc/LICENSE.md
    scala-2.13.1/doc/License.rtf
    scala-2.13.1/doc/README
    scala-2.13.1/doc/tools/
    scala-2.13.1/doc/tools/scaladoc.html
    scala-2.13.1/doc/tools/scalap.html
    scala-2.13.1/doc/tools/css/
    scala-2.13.1/doc/tools/css/style.css
    scala-2.13.1/doc/tools/scala.html
    scala-2.13.1/doc/tools/index.html
    scala-2.13.1/doc/tools/images/
    scala-2.13.1/doc/tools/images/scala_logo.png
    scala-2.13.1/doc/tools/images/external.gif
    scala-2.13.1/doc/tools/scalac.html
    scala-2.13.1/doc/tools/fsc.html
    scala-2.13.1/bin/
    scala-2.13.1/bin/fsc
    scala-2.13.1/bin/scalap.bat
    scala-2.13.1/bin/scala
    scala-2.13.1/bin/scaladoc.bat
    scala-2.13.1/bin/fsc.bat
    scala-2.13.1/bin/scala.bat
    scala-2.13.1/bin/scaladoc
    scala-2.13.1/bin/scalap
    scala-2.13.1/bin/scalac
    scala-2.13.1/bin/scalac.bat
    scala-2.13.1/LICENSE
    scala-2.13.1/man/
    scala-2.13.1/man/man1/
    scala-2.13.1/man/man1/scalac.1
    scala-2.13.1/man/man1/scala.1
    scala-2.13.1/man/man1/scaladoc.1
    scala-2.13.1/man/man1/fsc.1
    scala-2.13.1/man/man1/scalap.1
    scala-2.13.1/NOTICE
    [root@hp1 software]# 
    [root@hp1 software]# 
    [root@hp1 software]# 
    [root@hp1 software]# mv scala-2.13.1 scala

### 1.2.2 配置 ###

vim /etc/profile
     
    SCALA_HOME=/home/software/scala
    PATH=$SCALA_HOME/bin:$PATH
     
    source /etc/profile

### 1.2.3 启动 ###

[root@hp1 software]# scala
    Welcome to Scala 2.13.1 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_181).
    Type in expressions for evaluation. Or try :help.
    
    scala> 
    
    scala>

# 二.安装Apache Spark #

因为我这个环境是CDH 6.3.1版本，已经安装了spark，此次略过。  
完美，pyspark也都安装了，厉害了。

查找pyspark路径

[root@hp1 ~]# which pyspark
    /usr/bin/pyspark
    [root@hp1 ~]#

运行pyspark报错

[root@hp1 software]# pyspark      
    Python 2.7.5 (default, Apr  2 2020, 13:16:51) 
    [GCC 4.8.5 20150623 (Red Hat 4.8.5-39)] on linux2
    Type "help", "copyright", "credits" or "license" for more information.
    Setting default log level to "WARN".
    To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
    21/04/08 11:03:36 ERROR spark.SparkContext: Error initializing SparkContext.
    java.lang.IllegalArgumentException: Required executor memory (1024), overhead (384 MB), and PySpark memory (0 MB) is above the max threshold (1042 MB) of this cluster! Please check the values of 'yarn.scheduler.maximum-allocation-mb' and/or 'yarn.nodemanager.resource.memory-mb'.
            at org.apache.spark.deploy.yarn.Client.verifyClusterResources(Client.scala:346)
            at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:180)
            at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:60)
            at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:186)
            at org.apache.spark.SparkContext.<init>(SparkContext.scala:511)
            at org.apache.spark.api.java.JavaSparkContext.<init>(JavaSparkContext.scala:58)
            at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
            at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
            at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
            at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
            at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:247)
            at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
            at py4j.Gateway.invoke(Gateway.java:238)
            at py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)
            at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)
            at py4j.GatewayConnection.run(GatewayConnection.java:238)
            at java.lang.Thread.run(Thread.java:748)
    21/04/08 11:03:36 WARN cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: Attempted to request executors before the AM has registered!
    21/04/08 11:03:36 WARN metrics.MetricsSystem: Stopping a MetricsSystem that is not running
    21/04/08 11:03:36 WARN spark.SparkContext: Another SparkContext is being constructed (or threw an exception in its constructor).  This may indicate an error, since only one SparkContext may be running in this JVM (see SPARK-2243). The other SparkContext was created at:
    org.apache.spark.api.java.JavaSparkContext.<init>(JavaSparkContext.scala:58)
    sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
    sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
    sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
    java.lang.reflect.Constructor.newInstance(Constructor.java:423)
    py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:247)
    py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    py4j.Gateway.invoke(Gateway.java:238)
    py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)
    py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)
    py4j.GatewayConnection.run(GatewayConnection.java:238)
    java.lang.Thread.run(Thread.java:748)
    21/04/08 11:03:36 ERROR spark.SparkContext: Error initializing SparkContext.
    java.lang.IllegalArgumentException: Required executor memory (1024), overhead (384 MB), and PySpark memory (0 MB) is above the max threshold (1042 MB) of this cluster! Please check the values of 'yarn.scheduler.maximum-allocation-mb' and/or 'yarn.nodemanager.resource.memory-mb'.
            at org.apache.spark.deploy.yarn.Client.verifyClusterResources(Client.scala:346)
            at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:180)
            at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:60)
            at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:186)
            at org.apache.spark.SparkContext.<init>(SparkContext.scala:511)
            at org.apache.spark.api.java.JavaSparkContext.<init>(JavaSparkContext.scala:58)
            at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
            at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
            at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
            at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
            at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:247)
            at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
            at py4j.Gateway.invoke(Gateway.java:238)
            at py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)
            at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)
            at py4j.GatewayConnection.run(GatewayConnection.java:238)
            at java.lang.Thread.run(Thread.java:748)
    21/04/08 11:03:36 WARN cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: Attempted to request executors before the AM has registered!
    21/04/08 11:03:36 WARN metrics.MetricsSystem: Stopping a MetricsSystem that is not running
    /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/spark/python/pyspark/shell.py:45: UserWarning: Failed to initialize Spark session.
      warnings.warn("Failed to initialize Spark session.")
    Traceback (most recent call last):
      File "/opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/spark/python/pyspark/shell.py", line 41, in <module>
        spark = SparkSession._create_shell_session()
      File "/opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/spark/python/pyspark/sql/session.py", line 594, in _create_shell_session
        return SparkSession.builder.getOrCreate()
      File "/opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/spark/python/pyspark/sql/session.py", line 173, in getOrCreate
        sc = SparkContext.getOrCreate(sparkConf)
      File "/opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/spark/python/pyspark/context.py", line 354, in getOrCreate
        SparkContext(conf=conf or SparkConf())
      File "/opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/spark/python/pyspark/context.py", line 123, in __init__
        conf, jsc, profiler_cls)
      File "/opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/spark/python/pyspark/context.py", line 185, in _do_init
        self._jsc = jsc or self._initialize_context(self._conf._jconf)
      File "/opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/spark/python/pyspark/context.py", line 293, in _initialize_context
        return self._jvm.JavaSparkContext(jconf)
      File "/opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1525, in __call__
        answer, self._gateway_client, None, self._fqn)
      File "/opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value
        format(target_id, ".", name), value)
    Py4JJavaError: An error occurred while calling None.org.apache.spark.api.java.JavaSparkContext.
    : java.lang.IllegalArgumentException: Required executor memory (1024), overhead (384 MB), and PySpark memory (0 MB) is above the max threshold (1042 MB) of this cluster! Please check the values of 'yarn.scheduler.maximum-allocation-mb' and/or 'yarn.nodemanager.resource.memory-mb'.
            at org.apache.spark.deploy.yarn.Client.verifyClusterResources(Client.scala:346)
            at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:180)
            at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:60)
            at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:186)
            at org.apache.spark.SparkContext.<init>(SparkContext.scala:511)
            at org.apache.spark.api.java.JavaSparkContext.<init>(JavaSparkContext.scala:58)
            at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
            at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
            at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
            at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
            at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:247)
            at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
            at py4j.Gateway.invoke(Gateway.java:238)
            at py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)
            at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)
            at py4j.GatewayConnection.run(GatewayConnection.java:238)
            at java.lang.Thread.run(Thread.java:748)
    
    [root@hp1 software]#

网上搜到解决方案，鉴于测试机器内存较小，修改配置如下:

yarn.app.mapreduce.am.resource.mb =2g
    
    yarn.nodemanager.resource.memory-mb=4g
    
    yarn.scheduler.maximum-allocation-mb=2g

**修改完成后重启服务使配置生效，重新测试**

# 三.pyspark案例 #

用pyspark写一个wordcount程序  
**代码:**  
wordcount.py

#!/usr/bin/env python
    # -*- coding: utf-8 -*-
    
    import os
    import time
    from pyspark import SparkContext, SparkConf
    
    if __name__ == "__main__":
        #设置环境变量
        os.environ['JAVA_HOME'] = '/usr/java/jdk1.8.0_181'                      # java环境配置
        os.environ['HADOOP_HOME'] = '/opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop'          # hadoop安装目录
        os.environ['SPARK_HOME'] = '/opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/spark'  # 设置spark安装目录
    
        spark_conf = SparkConf()\
            .setAppName('Python_Spark_WordCount')\
            .setMaster('local[2]')
        # 设置Spark程序运行的地方，此处设置运行在本地模式，启动2个线程分析数据
    
        sc = SparkContext(conf=spark_conf) # 获取SparkContext实例对象, 用于读取要处理的数据和Job执行
        # 设置日志级别 Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, WARN
        sc.setLogLevel('WARN')
        # <SparkContext master=local[2] appName=Python_Spark_WordCount>
        print (sc)
    
        """ 创建RDD，读取要分析的数据： -1. 方式一：从本地集合（列表、元组、字典）进行并行化创建 -2. 方式二：从外部文件系统读取数据（HDFS、LocalFS） """
        # 第一种方式：从集合并行创建RDD
        def local_rdd(spark_context):
            datas = ['hadoop spark', 'spark hive spark spark', 'spark hadoop python hive', ' ']
            return spark_context.parallelize(datas)     # Create RDD
    
        # 第二种方式：从本地文件系统中读取
        def hdfs_rdd(spark_context):
            return spark_context.textFile("/user/rdedu/wc.data")  # 从文件中读取数据
    
        # rdd = local_rdd(sc) #方法1
        rdd = hdfs_rdd(sc)       #方法2
        print rdd.count()
        print rdd.first()
    
        # =============词频统计=======================================
        word_count_rdd = rdd\
            .filter(lambda line: len(line.strip()) != 0)\
            .flatMap(lambda line: line.strip().split(" "))\
            .map(lambda word: (word, 1))\
            .reduceByKey(lambda a, b: a + b)          # 将Key相同的Value进行合并
    
        for word, count in word_count_rdd.collect():  # collect()函数将rdd转换为列表
            print word, ', ', count
    
        print "===================================="
    
        # 依据统计的count值降序排序
        sort_rdd = word_count_rdd\
            .map(lambda (word, count): (count, word))\
            .sortByKey(ascending=False)
        print sort_rdd.collect()
    
        # def top(self, num, key=None):
        print word_count_rdd.top(3, key=lambda (word, count): count)
    
        # def takeOrdered(self, num, key=None) -> Bottom
        print word_count_rdd.takeOrdered(3, key=lambda (word, count): count)
    
        # 为了查看Spark程序运行是的WEB UI界面，让线程休眠一段时间
        time.sleep(100)
    
        # SparkContext Stop
        sc.stop()

**测试记录:**

[root@hp1 software]# spark-submit wordcount.py 
    21/04/08 14:13:13 INFO spark.SparkContext: Running Spark version 2.4.0-cdh6.3.1
    21/04/08 14:13:13 INFO logging.DriverLogger: Added a local log appender at: /tmp/spark-11001905-0583-4ed6-a7f5-787ae6d9565c/__driver_logs__/driver.log
    21/04/08 14:13:13 INFO spark.SparkContext: Submitted application: Python_Spark_WordCount
    21/04/08 14:13:13 INFO spark.SecurityManager: Changing view acls to: root
    21/04/08 14:13:13 INFO spark.SecurityManager: Changing modify acls to: root
    21/04/08 14:13:13 INFO spark.SecurityManager: Changing view acls groups to: 
    21/04/08 14:13:13 INFO spark.SecurityManager: Changing modify acls groups to: 
    21/04/08 14:13:13 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users  with view permissions: Set(root); groups with view permissions: Set(); users  with modify permissions: Set(root); groups with modify permissions: Set()
    21/04/08 14:13:13 INFO util.Utils: Successfully started service 'sparkDriver' on port 42666.
    21/04/08 14:13:13 INFO spark.SparkEnv: Registering MapOutputTracker
    21/04/08 14:13:13 INFO spark.SparkEnv: Registering BlockManagerMaster
    21/04/08 14:13:13 INFO storage.BlockManagerMasterEndpoint: Using org.apache.spark.storage.DefaultTopologyMapper for getting topology information
    21/04/08 14:13:13 INFO storage.BlockManagerMasterEndpoint: BlockManagerMasterEndpoint up
    21/04/08 14:13:13 INFO storage.DiskBlockManager: Created local directory at /tmp/blockmgr-190fe2cf-05dc-415b-ba34-168b596ddbfd
    21/04/08 14:13:13 INFO memory.MemoryStore: MemoryStore started with capacity 366.3 MB
    21/04/08 14:13:13 INFO spark.SparkEnv: Registering OutputCommitCoordinator
    21/04/08 14:13:13 INFO util.log: Logging initialized @1925ms
    21/04/08 14:13:13 INFO server.Server: jetty-9.3.z-SNAPSHOT, build timestamp: 2018-09-05T05:11:46+08:00, git hash: 3ce520221d0240229c862b122d2b06c12a625732
    21/04/08 14:13:13 INFO server.Server: Started @2002ms
    21/04/08 14:13:13 INFO server.AbstractConnector: Started ServerConnector@575fe4eb{HTTP/1.1,[http/1.1]}{0.0.0.0:4040}
    21/04/08 14:13:13 INFO util.Utils: Successfully started service 'SparkUI' on port 4040.
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@3cf93f90{/jobs,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@3f6c4275{/jobs/json,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@40aaa4fa{/jobs/job,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@6c969bc1{/jobs/job/json,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@313403cc{/stages,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@242cec64{/stages/json,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@51a6582b{/stages/stage,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@5b01516d{/stages/stage/json,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@38853d26{/stages/pool,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@5c4278a{/stages/pool/json,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@73315507{/storage,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@22eae835{/storage/json,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@33178d43{/storage/rdd,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@17cb14dc{/storage/rdd/json,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@6eabf24f{/environment,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@1b7514ef{/environment/json,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@1407ff57{/executors,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@5ba564ff{/executors/json,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@6aadcc4e{/executors/threadDump,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@59cc6f98{/executors/threadDump/json,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@721adeb1{/static,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@3e08dd87{/,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@29849cf7{/api,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@3c51f401{/jobs/job/kill,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@252c4495{/stages/stage/kill,null,AVAILABLE,@Spark}
    21/04/08 14:13:13 INFO ui.SparkUI: Bound SparkUI to 0.0.0.0, and started at http://hp1:4040
    21/04/08 14:13:13 INFO executor.Executor: Starting executor ID driver on host localhost
    21/04/08 14:13:13 INFO util.Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 33083.
    21/04/08 14:13:13 INFO netty.NettyBlockTransferService: Server created on hp1:33083
    21/04/08 14:13:13 INFO storage.BlockManager: Using org.apache.spark.storage.RandomBlockReplicationPolicy for block replication policy
    21/04/08 14:13:13 INFO storage.BlockManagerMaster: Registering BlockManager BlockManagerId(driver, hp1, 33083, None)
    21/04/08 14:13:13 INFO storage.BlockManagerMasterEndpoint: Registering block manager hp1:33083 with 366.3 MB RAM, BlockManagerId(driver, hp1, 33083, None)
    21/04/08 14:13:13 INFO storage.BlockManagerMaster: Registered BlockManager BlockManagerId(driver, hp1, 33083, None)
    21/04/08 14:13:13 INFO storage.BlockManager: external shuffle service port = 7337
    21/04/08 14:13:13 INFO storage.BlockManager: Initialized BlockManager: BlockManagerId(driver, hp1, 33083, None)
    21/04/08 14:13:14 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@5fba4081{/metrics/json,null,AVAILABLE,@Spark}
    21/04/08 14:13:14 INFO scheduler.EventLoggingListener: Logging events to hdfs://nameservice1/user/spark/applicationHistory/local-1617862393786
    21/04/08 14:13:14 WARN lineage.LineageWriter: Lineage directory /var/log/spark/lineage doesn't exist or is not writable. Lineage for this application will be disabled.
    21/04/08 14:13:14 INFO util.Utils: Extension com.cloudera.spark.lineage.NavigatorAppListener not being initialized.
    21/04/08 14:13:14 INFO logging.DriverLogger$DfsAsyncWriter: Started driver log file sync to: /user/spark/driverLogs/local-1617862393786_driver.log
    <SparkContext master=local[2] appName=Python_Spark_WordCount>
    1
    'hadoop spark', 'spark hive spark spark', 'spark hadoop python hive', ' '
    python ,  1
    'spark ,  2
    spark ,  1
    hive ,  1
    ' ,  2
    hive', ,  1
    'hadoop ,  1
    spark', ,  2
    hadoop ,  1
    ====================================
    [(2, u"'spark"), (2, u"'"), (2, u"spark',"), (1, u'python'), (1, u'spark'), (1, u'hive'), (1, u"hive',"), (1, u"'hadoop"), (1, u'hadoop')]
    [(u"'spark", 2), (u"'", 2), (u"spark',", 2)]
    [(u'python', 1), (u'spark', 1), (u'hive', 1)]
    
    
    
    
    [root@hp1 software]# 
    [root@hp1 software]#

# 参考: #

1.https://blog.csdn.net/u013227399/article/details/102897606  
2.https://www.cnblogs.com/erlou96/p/12933548.html