Flink任务、Spark任务提交到集群，通常需要将可执行Jar上传到集群，手动执行任务提交指令，如果有配套的大数据平台则需要上传Jar，由调度系统进行任务提交。 对开发者来说，本地IDEA调试Flink、Spark任务不涉及对象的序列化及反序列化，任务在本地调试通过后，执行在分布式环境下也可能会出错。 而将任务提交到集群进行调试还要走那些繁琐的流程太影响效率了。
因此，为方便大数据开发人员进行快速开发调试，开发了从本地IDEA提交Flink/Spark任务到集群的工具类。任务提交代码稍加改造后也可以和上层调度系统进行集成，替代脚本模式进行任务提交的方式。
-
支持Flink yarnPerJob模式下的任务提交。
-
支持Spark任务以Yarn Cluster模式提交到YARN，支持自动上传用户Jar包，依赖的Spark Jars需要提前上传到HDFS。
-
支持Spark任务提交到K8s Cluster，执行的jar需要包含在镜像中，任务执行时需要传递镜像名称及可执行文件路径。如果需要操作hive表，则需要传递集群所在文件夹，以及HADOOP_USER_NAME，系统进行Hadoop文件的挂载及环境变量的设置。
- 需要填写Flink任务运行时参数配置，任务运行所在的集群配置路径，本地Flink根路径。项目依赖flink1.10版本。
- 支持以YarnSession、YarnPerjob、Standalone模式进行任务提交，返回ApplicationId。
- example模块下包含一个FlinkDemo，打包后会转移到项目的examplJars中，可以尝试进行任务提交。
- 任务提交后，根据ApplicationId获取任务执行使用的jm、tm日志基本信息，包含日志访问URL,日志总字节大小,根据日志基本信息可以做日志滚动展示，防止Yarn日志过大导致日志读取卡死。
- 提供任务取消、任务状态获取、已完成任务日志获取接口。
V2版本增加flink-yarn-submiter-service，用来做多版本的隔离。新版本任务执行的入口类为 ClusterClient。
任务操作示例：
public static JobParamsInfo buildJobParamsInfo() {
// System.setProperty("java.security.krb5.conf", "/Users/maqi/tmp/hadoopconf/cdh514/krb5.conf");
// 可执行jar包路径
String runJarPath = "/Users/maqi/code/ClustersSubmiter/exampleJars/flink-kafka-reader/flink-kafka-reader.jar";
// 任务参数
String[] execArgs = new String[]{"-jobName", "flink110Submit", "--topic", "mqTest01", "--bootstrapServers", "172.16.8.107:9092"};
// 任务名称
String jobName = "Flink perjob submit";
// flink 文件夹路径
String flinkConfDir = "/Users/maqi/tmp/flink/flink-1.10.0/conf";
// flink lib包路径
String flinkJarPath = "/Users/maqi/tmp/flink/flink-1.10.0/lib";
// yarn 文件夹路径
String yarnConfDir = "/Users/maqi/tmp/hadoopconf/195";
// perjob 运行流任务
String runMode = "yarn_perjob";
// 作业依赖的外部文件
String[] dependFile = new String[]{"/Users/maqi/tmp/flink/flink-1.10.0/README.txt"};
// 任务提交队列
String queue = "c";
// yarnsession appid配置
Properties yarnSessionConfProperties = new Properties();
yarnSessionConfProperties.setProperty("yid", "application_1594265598097_5425");
// savepoint 及并行度相关
Properties confProperties = new Properties();
confProperties.setProperty("parallelism", "1");
JobParamsInfo jobParamsInfo = JobParamsInfo.builder()
.setExecArgs(execArgs)
.setName(jobName)
.setRunJarPath(runJarPath)
.setDependFile(dependFile)
.setFlinkConfDir(flinkConfDir)
.setYarnConfDir(yarnConfDir)
.setConfProperties(confProperties)
.setYarnSessionConfProperties(yarnSessionConfProperties)
.setFlinkJarPath(flinkJarPath)
.setQueue(queue)
.setRunMode(runMode)
.build();
return jobParamsInfo;
}
public static void main(String[] args) throws Exception {
JobParamsInfo jobParamsInfo = buildJobParamsInfo();
// job submit
Optional<Pair<String, String>> appIdAndJobId = submitFlinkJob(jobParamsInfo);
// running log info
appIdAndJobId.ifPresent((pair) -> printRollingLogBaseInfo(jobParamsInfo, pair));
// cancel job
Pair<String, String> job = new Pair<>("application_1594265598097_2688", "35a679c9f94311a8a8084e4d8d06a95d");
cancelFlinkJob(jobParamsInfo, job);
// flink job status
ETaskStatus jobStatus = getJobStatus(jobParamsInfo, new Pair<>("application_1594265598097_5425", "fa4ae50441c5d5363e8abbe5623e115a"));
System.out.println("job status is : " + jobStatus.toString());
// print finished Log
printFinishedLog(jobParamsInfo,"application_1594961717891_0103");
}
jobmanager日志格式:
{
"logs":[
{
"name":"jobmanager.err ",
"totalBytes":"555",
"url":"http://172-16-10-204:8042/node/containerlogs/container_e185_1593317332045_2246_01_000002/admin/jobmanager.err/"
},
{
"name":"jobmanager.log ",
"totalBytes":"31944",
"url":"http://172-16-10-204:8042/node/containerlogs/container_e185_1593317332045_2246_01_000002/admin/jobmanager.log/"
},
{
"name":"jobmanager.out ",
"totalBytes":"0",
"url":"http://172-16-10-204:8042/node/containerlogs/container_e185_1593317332045_2246_01_000002/admin/jobmanager.out/"
}
],
"typeName":"jobmanager"
}taskmanager日志格式:
{
"logs":[
{
"name":"taskmanager.err ",
"totalBytes":"560",
"url":"http://node03:8042/node/containerlogs/container_e27_1593571725037_0170_01_000002/admin/taskmanager.err/"
},
{
"name":"taskmanager.log ",
"totalBytes":"35937",
"url":"http://node03:8042/node/containerlogs/container_e27_1593571725037_0170_01_000002/admin/taskmanager.log/"
},
{
"name":"taskmanager.out ",
"totalBytes":"0",
"url":"http://node03:8042/node/containerlogs/container_e27_1593571725037_0170_01_000002/admin/taskmanager.out/"
}
],
"otherInfo":"{"dataPort":36218,"freeSlots":0,"hardware":{"cpuCores":4,"freeMemory":241172480,"managedMemory":308700779,"physicalMemory":8201641984},"id":"container_e27_1593571725037_0170_01_000002","path":"akka.tcp://flink@node03:36791/user/taskmanager_0","slotsNumber":1,"timeSinceLastHeartbeat":1593659561129}",
"typeName":"taskmanager"
}- 填写用户程序包路径、执行参数、集群配置文件夹、安全认证等相关配置。
- Spark任务提交使用Yarn cluster模式，使用的Spark Jar需要提前上传到HDFS并作为archive的参数。
- 针对SparkSQL任务，通过提交examples中的spark-sql-proxy程序包来直接操作hive表。
提交示例：
public static void main(String[] args) throws Exception {
boolean openKerberos = true;
String appName = "todd spark submit";
String runJarPath = "/Users/maqi/code/ClustersSubmiter/exampleJars/spark-sql-proxy/spark-sql-proxy.jar";
String mainClass = "cn.todd.spark.SparksqlProxy";
String yarnConfDir = "/Users/maqi/tmp/hadoopconf";
String principal = "hdfs/[email protected]";
String keyTab = "/Users/maqi/tmp/hadoopconf/hdfs.keytab";
String jarHdfsDir = "sparkproxy2";
String archive = "hdfs://nameservice1/sparkjars/jars";
String queue = "root.users.hdfs";
String execArgs = getExampleJobParams();
Properties confProperties = new Properties();
confProperties.setProperty("spark.executor.cores","2");
JobParamsInfo jobParamsInfo = JobParamsInfo.builder()
.setAppName(appName)
.setRunJarPath(runJarPath)
.setMainClass(mainClass)
.setYarnConfDir(yarnConfDir)
.setPrincipal(principal)
.setKeytab(keyTab)
.setJarHdfsDir(jarHdfsDir)
.setArchivePath(archive)
.setQueue(queue)
.setExecArgs(execArgs)
.setConfProperties(confProperties)
.setOpenKerberos(BooleanUtils.toString(openKerberos, "true", "false"))
.build();
YarnConfiguration yarnConf = YarnConfLoaderUtil.getYarnConf(yarnConfDir);
String applicationId = "";
if (BooleanUtils.toBoolean(openKerberos)) {
UserGroupInformation.setConfiguration(yarnConf);
UserGroupInformation userGroupInformation = UserGroupInformation.loginUserFromKeytabAndReturnUGI(principal, keyTab);
applicationId = userGroupInformation.doAs((PrivilegedExceptionAction<String>) () -> LauncherMain.run(jobParamsInfo, yarnConf));
} else {
LauncherMain.run(jobParamsInfo, yarnConf);
}
System.out.println(applicationId);
}- 基于Spark2.4.4进行开发，通过将spark-sql-proxy.jar包打入镜像来执行Sparksql并操作Hive表，无其他特殊操作。
- 操作Hive时需要传递hadoopConfDir,程序会自动将.xml文件内容进行挂载，如果非root用户操作Hive,需要设置HADOOP_USER_NAME。
- 通过读取kubeConfig配置文件进行Kuberclient的创建,而非官方提供的master url方式。
- 任务提交后立即返回spark-app-selector id,从而进行POD状态获取。
public static void main(String[] args) throws Exception {
String appName = "todd spark submit";
// 镜像内的jar路径
String runJarPath = "local:///opt/dtstack/spark/spark-sql-proxy.jar";
String mainClass = "cn.todd.spark.SparksqlProxy";
String hadoopConfDir = "/Users/maqi/tmp/hadoopconf/";
String kubeConfig = "/Users/maqi/tmp/conf/k8s.config";
String imageName = "mqspark:2.4.4";
String execArgs = getExampleJobParams();
Properties confProperties = new Properties();
confProperties.setProperty("spark.executor.instances", "2");
confProperties.setProperty("spark.kubernetes.namespace", "default");
confProperties.setProperty("spark.kubernetes.authenticate.driver.serviceAccountName", "spark");
confProperties.setProperty("spark.kubernetes.container.image.pullPolicy", "IfNotPresent");
JobParamsInfo jobParamsInfo = JobParamsInfo.builder()
.setAppName(appName)
.setRunJarPath(runJarPath)
.setMainClass(mainClass)
.setExecArgs(execArgs)
.setConfProperties(confProperties)
.setHadoopConfDir(hadoopConfDir)
.setKubeConfig(kubeConfig)
.setImageName(imageName)
.build();
String id = run(jobParamsInfo);
System.out.println(id);
}![dependabot[bot] avatar](https://avatars.githubusercontent.com/in/29110?v=4 "dependabot[bot]")
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