【Java Kubernates】Java调用kubernates提交Yaml到SparkOperator

背景

目前查询框架使用的是trino，但是trino也有其局限性，需要准备一个备用的查询框架。考虑使用spark，spark operator也已经部署到k8s，现在需要定向提交spark sql到k8s的sparkoperator上，使用k8s资源执行sql。

对比

查询了java调用k8s的框架，有两个：fabric8io/kubernetes-client和kubernetes-client/java，fabric8io/kubernetes-client：始于2015年，用户很多，社区活跃。Fabric8项目的愿景是成为运行在Kubernetes之上的云原生微服务的PaaS平台。Fabric8 Kubernetes客户端在Fabric8生态系统中发挥了关键作用，因为它是Kubernetes REST API的抽象

kubernetes-client/java：官方的Kubernetes Java客户端是由Brendan Burns（他也是Kubernetes的创始人）和其他几个用于其他语言（如PERL、Javascript、Python等）的客户端于2017年底启动的。所有客户端似乎都是从一个通用的OpenAPI生成器脚本生成的:kubernetes-client/gen和Java客户端也是以相同的方式生成的。因此，它的用法与使用该脚本生成的其他客户端相似。

具体可以参考下面这篇文章

https://itnext.io/difference-between-fabric8-and-official-kubernetes-java-client-3e0a994fd4aficon-default.png?t=N7T8https://itnext.io/difference-between-fabric8-and-official-kubernetes-java-client-3e0a994fd4af

最终我选择了fabric8io，因为我们需要使用k8s的自定义资源sparkApplication，对于自定义资源，kubernetes-client/java需要创建各个k8s对象的pojo，比较麻烦。而fabric8io/kubernetes-client支持两种方式，一种和前者一样，创建pojo，还有一种方式使用GenericKubernetesResource动态创建并使用自定义资源，为了简便，选择使用fabric8io。

Spark Operator镜像及部署

spark operator的部署不再介绍，参考我前面的博客文章。

这里提一下，我在重新使用spark operator的时候，发现原来官方的google的spark operator镜像已经不能拉取了，貌似是google发现它的两个镜像存在漏洞，所以关闭了开源镜像。重新寻找了类似的镜像，发现了有openlake的spark镜像。拉取spark和spark operator镜像

https://hub.docker.com/u/openlake

程序调用架构

一：主程序（Main App）

编写主程序，即调用spark的主要代码。将下面的程序打包成jar，比如我的zyspark-0.0.1-SNAPSHOT.jar

import java.io.File;
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.ResultSet;
import java.sql.Statement;
import java.util.ArrayList;
import java.util.List;import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.s3a.S3AFileSystem;
import org.apache.spark.SparkConf;
import org.apache.spark.SparkContext;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;public class SparkDemo {public static void main(String[] args) throws Exception{sparkQueryForFhc();	}public static void sparkQueryForFhc() throws Exception{System.out.println("=========================1");String warehouseLocation = new File("spark-warehouse").getAbsolutePath();System.out.println("===========================2");String metastoreUri = "thrift://10.40.8.200:5000";SparkConf sparkConf = new SparkConf();sparkConf.set("fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem");sparkConf.set("fs.s3a.access.key", "apPeWWr5KpXkzEW2jNKW");sparkConf.set("spark.hadoop.fs.s3a.path.style.access", "true");sparkConf.set("spark.hadoop.fs.s3a.connection.ssl.enabled", "true");sparkConf.set("fs.s3a.secret.key", "cRt3inWAhDYtuzsDnKGLGg9EJSbJ083ekuW7PejM");sparkConf.set("fs.s3a.endpoint", "wuxdimiov001.seagate.com:9000"); // 替换为实际的 S3 存储的地址sparkConf.set("spark.hadoop.fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem");sparkConf.set("spark.sql.metastore.uris", metastoreUri);sparkConf.set("spark.sql.warehouse.dir", warehouseLocation);sparkConf.set("spark.sql.catalogImplementation", "hive");sparkConf.set("hive.metastore.uris", metastoreUri);//Class.forName("org.apache.hadoop.fs.s3a.S3AFileSystem");long zhenyang2 =  System.currentTimeMillis();SparkSession sparkSession = SparkSession.builder().appName("Fhc Spark Query").config(sparkConf).enableHiveSupport().getOrCreate();System.out.println("sparkSession create cost:"+(System.currentTimeMillis()-zhenyang2));System.out.println("==============================3");// 获取 SparkConf 对象String exesql = sparkSession.sparkContext().getConf().get("spark.query.sql");System.out.println("==============================3.1:"+exesql);System.out.println("Hive Metastore URI: " + sparkConf.get("spark.sql.metastore.uris"));System.out.println("Hive Warehouse Directory: " + sparkConf.get("spark.sql.warehouse.dir"));System.out.println("SHOW DATABASES==============================3.1:"+exesql);sparkSession.sql("SHOW DATABASES").show();long zhenyang3 =  System.currentTimeMillis();Dataset<Row> sqlDF = sparkSession.sql(exesql);System.out.println("sparkSession sql:"+(System.currentTimeMillis()-zhenyang3));System.out.println("======================4");System.out.println("===========sqlDF count===========:"+sqlDF.count());sqlDF.show();long zhenyang5 =  System.currentTimeMillis();List<Row> jaList= sqlDF.javaRDD().collect();System.out.println("rdd collect cost:"+(System.currentTimeMillis()-zhenyang5));System.out.println("jaList list:"+jaList.size());List<TaskListModel> list = new ArrayList<TaskListModel>();long zhenyang4 =  System.currentTimeMillis();jaList.stream().forEachOrdered(result -> {System.out.println("serial_num is :"+result.getString(1));});System.out.println("SparkDemo foreach cost:"+(System.currentTimeMillis()-zhenyang4));System.out.println("=========================5");sparkSession.close();}}

二：调用k8s程序（Spark App）

  • 首先保证spark operator驱动程序已经发布在k8s集群
  • 创建一个springboot程序，开放restful接口，接收传入的参数，比如spark的driver和executor参数，cpu，内存，instance个数等，及传入的需要运行的sql。
  • 组织yaml内容，使用fabric8io将yaml提交到k8s执行

maven导入fabric8io包

    <dependency><groupId>io.fabric8</groupId><artifactId>kubernetes-client</artifactId><version>6.1.1</version> <!-- 替换为实际版本 --></dependency>

代码

注：因为sparkapplication是在k8s的自定义资源，应使用CustomResourceDefinitionContext来加载sparkapplication程序，提交到k8s的核心代码在submitSparkApplicationFabi2方法。

import java.io.File;
import java.io.FileReader;
import java.io.InputStream;
import java.util.HashMap;
import java.util.Map;import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.PostMapping;
import org.springframework.web.bind.annotation.RequestBody;
import org.springframework.web.bind.annotation.RestController;import io.fabric8.kubernetes.api.model.GenericKubernetesResource;
import io.fabric8.kubernetes.client.*;
import io.fabric8.kubernetes.client.dsl.base.CustomResourceDefinitionContext;
import io.fabric8.kubernetes.client.utils.Serialization;
import io.kubernetes.client.openapi.ApiClient;
import io.kubernetes.client.openapi.Configuration;
import io.kubernetes.client.openapi.apis.CustomObjectsApi;
import io.kubernetes.client.util.ClientBuilder;
import io.kubernetes.client.util.KubeConfig;
import io.kubernetes.client.util.Yaml;@RestController
public class SparkSqlController {@GetMapping(value = "/test")public String test() {System.out.println("test()");return "Spring Boot SparkSqlController:Hello World";}@PostMapping(value = "/submitSparkSql",consumes="application/json;charset=utf-8")public String executeSparkSql(@RequestBody Object message1) throws Exception {String errorInf = "";@SuppressWarnings("unchecked")Map<String,Object> message = (Map<String,Object>)message1;System.out.println(message);String taskName = String.valueOf(message.get("taskName"));String sparkImage = String.valueOf(message.get("sparkImage"));String mainClass = String.valueOf(message.get("mainClass"));String sparkJarFile = String.valueOf(message.get("sparkJarFile"));String driverCpu = String.valueOf(message.get("driverCpu"));String driverMemory = String.valueOf(message.get("driverMemory"));String executorCpu = String.valueOf(message.get("executorCpu"));String instance = String.valueOf(message.get("instance"));String executorMemory = String.valueOf(message.get("executorMemory"));String dynamicSQLQuery = String.valueOf(message.get("dynamicSQLQuery"));// 构建 SparkApplication YAML 配置String sparkApplicationYAML = buildSparkApplicationYAML(taskName, sparkImage, sparkJarFile, mainClass, instance, driverCpu, driverMemory, executorCpu, executorMemory, dynamicSQLQuery);System.out.println(sparkApplicationYAML);// 提交 SparkApplication 到 KubernetessubmitSparkApplicationFabi2(sparkApplicationYAML);return null;}//组织yaml，根据动态传入的参数生成yaml	private static String buildSparkApplicationYAML(String taskName, String sparkImage, String sparkJarFile, String mainClass, String instance,String driverCpu, String driverMemory, String executorCpu, String executorMemory, String dynamicSQLQuery) {return String.format("apiVersion: \"sparkoperator.k8s.io/v1beta2\"\n" +"kind: SparkApplication\n" +"metadata:\n" +"  name: %s\n" +"  namespace: spark-app\n" +"spec:\n" +"  type: Scala\n" +"  mode: cluster\n" +"  image: \"%s\"\n" +"  imagePullPolicy: Always\n" +"  imagePullSecrets: [\"harbor\"]\n" +"  mainClass: \"%s\"\n" +"  mainApplicationFile: \"%s\"\n" +"  sparkVersion: \"3.3.1\"\n" +"  restartPolicy:\n" +"    type: Never\n" +"  volumes:\n" +"    - name: nfs-spark-volume\n" +"      persistentVolumeClaim:\n" +"        claimName: sparkcode\n" +"  driver:\n" +"    cores: %s\n" +"    coreLimit: \"1200m\"\n" +"    memory: \"%s\"\n" +"    labels:\n" +"      version: 3.3.1\n" +"    serviceAccount: spark-svc-account\n" +"    volumeMounts:\n" +"      - name: nfs-spark-volume\n" +"        mountPath: \"/app/sparkcode\"\n" +"    env:\n" +"      - name: AWS_REGION\n" +"        valueFrom:\n" +"          secretKeyRef:\n" +"            name: minio-secret\n" +"            key: AWS_REGION\n" +"      - name: AWS_ACCESS_KEY_ID\n" +"        valueFrom:\n" +"          secretKeyRef:\n" +"            name: minio-secret\n" +"            key: AWS_ACCESS_KEY_ID\n" +"      - name: AWS_SECRET_ACCESS_KEY\n" +"        valueFrom:\n" +"          secretKeyRef:\n" +"            name: minio-secret\n" +"            key: AWS_SECRET_ACCESS_KEY\n" +"      - name: MINIO_ENDPOINT\n" +"        valueFrom:\n" +"          secretKeyRef:\n" +"            name: minio-secret\n" +"            key: MINIO_ENDPOINT\n" +"      - name: MINIO_HOST\n" +"        valueFrom:\n" +"          secretKeyRef:\n" +"            name: minio-secret\n" +"            key: MINIO_HOST\n" +"      - name: BUCKET_NAME\n" +"        valueFrom:\n" +"          secretKeyRef:\n" +"            name: minio-secret\n" +"            key: BUCKET_NAME\n" +"  executor:\n" +"    cores: %s\n" +"    instances: %s\n" +"    memory: \"%s\"\n" +"    labels:\n" +"      version: 3.3.1\n" +"    volumeMounts:\n" +"      - name: nfs-spark-volume\n" +"        mountPath: \"/app/sparkcode\"\n" +"    env:\n" +"      - name: AWS_REGION\n" +"        valueFrom:\n" +"          secretKeyRef:\n" +"            name: minio-secret\n" +"            key: AWS_REGION\n" +"      - name: AWS_ACCESS_KEY_ID\n" +"        valueFrom:\n" +"          secretKeyRef:\n" +"            name: minio-secret\n" +"            key: AWS_ACCESS_KEY_ID\n" +"      - name: AWS_SECRET_ACCESS_KEY\n" +"        valueFrom:\n" +"          secretKeyRef:\n" +"            name: minio-secret\n" +"            key: AWS_SECRET_ACCESS_KEY\n" +"      - name: MINIO_ENDPOINT\n" +"        valueFrom:\n" +"          secretKeyRef:\n" +"            name: minio-secret\n" +"            key: MINIO_ENDPOINT\n" +"      - name: MINIO_HOST\n" +"        valueFrom:\n" +"          secretKeyRef:\n" +"            name: minio-secret\n" +"            key: MINIO_HOST\n" +"      - name: BUCKET_NAME\n" +"        valueFrom:\n" +"          secretKeyRef:\n" +"            name: minio-secret\n" +"            key: BUCKET_NAME\n" +"  sparkConf:\n" +"    spark.query.sql: \"%s\"",taskName,sparkImage,mainClass,sparkJarFile,driverCpu,driverMemory,executorCpu,instance,executorMemory,dynamicSQLQuery);}private static void submitSparkApplicationFabi2(String sparkApplicationYAML) throws Exception{try (KubernetesClient client = new KubernetesClientBuilder().build()) {//默认读取~/.kube/config的配置CustomResourceDefinitionContext animalCrdContext = new CustomResourceDefinitionContext.Builder().withName("sparkapplications.sparkoperator.k8s.io").withGroup("sparkoperator.k8s.io").withKind("SparkApplication").withScope("Namespaced").withVersion("v1beta2").withPlural("sparkapplications").build();GenericKubernetesResource cr3 = Serialization.unmarshal(sparkApplicationYAML, GenericKubernetesResource.class);client.genericKubernetesResources(animalCrdContext).inNamespace("spark-app").resource(cr3).create();System.out.println("over");} catch (Exception e) {e.printStackTrace();}}}

实际的生成的yaml

apiVersion: "sparkoperator.k8s.io/v1beta2"
kind: SparkApplication
metadata:name: zy-sparknamespace: spark-app
spec:type: Scalamode: clusterimage: "10.38.199.203:1443/fhc/zy-spark:v0.1"  //以openlake/spark镜像为基准的本地镜像imagePullPolicy: AlwaysimagePullSecrets: ["harbor"]mainClass: com.seagate.client.zyspark.SparkDemo //主程序入口mainApplicationFile: "local:///app/sparkcode/zyspark-0.0.1-SNAPSHOT.jar"  //主程序sparkVersion: "3.3.1"restartPolicy:type: Nevervolumes:- name: nfs-spark-volumepersistentVolumeClaim:claimName: sparkcodedriver:cores: 1coreLimit: "1200m"memory: "2G"labels:version: 3.3.1serviceAccount: spark-svc-accountvolumeMounts:- name: nfs-spark-volumemountPath: "/app/sparkcode"env:    //以下为minio的访问参数- name: AWS_REGIONvalueFrom:secretKeyRef:name: minio-secretkey: AWS_REGION- name: AWS_ACCESS_KEY_IDvalueFrom:secretKeyRef:name: minio-secretkey: AWS_ACCESS_KEY_ID- name: AWS_SECRET_ACCESS_KEYvalueFrom:secretKeyRef:name: minio-secretkey: AWS_SECRET_ACCESS_KEY- name: MINIO_ENDPOINTvalueFrom:secretKeyRef:name: minio-secretkey: MINIO_ENDPOINT- name: MINIO_HOSTvalueFrom:secretKeyRef:name: minio-secretkey: MINIO_HOST- name: BUCKET_NAMEvalueFrom:secretKeyRef:name: minio-secretkey: BUCKET_NAMEexecutor:cores: 1instances: 10memory: "1G"labels:version: 3.3.1volumeMounts:- name: nfs-spark-volumemountPath: "/app/sparkcode"env:- name: AWS_REGIONvalueFrom:secretKeyRef:name: minio-secretkey: AWS_REGION- name: AWS_ACCESS_KEY_IDvalueFrom:secretKeyRef:name: minio-secretkey: AWS_ACCESS_KEY_ID- name: AWS_SECRET_ACCESS_KEYvalueFrom:secretKeyRef:name: minio-secretkey: AWS_SECRET_ACCESS_KEY- name: MINIO_ENDPOINTvalueFrom:secretKeyRef:name: minio-secretkey: MINIO_ENDPOINT- name: MINIO_HOSTvalueFrom:secretKeyRef:name: minio-secretkey: MINIO_HOST- name: BUCKET_NAMEvalueFrom:secretKeyRef:name: minio-secretkey: BUCKET_NAMEsparkConf:spark.query.sql: "select * from  cimarronbp_n.p025_load_stat limit 10" //传入的sql

三：传入参数，调用restful接口（Client）

public static void main(String[] args) throws Exception{Map<String,Object> param = new HashMap<String, Object>();param.put("taskName", "spark"+System.currentTimeMillis());param.put("sparkImage", "10.38.199.203:1443/fhc/zy-spark:v0.1");param.put("mainClass", "com.seagate.client.zyspark.SparkDemo");param.put("sparkJarFile", "local:///app/sparkcode/zyspark-0.0.1-SNAPSHOT.jar");param.put("driverCpu", "1");param.put("driverMemory", "1G");param.put("executorCpu", "1");param.put("instance", "5");param.put("executorMemory", "2G");param.put("dynamicSQLQuery", "select * from  cimarronbp_n.p025_load_stat limit 10");callSparkSqk(JSONObject.toJSON(param));
}

四：查看log

查看rancher的spark-app的namesapce下面，生成了driver和executor 的pod

查看driver的log，生成了453个task

++ id -u
+ myuid=1000
++ id -g
+ mygid=1000
+ set +e
++ getent passwd 1000
+ uidentry=hive:x:1000:1000::/home/hive:/bin/bash
+ set -e
+ '[' -z hive:x:1000:1000::/home/hive:/bin/bash ']'
+ '[' -z /usr/local/openjdk-11 ']'
+ SPARK_CLASSPATH=':/opt/spark/jars/*'
+ env
+ sort -t_ -k4 -n
+ grep SPARK_JAVA_OPT_
+ sed 's/[^=]*=\(.*\)/\1/g'
+ readarray -t SPARK_EXECUTOR_JAVA_OPTS
+ '[' -n '' ']'
+ '[' -z ']'
+ '[' -z ']'
+ '[' -n '' ']'
+ '[' -z ']'
+ '[' -z x ']'
+ SPARK_CLASSPATH='/opt/spark/conf::/opt/spark/jars/*'
+ case "$1" in
+ shift 1
+ CMD=("$SPARK_HOME/bin/spark-submit" --conf "spark.driver.bindAddress=$SPARK_DRIVER_BIND_ADDRESS" --deploy-mode client "$@")
+ exec /usr/bin/tini -s -- /opt/spark/bin/spark-submit --conf spark.driver.bindAddress=10.42.2.226 --deploy-mode client --properties-file /opt/spark/conf/spark.properties --class com.seagate.client.zyspark.SparkDemo local:///app/sparkcode/zyspark-0.0.1-SNAPSHOT.jar
24/01/24 07:31:21 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
=========================1
===========================2
24/01/24 07:31:21 INFO HiveConf: Found configuration file null
24/01/24 07:31:22 INFO SparkContext: Running Spark version 3.3.2
24/01/24 07:31:22 INFO ResourceUtils: ==============================================================
24/01/24 07:31:22 INFO ResourceUtils: No custom resources configured for spark.driver.
24/01/24 07:31:22 INFO ResourceUtils: ==============================================================
24/01/24 07:31:22 INFO SparkContext: Submitted application: spark1706081470242
24/01/24 07:31:22 INFO ResourceProfile: Default ResourceProfile created, executor resources: Map(cores -> name: cores, amount: 1, script: , vendor: , memory -> name: memory, amount: 2048, script: , vendor: , offHeap -> name: offHeap, amount: 0, script: , vendor: ), task resources: Map(cpus -> name: cpus, amount: 1.0)
24/01/24 07:31:22 INFO ResourceProfile: Limiting resource is cpus at 1 tasks per executor
24/01/24 07:31:22 INFO ResourceProfileManager: Added ResourceProfile id: 0
24/01/24 07:31:22 INFO SecurityManager: Changing view acls to: hive,root
24/01/24 07:31:22 INFO SecurityManager: Changing modify acls to: hive,root
24/01/24 07:31:22 INFO SecurityManager: Changing view acls groups to: 
24/01/24 07:31:22 INFO SecurityManager: Changing modify acls groups to: 
24/01/24 07:31:22 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users  with view permissions: Set(hive, root); groups with view permissions: Set(); users  with modify permissions: Set(hive, root); groups with modify permissions: Set()
24/01/24 07:31:22 INFO Utils: Successfully started service 'sparkDriver' on port 7078.
24/01/24 07:31:22 INFO SparkEnv: Registering MapOutputTracker
24/01/24 07:31:22 INFO SparkEnv: Registering BlockManagerMaster
24/01/24 07:31:23 INFO BlockManagerMasterEndpoint: Using org.apache.spark.storage.DefaultTopologyMapper for getting topology information
24/01/24 07:31:23 INFO BlockManagerMasterEndpoint: BlockManagerMasterEndpoint up
24/01/24 07:31:23 INFO SparkEnv: Registering BlockManagerMasterHeartbeat
24/01/24 07:31:23 INFO DiskBlockManager: Created local directory at /var/data/spark-f47fd19b-5ec5-4ed4-9bb8-d43710f560da/blockmgr-823ae0ab-d2a2-4448-a920-2c80674a13c4
24/01/24 07:31:23 INFO MemoryStore: MemoryStore started with capacity 413.9 MiB
24/01/24 07:31:23 INFO SparkEnv: Registering OutputCommitCoordinator
24/01/24 07:31:23 INFO Utils: Successfully started service 'SparkUI' on port 4040.
24/01/24 07:31:23 INFO SparkContext: Added JAR local:///app/sparkcode/zyspark-0.0.1-SNAPSHOT.jar at file:/app/sparkcode/zyspark-0.0.1-SNAPSHOT.jar with timestamp 1706081482116
24/01/24 07:31:23 INFO SparkContext: The JAR local:///app/sparkcode/zyspark-0.0.1-SNAPSHOT.jar at file:/app/sparkcode/zyspark-0.0.1-SNAPSHOT.jar has been added already. Overwriting of added jar is not supported in the current version.
24/01/24 07:31:23 INFO SparkKubernetesClientFactory: Auto-configuring K8S client using current context from users K8S config file
24/01/24 07:31:25 INFO ExecutorPodsAllocator: Going to request 5 executors from Kubernetes for ResourceProfile Id: 0, target: 5, known: 0, sharedSlotFromPendingPods: 2147483647.
24/01/24 07:31:25 INFO BasicExecutorFeatureStep: Decommissioning not enabled, skipping shutdown script
24/01/24 07:31:26 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 7079.
24/01/24 07:31:26 INFO NettyBlockTransferService: Server created on spark1706081470242-7219f68d3a6157ef-driver-svc.spark-app.svc:7079
24/01/24 07:31:26 INFO BlockManager: Using org.apache.spark.storage.RandomBlockReplicationPolicy for block replication policy
24/01/24 07:31:26 INFO BlockManagerMaster: Registering BlockManager BlockManagerId(driver, spark1706081470242-7219f68d3a6157ef-driver-svc.spark-app.svc, 7079, None)
24/01/24 07:31:26 INFO BlockManagerMasterEndpoint: Registering block manager spark1706081470242-7219f68d3a6157ef-driver-svc.spark-app.svc:7079 with 413.9 MiB RAM, BlockManagerId(driver, spark1706081470242-7219f68d3a6157ef-driver-svc.spark-app.svc, 7079, None)
24/01/24 07:31:26 INFO BlockManagerMaster: Registered BlockManager BlockManagerId(driver, spark1706081470242-7219f68d3a6157ef-driver-svc.spark-app.svc, 7079, None)
24/01/24 07:31:26 INFO BlockManager: Initialized BlockManager: BlockManagerId(driver, spark1706081470242-7219f68d3a6157ef-driver-svc.spark-app.svc, 7079, None)
24/01/24 07:31:26 INFO BasicExecutorFeatureStep: Decommissioning not enabled, skipping shutdown script
24/01/24 07:31:26 INFO BasicExecutorFeatureStep: Decommissioning not enabled, skipping shutdown script
24/01/24 07:31:26 INFO BasicExecutorFeatureStep: Decommissioning not enabled, skipping shutdown script
24/01/24 07:31:26 INFO BasicExecutorFeatureStep: Decommissioning not enabled, skipping shutdown script
24/01/24 07:31:30 INFO KubernetesClusterSchedulerBackend$KubernetesDriverEndpoint: Registered executor NettyRpcEndpointRef(spark-client://Executor) (10.42.4.3:50280) with ID 3,  ResourceProfileId 0
24/01/24 07:31:30 INFO BlockManagerMasterEndpoint: Registering block manager 10.42.4.3:38355 with 1048.8 MiB RAM, BlockManagerId(3, 10.42.4.3, 38355, None)
24/01/24 07:31:30 INFO KubernetesClusterSchedulerBackend$KubernetesDriverEndpoint: Registered executor NettyRpcEndpointRef(spark-client://Executor) (10.42.3.163:33768) with ID 2,  ResourceProfileId 0
24/01/24 07:31:30 INFO BlockManagerMasterEndpoint: Registering block manager 10.42.3.163:40237 with 1048.8 MiB RAM, BlockManagerId(2, 10.42.3.163, 40237, None)
24/01/24 07:31:30 INFO KubernetesClusterSchedulerBackend$KubernetesDriverEndpoint: Registered executor NettyRpcEndpointRef(spark-client://Executor) (10.42.7.131:56864) with ID 5,  ResourceProfileId 0
24/01/24 07:31:30 INFO BlockManagerMasterEndpoint: Registering block manager 10.42.7.131:45533 with 1048.8 MiB RAM, BlockManagerId(5, 10.42.7.131, 45533, None)
24/01/24 07:31:30 INFO KubernetesClusterSchedulerBackend$KubernetesDriverEndpoint: Registered executor NettyRpcEndpointRef(spark-client://Executor) (10.42.2.227:52014) with ID 4,  ResourceProfileId 0
24/01/24 07:31:31 INFO KubernetesClusterSchedulerBackend: SchedulerBackend is ready for scheduling beginning after reached minRegisteredResourcesRatio: 0.8
sparkSession create cost:9290
==============================3
==============================3.1:select * from  cimarronbp_n.p025_load_stat limit 10
Hive Metastore URI: thrift://10.40.8.200:5000
Hive Warehouse Directory: /opt/spark/work-dir/spark-warehouse
SHOW DATABASES==============================3.1:select * from  cimarronbp_n.p025_load_stat limit 10
24/01/24 07:31:31 INFO BlockManagerMasterEndpoint: Registering block manager 10.42.2.227:43975 with 1048.8 MiB RAM, BlockManagerId(4, 10.42.2.227, 43975, None)
24/01/24 07:31:31 INFO SharedState: Setting hive.metastore.warehouse.dir ('null') to the value of spark.sql.warehouse.dir.
24/01/24 07:31:31 INFO SharedState: Warehouse path is 'file:/opt/spark/work-dir/spark-warehouse'.
24/01/24 07:31:31 INFO KubernetesClusterSchedulerBackend$KubernetesDriverEndpoint: Registered executor NettyRpcEndpointRef(spark-client://Executor) (10.42.6.99:42804) with ID 1,  ResourceProfileId 0
24/01/24 07:31:31 INFO BlockManagerMasterEndpoint: Registering block manager 10.42.6.99:36793 with 1048.8 MiB RAM, BlockManagerId(1, 10.42.6.99, 36793, None)
24/01/24 07:31:36 INFO HiveUtils: Initializing HiveMetastoreConnection version 2.3.9 using Spark classes.
24/01/24 07:31:36 INFO HiveClientImpl: Warehouse location for Hive client (version 2.3.9) is file:/opt/spark/work-dir/spark-warehouse
24/01/24 07:31:36 INFO metastore: Trying to connect to metastore with URI thrift://10.40.8.200:5000
24/01/24 07:31:36 INFO metastore: Opened a connection to metastore, current connections: 1
24/01/24 07:31:36 INFO metastore: Connected to metastore.
24/01/24 07:31:37 INFO CodeGenerator: Code generated in 329.422595 ms
24/01/24 07:31:37 INFO CodeGenerator: Code generated in 8.039488 ms
24/01/24 07:31:37 INFO CodeGenerator: Code generated in 8.313137 ms
+------------+
|   namespace|
+------------+
|  cimarronbp|
|cimarronbp_n|
|     default|
|        idat|
|     mintest|
|mintestsmall|
|         ods|
+------------+sparkSession sql:396
======================4
24/01/24 07:31:38 INFO DataSourceStrategy: Pruning directories with: 
24/01/24 07:31:39 INFO CodeGenerator: Code generated in 11.76912 ms
24/01/24 07:31:39 INFO SQLStdHiveAccessController: Created SQLStdHiveAccessController for session context : HiveAuthzSessionContext [sessionString=486be1dc-3907-49cb-ad05-f4200a382ae5, clientType=HIVECLI]
24/01/24 07:31:39 WARN SessionState: METASTORE_FILTER_HOOK will be ignored, since hive.security.authorization.manager is set to instance of HiveAuthorizerFactory.
24/01/24 07:31:39 INFO metastore: Mestastore configuration hive.metastore.filter.hook changed from org.apache.hadoop.hive.metastore.DefaultMetaStoreFilterHookImpl to org.apache.hadoop.hive.ql.security.authorization.plugin.AuthorizationMetaStoreFilterHook
24/01/24 07:31:39 INFO metastore: Closed a connection to metastore, current connections: 0
24/01/24 07:31:39 INFO metastore: Trying to connect to metastore with URI thrift://10.40.8.200:5000
24/01/24 07:31:39 INFO metastore: Opened a connection to metastore, current connections: 1
24/01/24 07:31:39 INFO metastore: Connected to metastore.
24/01/24 07:31:39 INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 215.8 KiB, free 413.7 MiB)
24/01/24 07:31:39 INFO MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 36.3 KiB, free 413.7 MiB)
24/01/24 07:31:39 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on spark1706081470242-7219f68d3a6157ef-driver-svc.spark-app.svc:7079 (size: 36.3 KiB, free: 413.9 MiB)
24/01/24 07:31:39 INFO SparkContext: Created broadcast 0 from 
24/01/24 07:31:39 INFO metastore: Trying to connect to metastore with URI thrift://10.40.8.200:5000
24/01/24 07:31:39 INFO metastore: Opened a connection to metastore, current connections: 2
24/01/24 07:31:39 INFO metastore: Connected to metastore.
24/01/24 07:31:50 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
24/01/24 07:31:50 INFO MetricsSystemImpl: Scheduled Metric snapshot period at 10 second(s).24/01/24 07:31:58 INFO DAGScheduler: Registering RDD 1248 (count at SparkDemo.java:443) as input to shuffle 0
24/01/24 07:31:58 INFO DAGScheduler: Got map stage job 0 (count at SparkDemo.java:443) with 453 output partitions
24/01/24 07:31:58 INFO DAGScheduler: Final stage: ShuffleMapStage 0 (count at SparkDemo.java:443)
24/01/24 07:31:58 INFO DAGScheduler: Parents of final stage: List()
24/01/24 07:31:58 INFO DAGScheduler: Missing parents: List()
24/01/24 07:31:58 INFO DAGScheduler: Submitting ShuffleMapStage 0 (MapPartitionsRDD[1248] at count at SparkDemo.java:443), which has no missing parents
24/01/24 07:31:59 INFO MemoryStore: Block broadcast_1 stored as values in memory (estimated size 416.1 KiB, free 413.3 MiB)
24/01/24 07:31:59 INFO MemoryStore: Block broadcast_1_piece0 stored as bytes in memory (estimated size 64.4 KiB, free 413.2 MiB)
24/01/24 07:31:59 INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on spark1706081470242-7219f68d3a6157ef-driver-svc.spark-app.svc:7079 (size: 64.4 KiB, free: 413.8 MiB)
24/01/24 07:31:59 INFO SparkContext: Created broadcast 1 from broadcast at DAGScheduler.scala:1513
24/01/24 07:31:59 INFO DAGScheduler: Submitting 453 missing tasks from ShuffleMapStage 0 (MapPartitionsRDD[1248] at count at SparkDemo.java:443) (first 15 tasks are for partitions Vector(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14))
24/01/24 07:31:59 INFO TaskSchedulerImpl: Adding task set 0.0 with 453 tasks resource profile 0
24/01/24 07:31:59 INFO TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0) (10.42.2.227, executor 4, partition 0, PROCESS_LOCAL, 4708 bytes) taskResourceAssignments Map()
24/01/24 07:31:59 INFO TaskSetManager: Starting task 1.0 in stage 0.0 (TID 1) (10.42.6.99, executor 1, partition 1, PROCESS_LOCAL, 4710 bytes) taskResourceAssignments Map()
24/01/24 07:31:59 INFO TaskSetManager: Starting task 2.0 in stage 0.0 (TID 2) (10.42.4.3, executor 3, partition 2, PROCESS_LOCAL, 4712 bytes) taskResourceAssignments Map()
24/01/24 07:31:59 INFO TaskSetManager: Starting task 3.0 in stage 0.0 (TID 3) (10.42.7.131, executor 5, partition 3, PROCESS_LOCAL, 4712 bytes) taskResourceAssignments Map()
24/01/24 07:31:59 INFO TaskSetManager: Starting task 4.0 in stage 0.0 (TID 4) (10.42.3.163, executor 2, partition 4, PROCESS_LOCAL, 4710 bytes) taskResourceAssignments Map()
24/01/24 07:31:59 INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on 10.42.7.131:45533 (size: 64.4 KiB, free: 1048.7 MiB)
24/01/24 07:31:59 INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on 10.42.3.163:40237 (size: 64.4 KiB, free: 1048.7 MiB)
24/01/24 07:31:59 INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on 10.42.2.227:43975 (size: 64.4 KiB, free: 1048.7 MiB)
24/01/24 07:31:59 INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on 10.42.4.3:38355 (size: 64.4 KiB, free: 1048.7 MiB)
24/01/24 07:31:59 INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on 10.42.6.99:36793 (size: 64.4 KiB, free: 1048.7 MiB)
24/01/24 07:31:59 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on 10.42.4.3:38355 (size: 36.3 KiB, free: 1048.7 MiB)
24/01/24 07:32:00 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on 10.42.7.131:45533 (size: 36.3 KiB, free: 1048.7 MiB)
24/01/24 07:32:00 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on 10.42.2.227:43975 (size: 36.3 KiB, free: 1048.7 MiB)
24/01/24 07:32:00 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on 10.42.3.163:40237 (size: 36.3 KiB, free: 1048.7 MiB)
24/01/24 07:32:00 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on 10.42.6.99:36793 (size: 36.3 KiB, free: 1048.7 MiB)
24/01/24 07:32:02 INFO TaskSetManager: Starting task 5.0 in stage 0.0 (TID 5) (10.42.4.3, executor 3, partition 5, PROCESS_LOCAL, 4712 bytes) taskResourceAssignments Map()
24/01/24 07:32:02 INFO TaskSetManager: Finished task 2.0 in stage 0.0 (TID 2) in 2932 ms on 10.42.4.3 (executor 3) (1/453)
24/01/24 07:32:02 INFO TaskSetManager: Starting task 6.0 in stage 0.0 (TID 6) (10.42.2.227, executor 4, partition 6, PROCESS_LOCAL, 4712 bytes) taskResourceAssignments Map()
24/01/24 07:32:02 INFO TaskSetManager: Finished task 0.0 in stage 0.0 (TID 0) in 2991 ms on 10.42.2.227 (executor 4) (2/453)
24/01/24 07:32:02 INFO TaskSetManager: Starting task 7.0 in stage 0.0 (TID 7) (10.42.3.163, executor 2, partition 7, PROCESS_LOCAL, 4710 bytes) taskResourceAssignments Map()
24/01/24 07:32:02 INFO TaskSetManager: Finished task 4.0 in stage 0.0 (TID 4) in 3004 ms on 10.42.3.163 (executor 2) (3/453)
24/01/24 07:32:02 INFO TaskSetManager: Starting task 8.0 in stage 0.0 (TID 8) (10.42.4.3, executor 3, partition 8, PROCESS_LOCAL, 4710 bytes) taskResourceAssignments Map()
24/01/24 07:32:02 INFO TaskSetManager: Finished task 5.0 in stage 0.0 (TID 5) in 130 ms on 10.42.4.3 (executor 3) (4/453)
24/01/24 07:32:02 INFO TaskSetManager: Starting task 9.0 in stage 0.0 (TID 9) (10.42.7.131, executor 5, partition 9, PROCESS_LOCAL, 4708 bytes) taskResourceAssignments Map()
24/01/24 07:32:02 INFO TaskSetManager: Finished task 3.0 in stage 0.0 (TID 3) in 3107 ms on 10.42.7.131 (executor 5) (5/453)
24/01/24 07:32:02 INFO TaskSetManager: Starting task 10.0 in stage 0.0 (TID 10) (10.42.3.163, executor 2, partition 10, PROCESS_LOCAL, 4709 bytes) taskResourceAssignments Map()
24/01/24 07:32:02 INFO TaskSetManager: Finished task 7.0 in stage 0.0 (TID 7) in 134 ms on 10.42.3.163 (executor 2) (6/453)
24/01/24 07:32:02 INFO TaskSetManager: Starting task 11.0 in stage 0.0 (TID 11) (10.42.4.3, executor 3, partition 11, PROCESS_LOCAL, 4711 bytes) taskResourceAssignments Map()
24/01/24 07:32:02 INFO TaskSetManager: Finished task 8.0 in stage 0.0 (TID 8) in 100 ms on 10.42.4.3 (executor 3) (7/453)
24/01/24 07:32:02 INFO TaskSetManager: Starting task 12.0 in stage 0.0 (TID 12) (10.42.7.131, executor 5, partition 12, PROCESS_LOCAL, 4708 bytes) taskResourceAssignments Map()
24/01/24 07:32:02 INFO TaskSetManager: Finished task 9.0 in stage 0.0 (TID 9) in 97 ms on 10.42.7.131 (executor 5) (8/453)
24/01/24 07:32:02 INFO TaskSetManager: Starting task 13.0 in stage 0.0 (TID 13) (10.42.3.163, executor 2, partition 13, PROCESS_LOCAL, 4708 bytes) taskResourceAssignments Map()
24/01/24 07:32:02 INFO TaskSetManager: Finished task 10.0 in stage 0.0 (TID 10) in 87 ms on 10.42.3.163 (executor 2) (9/453)
24/01/24 07:32:02 INFO TaskSetManager: Starting task 14.0 in stage 0.0 (TID 14) (10.42.4.3, executor 3, partition 14, PROCESS_LOCAL, 4713 bytes) taskResourceAssignments Map()...........24/01/24 07:32:18 INFO DAGScheduler: Final stage: ResultStage 3 (show at SparkDemo.java:445)
24/01/24 07:32:18 INFO DAGScheduler: Parents of final stage: List()
24/01/24 07:32:18 INFO DAGScheduler: Missing parents: List()
24/01/24 07:32:18 INFO DAGScheduler: Submitting ResultStage 3 (MapPartitionsRDD[2499] at show at SparkDemo.java:445), which has no missing parents
24/01/24 07:32:18 INFO MemoryStore: Block broadcast_4 stored as values in memory (estimated size 565.9 KiB, free 413.1 MiB)
24/01/24 07:32:18 INFO MemoryStore: Block broadcast_4_piece0 stored as bytes in memory (estimated size 79.7 KiB, free 413.0 MiB)
24/01/24 07:32:18 INFO BlockManagerInfo: Added broadcast_4_piece0 in memory on spark1706081470242-7219f68d3a6157ef-driver-svc.spark-app.svc:7079 (size: 79.7 KiB, free: 413.8 MiB)
24/01/24 07:32:18 INFO SparkContext: Created broadcast 4 from broadcast at DAGScheduler.scala:1513
24/01/24 07:32:18 INFO DAGScheduler: Submitting 1 missing tasks from ResultStage 3 (MapPartitionsRDD[2499] at show at SparkDemo.java:445) (first 15 tasks are for partitions Vector(0))
24/01/24 07:32:18 INFO TaskSchedulerImpl: Adding task set 3.0 with 1 tasks resource profile 0
24/01/24 07:32:18 INFO TaskSetManager: Starting task 0.0 in stage 3.0 (TID 454) (10.42.3.163, executor 2, partition 0, PROCESS_LOCAL, 4722 bytes) taskResourceAssignments Map()
24/01/24 07:32:18 INFO BlockManagerInfo: Added broadcast_4_piece0 in memory on 10.42.3.163:40237 (size: 79.7 KiB, free: 1048.7 MiB)
24/01/24 07:32:19 INFO BlockManagerInfo: Added broadcast_3_piece0 in memory on 10.42.3.163:40237 (size: 36.4 KiB, free: 1048.7 MiB)
24/01/24 07:32:19 INFO TaskSetManager: Finished task 0.0 in stage 3.0 (TID 454) in 323 ms on 10.42.3.163 (executor 2) (1/1)
24/01/24 07:32:19 INFO TaskSchedulerImpl: Removed TaskSet 3.0, whose tasks have all completed, from pool 
24/01/24 07:32:19 INFO DAGScheduler: ResultStage 3 (show at SparkDemo.java:445) finished in 0.346 s
24/01/24 07:32:19 INFO DAGScheduler: Job 2 is finished. Cancelling potential speculative or zombie tasks for this job
24/01/24 07:32:19 INFO TaskSchedulerImpl: Killing all running tasks in stage 3: Stage finished
24/01/24 07:32:19 INFO DAGScheduler: Job 2 finished: show at SparkDemo.java:445, took 0.362538 s
24/01/24 07:32:19 INFO CodeGenerator: Code generated in 23.175128 ms
+----------+---------+--------------+------+----------+---+--------------+---------+---------+-------------+-------------+----------+----------+------+---------+
|serial_num|trans_seq|    state_name|spc_id|occurrence|seq|test_seq_event|stat_name|load_time|load_peak_cur|load_peak_vel|vcm_offset|event_date|family|operation|
+----------+---------+--------------+------+----------+---+--------------+---------+---------+-------------+-------------+----------+----------+------+---------+
|  WP01C6DB|       53|LUL_TEST25_SCS| 30101|         1| 17|             1|      MAX|    156.5|       -511.4|         -3.2|   65535.0|  20231202|   3AK|     CRT2|
|  WP01C6DB|       53|LUL_TEST25_SCS| 30101|         1| 17|             1|      MIN|    156.5|       -511.4|         -3.2|   65535.0|  20231202|   3AK|     CRT2|
|  WP01C6DB|       53|LUL_TEST25_SCS| 30101|         1| 17|             1|      AVG|    156.5|       -511.4|         -3.2|   65535.0|  20231202|   3AK|     CRT2|
|  WP01C6DB|       53|LUL_TEST25_SCS| 30101|         1| 17|             1|    STDEV|      0.0|          0.0|          0.0|   65535.0|  20231202|   3AK|     CRT2|
|  WP01C6DB|       53|LUL_TEST25_SCS| 30101|         1| 17|             1|   ERRCNT|      0.0|          0.0|          0.0|       0.0|  20231202|   3AK|     CRT2|
|  WP0187VM|       51|LUL_TEST25_SCS| 30101|         1| 17|             1|      MAX|    152.2|       -512.9|         -3.1|   65535.0|  20231202|   3AK|     CRT2|
|  WP0187VM|       51|LUL_TEST25_SCS| 30101|         1| 17|             1|      MIN|    152.2|       -512.9|         -3.1|   65535.0|  20231202|   3AK|     CRT2|
|  WP0187VM|       51|LUL_TEST25_SCS| 30101|         1| 17|             1|      AVG|    152.2|       -512.9|         -3.1|   65535.0|  20231202|   3AK|     CRT2|
|  WP0187VM|       51|LUL_TEST25_SCS| 30101|         1| 17|             1|    STDEV|      0.0|          0.0|          0.0|   65535.0|  20231202|   3AK|     CRT2|
|  WP0187VM|       51|LUL_TEST25_SCS| 30101|         1| 17|             1|   ERRCNT|      0.0|          0.0|          0.0|       0.0|  20231202|   3AK|     CRT2|
+----------+---------+--------------+------+----------+---+--------------+---------+---------+-------------+-------------+----------+----------+------+---------+24/01/24 07:32:19 INFO DataSourceStrategy: Pruning directories with: 
24/01/24 07:32:19 INFO CodeGenerator: Code generated in 71.006193 ms
24/01/24 07:32:19 INFO MemoryStore: Block broadcast_5 stored as values in memory (estimated size 216.0 KiB, free 412.8 MiB)
24/01/24 07:32:19 INFO MemoryStore: Block broadcast_5_piece0 stored as bytes in memory (estimated size 36.4 KiB, free 412.8 MiB)
24/01/24 07:32:19 INFO BlockManagerInfo: Added broadcast_5_piece0 in memory on spark1706081470242-7219f68d3a6157ef-driver-svc.spark-app.svc:7079 (size: 36.4 KiB, free: 413.8 MiB)
24/01/24 07:32:19 INFO SparkContext: Created broadcast 5 from 
24/01/24 07:32:22 INFO BlockManagerInfo: Removed broadcast_4_piece0 on spark1706081470242-7219f68d3a6157ef-driver-svc.spark-app.svc:7079 in memory (size: 79.7 KiB, free: 413.9 MiB)
24/01/24 07:32:22 INFO BlockManagerInfo: Removed broadcast_4_piece0 on 10.42.3.163:40237 in memory (size: 79.7 KiB, free: 1048.8 MiB).................24/01/24 07:32:32 INFO DAGScheduler: looking for newly runnable stages
24/01/24 07:32:32 INFO DAGScheduler: running: Set()
24/01/24 07:32:32 INFO DAGScheduler: waiting: Set()
24/01/24 07:32:32 INFO DAGScheduler: failed: Set()
24/01/24 07:32:32 INFO CodeGenerator: Code generated in 17.641789 ms
24/01/24 07:32:32 INFO SparkContext: Starting job: collect at SparkDemo.java:448
24/01/24 07:32:32 INFO DAGScheduler: Got job 4 (collect at SparkDemo.java:448) with 1 output partitions
24/01/24 07:32:32 INFO DAGScheduler: Final stage: ResultStage 6 (collect at SparkDemo.java:448)
24/01/24 07:32:32 INFO DAGScheduler: Parents of final stage: List(ShuffleMapStage 5)
24/01/24 07:32:32 INFO DAGScheduler: Missing parents: List()
24/01/24 07:32:32 INFO DAGScheduler: Submitting ResultStage 6 (MapPartitionsRDD[3753] at javaRDD at SparkDemo.java:448), which has no missing parents
24/01/24 07:32:32 INFO MemoryStore: Block broadcast_7 stored as values in memory (estimated size 40.2 KiB, free 412.8 MiB)
24/01/24 07:32:32 INFO MemoryStore: Block broadcast_7_piece0 stored as bytes in memory (estimated size 17.0 KiB, free 412.7 MiB)
24/01/24 07:32:32 INFO BlockManagerInfo: Added broadcast_7_piece0 in memory on spark1706081470242-7219f68d3a6157ef-driver-svc.spark-app.svc:7079 (size: 17.0 KiB, free: 413.8 MiB)
24/01/24 07:32:32 INFO SparkContext: Created broadcast 7 from broadcast at DAGScheduler.scala:1513
24/01/24 07:32:32 INFO DAGScheduler: Submitting 1 missing tasks from ResultStage 6 (MapPartitionsRDD[3753] at javaRDD at SparkDemo.java:448) (first 15 tasks are for partitions Vector(0))
24/01/24 07:32:32 INFO TaskSchedulerImpl: Adding task set 6.0 with 1 tasks resource profile 0
24/01/24 07:32:32 INFO TaskSetManager: Starting task 0.0 in stage 6.0 (TID 908) (10.42.7.131, executor 5, partition 0, NODE_LOCAL, 4472 bytes) taskResourceAssignments Map()
24/01/24 07:32:32 INFO BlockManagerInfo: Added broadcast_7_piece0 in memory on 10.42.7.131:45533 (size: 17.0 KiB, free: 1048.7 MiB)
24/01/24 07:32:32 INFO MapOutputTrackerMasterEndpoint: Asked to send map output locations for shuffle 1 to 10.42.7.131:56864
24/01/24 07:32:33 INFO TaskSetManager: Finished task 0.0 in stage 6.0 (TID 908) in 1175 ms on 10.42.7.131 (executor 5) (1/1)
24/01/24 07:32:33 INFO TaskSchedulerImpl: Removed TaskSet 6.0, whose tasks have all completed, from pool 
24/01/24 07:32:33 INFO DAGScheduler: ResultStage 6 (collect at SparkDemo.java:448) finished in 1.183 s
24/01/24 07:32:33 INFO DAGScheduler: Job 4 is finished. Cancelling potential speculative or zombie tasks for this job
24/01/24 07:32:33 INFO TaskSchedulerImpl: Killing all running tasks in stage 6: Stage finished
24/01/24 07:32:33 INFO DAGScheduler: Job 4 finished: collect at SparkDemo.java:448, took 1.191350 s
rdd collect cost:14796
jaList list:10
serial_num is :32
serial_num is :32
serial_num is :32
serial_num is :32
serial_num is :32
serial_num is :32
serial_num is :32
serial_num is :32
serial_num is :32
serial_num is :32
SparkDemo foreach cost:1
=========================5
24/01/24 07:32:34 INFO SparkUI: Stopped Spark web UI at http://spark1706081470242-7219f68d3a6157ef-driver-svc.spark-app.svc:4040
24/01/24 07:32:34 INFO KubernetesClusterSchedulerBackend: Shutting down all executors
24/01/24 07:32:34 INFO KubernetesClusterSchedulerBackend$KubernetesDriverEndpoint: Asking each executor to shut down
24/01/24 07:32:34 WARN ExecutorPodsWatchSnapshotSource: Kubernetes client has been closed.
24/01/24 07:32:34 INFO MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped!
24/01/24 07:32:34 INFO MemoryStore: MemoryStore cleared
24/01/24 07:32:34 INFO BlockManager: BlockManager stopped
24/01/24 07:32:34 INFO BlockManagerMaster: BlockManagerMaster stopped
24/01/24 07:32:34 INFO OutputCommitCoordinator$OutputCommitCoordinatorEndpoint: OutputCommitCoordinator stopped!
24/01/24 07:32:34 INFO SparkContext: Successfully stopped SparkContext

本文来自互联网用户投稿，该文观点仅代表作者本人，不代表本站立场。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如若转载，请注明出处：http://www.mzph.cn/news/645281.shtml

如若内容造成侵权/违法违规/事实不符，请联系多彩编程网进行投诉反馈email:809451989@qq.com，一经查实，立即删除！

相关文章

linux安装docker--更具官网教程

linux安装docker--更具官网教程

1.访问https://docs.docker.com/ 2.进入download 3输入cento 或者直接访问地址Install Docker Engine on CentOS | Docker Docs 4一步一步根据官网命令走 2安装 3 4 方式一&#xff1a; service docker start&#xff08;开启&#xff09; service docker status&#xff08…
阅读更多...
使用PowerShell命令行，批量修改文件编码

使用PowerShell命令行，批量修改文件编码

目录 ■前言 ■PowerShell命令 ■效果 ■前言 今天统计修改代码量&#xff0c;使用工具时&#xff0c;发现有些代码无法统计。 原因时UTF-8中有某些特殊字符&#xff0c;工具不能识别。 但是&#xff0c;如果把代码转换为SJIS格式&#xff0c;就能正常统计了。 因此&…
阅读更多...
听筒及麦克风电路时序分析

听筒及麦克风电路时序分析

打电话的时候。当没有免提的时候&#xff0c;用的是mic1&#xff0c;麦克风1居然是在J7尾插座子上&#xff0c;所以要把手机的下面贴近嘴巴。mic1的信号给到音频编解码u21&#xff0c;u21通过i2s线给cpu, 然后给基带cpu,然后通过射频发射出去。当要听声音的时候&#xff0c;射频…
阅读更多...
【数学建模】插值与拟合

【数学建模】插值与拟合

文章目录 插值插值方法用Python解决插值问题 拟合最小二乘拟合数据拟合的Python实现 适用情况 处理由试验、测量得到的大量数据或一些过于复杂而不便于计算的函数表达式时&#xff0c;构造一个简单函数作为要考察数据或复杂函数的近似 定义 给定一组数据&#xff0c;需要确定满…
阅读更多...
【软件测试】学习笔记-性能测试场景的分类

【软件测试】学习笔记-性能测试场景的分类

性能测试场景的重要程度类似于业务测试的 case&#xff0c;case 是你进行业务测试的指引&#xff0c;case 是否完善也直接决定了测试的覆盖率。同理&#xff0c;场景是传递执行性能测试的步骤和目的&#xff0c;关于这两点是你一定要清楚的。 首先认识下最重要的三个性能场景&…
阅读更多...
Java项目：12 Springboot的垃圾回收管理系统

Java项目：12 Springboot的垃圾回收管理系统

作者主页&#xff1a;舒克日记 简介&#xff1a;Java领域优质创作者、Java项目、学习资料、技术互助 文中获取源码 1.介绍 垃圾分类查询管理系统&#xff0c;对不懂的垃圾进行查询进行分类并可以预约上门回收垃圾。 让用户自己分类垃圾&#xff0c; 按国家标准自己分类&#x…
阅读更多...
解读IP风险画像标签：深度洞察网络安全

解读IP风险画像标签：深度洞察网络安全

在当今数字化的世界中&#xff0c;网络安全成为企业和个人关注的焦点。IP风险画像标签作为网络安全的利器&#xff0c;扮演着深度洞察网络风险的角色。本文将深入解读IP风险画像标签&#xff0c;揭示其在网络安全领域的重要性和功能。 1. IP风险画像标签是什么&#xff1f; I…
阅读更多...
Jmeter-解析返回参数-解析并操作json

Jmeter-解析返回参数-解析并操作json

工作中经常会遇到JSON字符串&#xff0c;接口的入参和返回参数也多数是JSON格式&#xff0c;自动化项目中常需要写脚本处理返回结果&#xff0c;本文总结java或jmeter的beanshell脚本中对于json的常用操作 json字符串的格式 简单的JSON字符串&#xff1a;{“key”:“value”,“…
阅读更多...
【服务器数据恢复】EqualLogic存储磁盘坏道导致存储不可用的数据恢复案例

【服务器数据恢复】EqualLogic存储磁盘坏道导致存储不可用的数据恢复案例

服务器数据恢复环境&故障&#xff1a; 某公司IT部门一台某品牌EqualLogic PS6100系列存储在运行过程中突然崩溃。 服务器管理员对故障服务器存储进行初步检查&#xff0c;经过检测发现导致该服务器存储无法正常工作的原因是该存储中raid5磁盘阵列内有2块硬盘出现故障离线&a…
阅读更多...
数字图像处理（实践篇）二十七 Python-OpenCV 滑动条的使用

数字图像处理（实践篇）二十七 Python-OpenCV 滑动条的使用

目录 1 涉及的函数 2 实践 1 涉及的函数 ⒈ setWindowProperty()用于设置GUI应用程序的属性 cv2.setWindowProperty(windowsName, prop_id, prop_value) 参数： ①
阅读更多...
UI 自动化测试框架：PO 模式+数据驱动

UI 自动化测试框架：PO 模式+数据驱动

1. PO 设计模式简介 什么是 PO 模式&#xff1f; PO&#xff08;PageObject&#xff09;设计模式将某个页面的所有元素对象定位和对元素对象的操作封装成一个 Page 类&#xff0c;并以页面为单位来写测试用例&#xff0c;实现页面对象和测试用例的分离。 PO 模式的设计思想与…
阅读更多...
Nodejs--Linux环境安装及配置

Nodejs--Linux环境安装及配置

一、下载安装包 &#xff08;1&#xff09;通过命令uname -a 查看Linux系统版本。 &#xff08;2&#xff09;打开官网下载地址&#xff1a; https://www.nodejs.com.cn/ 选择对应的版本下载安装包。 &#xff08;3&#xff09;将下载好的安装包传到Linux对应目录进行解压。…
阅读更多...
想要透明拼接屏展现更加效果，视频源是技术活，尤其作为直播背景

想要透明拼接屏展现更加效果，视频源是技术活，尤其作为直播背景

随着科技的飞速发展&#xff0c;视频制作和显示技术也在不断进步。透明拼接屏视频作为一种新型的视频形式&#xff0c;在许多场合都得到了广泛的应用。尼伽小编将深入探讨透明拼接屏视频的制作过程、要求、清晰度&#xff0c;以及目前常作为直播背景的优势。 一、透明拼接屏视频…
阅读更多...
力扣hot100 两两交换链表中的节点 双指针

力扣hot100 两两交换链表中的节点 双指针

Problem: 24. 两两交换链表中的节点 复杂度 时间复杂度: O ( n ) O(n) O(n) 空间复杂度: O ( 1 ) O(1) O(1) Code /*** Definition for singly-linked list.* public class ListNode {* int val;* ListNode next;* ListNode() {}* ListNode(int val) { …
阅读更多...
国民技术N32G430C8开发笔记二-UART驱动开发

国民技术N32G430C8开发笔记二-UART驱动开发

参考demo E:\tfs\data\smartsafe\N32G430\Nations.N32G430_Library.1.0.0\projects\n32g430_EVAL\examples\USART\Interrupt开发uart1和uart3驱动。Uart1用于通信&#xff0c;uart3用于调试。 参考用户手册5.2.5复用功能。Uart1_tx引脚使用PA9&#xff0c;重映射使用AF5&#x…
阅读更多...
SpringMVC第二天

SpringMVC第二天

今日内容 能够掌握SSM整合的流程 能够编写SSM整合功能模块类 能够使用Result统一表现层响应结果 能够编写异常处理器进行项目异常 能够完成SSM整合前端页面发送请求实现增删改查操作 能够编写拦截器并配置拦截器 一、SSM整合【重点】 1 SSM整合配置 问题导入 请描述“SSM整…
阅读更多...
加速应用开发：低代码云SaaS和源码交付模式如何选

加速应用开发：低代码云SaaS和源码交付模式如何选

随着数字化转型的加速&#xff0c;企业对于快速开发和交付高质量应用的需求也越来越迫切。为了满足这一需求&#xff0c;开发者们开始探索采用低代码平台进行软件开发工作&#xff0c;以加速应用开发过程。 目前&#xff0c;市场上的低代码产品众多&#xff0c;但基本可分为简单…
阅读更多...
黑马Java——面向对象进阶（static继承）

黑马Java——面向对象进阶（static继承）

1.static静态变量 静态变量是随着类的加载而加载的&#xff0c;优先与对象出现的
阅读更多...
写一份简单的产品说明书：格式和排版建议

写一份简单的产品说明书：格式和排版建议

现在的市场竞争那么激烈&#xff0c;拥有一份简洁明了的产品说明书可以说是很重要的。产品说明书不仅向用户提供了对产品的详细了解&#xff0c;还能够树立品牌形象&#xff0c;提升用户体验。 | 一、写一份简单的产品说明书—一些建议 1.创意封面设计 一个吸引人的封面设计能…
阅读更多...
【数据结构】 循环队列的基本操作 （C语言版）

【数据结构】 循环队列的基本操作 （C语言版）

目录 一、顺序队列 1、顺序队列的定义&#xff1a; 2、顺序队列的优缺点&#xff1a; 二、循环队列 1、循环队列的定义&#xff1a; 2、循环队列的优缺点&#xff1a; 三、循环队列的基本操作算法&#xff08;C语言&#xff09; 1、宏定义 2、创建结构体 3、循环队…
阅读更多...
推荐文章
最新文章

Copyright @ 2022~2023