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README
Apache-2.0

SparkSubmit 步骤

SparkSubmit 整体分为以下的步骤:

  1. 执行 spark-submit 脚本
  2. 执行 spark-class 脚本到 build_command
  3. 执行 spark-class 脚本的 build_command;java 启动 java -Xmx128m -cp $LAUNCH_CLASSPATH org.apache.spark.launcher.Main
    org.apache.spark.deploy.SparkSubmit --master xx --deploy-mode cluster ...
    校验和解析 命令行参数 输出 上面程序组装好的 命令,并且在后面人为添加 空格0 :
    CMD=${JAVA_HOME/bin/java conf/java-opts xxx -cp org.apache.spark.deploy.SparkSubmit --master xx --deploy-mode x --name xx --conf xxx=yyy --jars xx,yy,zz --class zzz userJar userArgs 0
  4. 执行 spark-class 的 build_command 之后的脚本;校验上面的命令,去除后面的 空格0;
    shell exec $CMD 命令
  5. java 启动 org.apache.spark.deploy.SparkSubmit,进入到这个 类 的生命周期里面去了
  6. 在 org.apache.spark.deploy.SparkSubmit 里面 首先也会解析 参数,然后进去 submit 子选项中,接下来 prepareSubmitEnvironment
    完成 sparkConf = --conf ; childClasspath = [用户jar, --jars 。。。] ; childMainClass = org.apache.spark.deploy.yarn.YarnClusterApplication
    childArgs = --jar 用户的jarxxx --class yyy --arg 用户参数1 --arg 用户参数2 的 组装
    注意 yarn-class 模式下--conf必须包含的配置:
    //yarn-cluster sparkConf =:spark.master,spark.submit.deployMode,spark.app.name,spark.driver.extraClassPath=null,spark.driver.extraJavaOptions=null,
    //spark.driver.extraLibraryPath=null,spark.yarn.queue=null,spark.executor.instances=--num-executors,spark.yarn.dist.pyFiles=null,spark.yarn.dist.jars=--jars
    //spark.yarn.dist.files=--flies,spark.yarn.dist.archives=--archives,spark.yarn.principal=--principal,spark.yarn.keytab=--keytab,spark.executor.cores=--executor-cores,
    //spark.executor.memory=--executor-memory,spark.driver.memory=--driver-memory,spark.driver.cores=--driver-cores,
    //注意没有 spark.jars 和 spark.files 在 yarn-cluster 模式下
    //注意这里的 conf 里面有 spark-defaults.conf 的值了

注意在这个 有一个 classLoader 会把 childClasspath 都加载进去,并把 Thread.currentThread.setContextClassLoader(loader) 最后调用 childMainClass 的start 方法。org.apache.spark.deploy.yarn.YarnClusterApplication.start(childArgs, sparkConf)
7. 进入到了 YarnClusterApplication 的 start 方法;
首先也会有一个参数解析的过程 使用 org.apache.spark.deploy.yarn.ClientArguments 解析 (childArgs, sparkConf) 的参数
--conf 中如果存在 spark.jars,spark.files 将会被移除了 然后 new Client(new ClientArguments(args), conf).run()
进入到 org.apache.spark.deploy.yarn.Client 中去了
8. Client 的 run 方法 里面 创建 yarnClient
验证yarn资源,申请 dirver的总内存 和 executor 是否 超过了 yarn 一个Container 最大允许的 memeory
创建 container 上下文,准备资源 jars,confs,classpath等 组装和安装启动命令等
创建 APP submit 上下文
提交这个 appContext
注意:组装好的命令:
//javaOpts=-Xmx?m-Djava.io.tmpdir=./tmp-Dspark.yarn.app.container.log.dir=<LOG_DIR>
//amArgs=org.apache.spark.deploy.yarn.ApplicationMaster --class xxx --jar xxx(包含用户jar和--jars) --arg args1 --arg args2 --properties-file pwd/spark_conf/spark_conf.properties
//commands = JAVA_HOME/bin/java -server javaOpts amArgs 1> <LOG_DIR>/stdout 2> <LOG_DIR>/stderr
8.1 当上面 yarnClient提交完之后,整个流程就进入到了 yarn集群了,可以看到 会在 yarn中首先运行 org.apache.spark.deploy.yarn.ApplicationMaster

  1. 第一步 解析参数 装备 ,如 从 spark hdfs stage 的 pwd/spark_conf/spark_conf.properties 中 加载 sparkConf ,并且设置 到 system 属性中去; 根据 配置 构造 classLoader ;准备 启动 container 的 resource list 第二步 启动用户 线程 ,APPMaster 等待 用户线程 sparkContent 初始化 完成 (里面又会 启动 其他一些 后台线程 响应 contianer 的 注册 等) ,pause 用户 线程 第三步 用户线程 sparkContent 初始化 完成 ,APPMaster 线程 开始申请 containers 资源,并且 组装 classpath 和启动 命令 ,启动 containers 想 driver 注册自己 第四步 APPMaster 线程 安装 AMEndpoint 等 恢复 用户 线程; 继续刚才 pause 用户的线程 继续运行
    第五步 等待 用户的线程 的完成

  2. 命令行 报告 job 运行状态
    用户的jar和第三方的jar 都会在 driver 和 executors 中存在的

典型的 spark-defaults.conf 配置

spark.authenticate=false
spark.dynamicAllocation.enabled=true
spark.dynamicAllocation.executorIdleTimeout=60
spark.dynamicAllocation.minExecutors=0
spark.dynamicAllocation.schedulerBacklogTimeout=1
spark.eventLog.enabled=true
spark.serializer=org.apache.spark.serializer.KryoSerializer
spark.shuffle.service.enabled=true
spark.shuffle.service.port=7337
spark.ui.killEnabled=true
spark.master=yarn
spark.submit.deployMode=client
spark.sql.hive.metastore.jars=${env:HADOOP_COMMON_HOME}/../hive/lib/:${env:HADOOP_COMMON_HOME}/client/
spark.sql.hive.metastore.version=1.1.0
spark.sql.catalogImplementation=hive
spark.eventLog.dir=hdfs://nameservice1/user/spark/spark2ApplicationHistory
spark.yarn.historyServer.address=http://njtest-cdh5-nn02.nj:18089
spark.yarn.jars=local:/opt/cloudera/parcels/SPARK2-2.1.0.cloudera2-1.cdh5.7.0.p0.171658/lib/spark2/jars/*
spark.driver.extraLibraryPath=/opt/cloudera/parcels/CDH-5.8.4-1.cdh5.8.4.p0.5/lib/hadoop/lib/native
spark.executor.extraLibraryPath=/opt/cloudera/parcels/CDH-5.8.4-1.cdh5.8.4.p0.5/lib/hadoop/lib/native
spark.yarn.am.extraLibraryPath=/opt/cloudera/parcels/CDH-5.8.4-1.cdh5.8.4.p0.5/lib/hadoop/lib/native
spark.hadoop.mapreduce.application.classpath=
spark.hadoop.yarn.application.classpath=
spark.yarn.config.gatewayPath=/opt/cloudera/parcels
spark.yarn.config.replacementPath={{HADOOP_COMMON_HOME}}/../../..

典型的 spark hdfs stage spark_conf.properties

Spark configuration.
Wed Jul 15 13:50:49 CST 2020
spark.dynamicAllocation.minExecutors=0
spark.shuffle.service.enabled=true
spark.yarn.secondary.jars=expression-Engine-1.0-SNAPSHOT.jar,mysql-connector-java-5.1.39.jar,spark-streaming-kafka-0-10_2.11-2.1.0.jar,kafka_2.11-0.10.0-kafka-2.1.0.jar,kafka-clients-0.10.0-kafka-2.1.0.jar
spark.executor.cores=3
spark.yarn.am.extraLibraryPath=/opt/cloudera/parcels/CDH-5.8.4-1.cdh5.8.4.p0.5/lib/hadoop/lib/native
spark.executor.extraLibraryPath=/opt/cloudera/parcels/CDH-5.8.4-1.cdh5.8.4.p0.5/lib/hadoop/lib/native
spark.yarn.jars=local:/opt/cloudera/parcels/SPARK2-2.1.0.cloudera2-1.cdh5.7.0.p0.171658/lib/spark2/jars/*
spark.hadoop.mapreduce.application.classpath=
spark.executor.memoryoverhead=2g
spark.sql.hive.metastore.jars=${env:HADOOP_COMMON_HOME}/../hive/lib/:${env:HADOOP_COMMON_HOME}/client/
spark.executor.memory=8g
spark.yarn.cache.types=FILE,FILE,FILE,FILE,FILE,FILE
spark.master=yarn
spark.driver.memory=4g
spark.hadoop.yarn.application.classpath=
spark.authenticate=false
spark.driver.extraLibraryPath=/opt/cloudera/parcels/CDH-5.8.4-1.cdh5.8.4.p0.5/lib/hadoop/lib/native
spark.sql.catalogImplementation=hive
spark.submit.deployMode=cluster
spark.dynamicAllocation.enabled=true
spark.sql.hive.metastore.version=1.1.0
spark.app.name=com.saic.portrait.stream.StreamJob
spark.eventLog.enabled=true
spark.shuffle.service.port=7337
spark.yarn.dist.jars=file:/home/center/script/jars/expression-Engine-1.0-SNAPSHOT.jar,file:/home/center/script/jars/mysql-connector-java-5.1.39.jar,file:/home/center/script/jars/spark-streaming-kafka-0-10_2.11-2.1.0.jar,file:/home/center/script/jars/kafka_2.11-0.10.0-kafka-2.1.0.jar,file:/home/center/script/jars/kafka-clients-0.10.0-kafka-2.1.0.jar
spark.yarn.cache.visibilities=PRIVATE,PRIVATE,PRIVATE,PRIVATE,PRIVATE,PRIVATE
spark.yarn.config.replacementPath={{HADOOP_COMMON_HOME}}/../../..
spark.yarn.cache.timestamps=1594792255974,1594792256542,1594792256674,1594792256750,1594792256908,1594792256973
spark.dynamicAllocation.executorIdleTimeout=60
spark.dynamicAllocation.schedulerBacklogTimeout=1
spark.yarn.cache.filenames=hdfs://nameservice1/user/center/.sparkStaging/application_1594720537016_0012/personportrait-1.0-SNAPSHOT.jar#app.jar,hdfs://nameservice1/user/center/.sparkStaging/application_1594720537016_0012/expression-Engine-1.0-SNAPSHOT.jar#expression-Engine-1.0-SNAPSHOT.jar,hdfs://nameservice1/user/center/.sparkStaging/application_1594720537016_0012/mysql-connector-java-5.1.39.jar#mysql-connector-java-5.1.39.jar,hdfs://nameservice1/user/center/.sparkStaging/application_1594720537016_0012/spark-streaming-kafka-0-10_2.11-2.1.0.jar#spark-streaming-kafka-0-10_2.11-2.1.0.jar,hdfs://nameservice1/user/center/.sparkStaging/application_1594720537016_0012/kafka_2.11-0.10.0-kafka-2.1.0.jar#kafka_2.11-0.10.0-kafka-2.1.0.jar,hdfs://nameservice1/user/center/.sparkStaging/application_1594720537016_0012/kafka-clients-0.10.0-kafka-2.1.0.jar#kafka-clients-0.10.0-kafka-2.1.0.jar
spark.serializer=org.apache.spark.serializer.KryoSerializer
spark.yarn.config.gatewayPath=/opt/cloudera/parcels
spark.yarn.cache.sizes=1933911,9994102,989495,216259,5156768,747732
spark.yarn.cache.confArchive=hdfs://nameservice1/user/center/.sparkStaging/application_1594720537016_0012/spark_conf.zip
spark.eventLog.dir=hdfs://nameservice1/user/spark/spark2ApplicationHistory
spark.executor.instances=2
spark.ui.killEnabled=true
spark.yarn.historyServer.address=http://njtest-cdh5-nn02.nj:18089

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https://gitee.com/jsqf/spark_myself_commit.git
git@gitee.com:jsqf/spark_myself_commit.git
jsqf
spark_myself_commit
spark_myself_commit
v2.3.4

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