背景:
调研过OOZIE和AZKABA,这种都是只是使用spark-submit.sh来提交任务,任务提交上去之后获取不到ApplicationId,更无法跟踪spark application的任务状态,无法kill application,更无法获取application的日志信息。因此,为了实现一个spark的调度平台所以有了以下调研及测试结论。
调研目前流行的SPARK任务调度:Oozie和Azkaban。
但是这两个平台不能满足以下功能(这些功能是希望有的):
1) 无法满足即安全(使用shell提交任务,操作用户权限控制)又可以Spark状态监控(跟踪SPARK application的任务状态);
2) 无法满足监控集群运行状态;
3) 无法满足对每个任务设置监控策略。比如:任务假死状态判定。
一个合格的spark调度平台要具有的基本功能:可以submit,kill,监控,获取日志,跟踪历史记录。
本篇文章主要讲解如何使用YarnClient API实现,借助于YarnClient来实现监控任务,杀死任务,获取日志,使用org.apache.spark.deploy.yarn.Client提交spark任务并返回spark任务的applicationId。
备注:之前研究过使用SparkLauncher类进行调度,该方案也是一种不错的方案,如果读者你喜欢也可以尝试使用SparkLauncher,它一样可以提交后返回spark任务的applicationid(提交后无状态,需要等待applicaitonId不为空为止)。
环境配置:
1)由于我们是使用java 代码(需要发布到web项目中,而不是shell调用[不可以再shell中设置环境变量])去调用,因此我们需要centos系统环境变量中包含以下变量:
SPARK_KAFKA_VERSION
HADOOP_HOME
HADOOP_COMMON_HOME
SPARK_HOME
SPARK_CONF_DIR
HADOOP_CONF_DIR
YARN_CONF_DIR
SPARK_DIST_CLASSPATH
SPARK_EXTRA_LIB_PATH
LD_LIBRARY_PATH
如果你对spark-env.sh文件比较熟悉的话,你会发现上边这些变量来自于该文件,那么,我们嗯只需要把spark-env.sh引入到/ect/profile就可以。
spark-env.sh
1 bash-4.1$ more /home1/opt/cloudera/parcels/SPARK2-2.3.0.cloudera3-1.cdh5.13.3.p0.458809/lib/spark2/conf/spark-env.sh
2 #!/usr/bin/env bash
3 ##
4 # Generated by Cloudera Manager and should not be modified directly
5 ##
6
7 SELF="$(cd $(dirname $BASH_SOURCE) && pwd)"
8 if [ -z "$SPARK_CONF_DIR" ]; then
9 export SPARK_CONF_DIR="$SELF"
10 fi
11
12 export SPARK_HOME=/home1/opt/cloudera/parcels/SPARK2-2.3.0.cloudera3-1.cdh5.13.3.p0.458809/lib/spark2
13
14 SPARK_PYTHON_PATH=""
15 if [ -n "$SPARK_PYTHON_PATH" ]; then
16 export PYTHONPATH="$PYTHONPATH:$SPARK_PYTHON_PATH"
17 fi
18
19 export HADOOP_HOME=/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/lib/hadoop
20 export HADOOP_COMMON_HOME="$HADOOP_HOME"
21
22 if [ -n "$HADOOP_HOME" ]; then
23 LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${HADOOP_HOME}/lib/native
24 fi
25
26 SPARK_EXTRA_LIB_PATH="/home1/opt/cloudera/parcels/GPLEXTRAS-5.13.0-1.cdh5.13.0.p0.29/lib/hadoop/lib/native"
27 if [ -n "$SPARK_EXTRA_LIB_PATH" ]; then
28 LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$SPARK_EXTRA_LIB_PATH
29 fi
30
31 export LD_LIBRARY_PATH
32
33 HADOOP_CONF_DIR=${HADOOP_CONF_DIR:-$SPARK_CONF_DIR/yarn-conf}
34 export HADOOP_CONF_DIR
35
36 PYLIB="$SPARK_HOME/python/lib"
37 if [ -f "$PYLIB/pyspark.zip" ]; then
38 PYSPARK_ARCHIVES_PATH=
39 for lib in "$PYLIB"/*.zip; do
40 if [ -n "$PYSPARK_ARCHIVES_PATH" ]; then
41 PYSPARK_ARCHIVES_PATH="$PYSPARK_ARCHIVES_PATH,local:$lib"
42 else
43 PYSPARK_ARCHIVES_PATH="local:$lib"
44 fi
45 done
46 export PYSPARK_ARCHIVES_PATH
47 fi
48
49 # Spark uses `set -a` to export all variables created or modified in this
50 # script as env vars. We use a temporary variables to avoid env var name
51 # collisions.
52 # If PYSPARK_PYTHON is unset, set to CDH_PYTHON
53 TMP_PYSPARK_PYTHON=${PYSPARK_PYTHON:-''}
54 # If PYSPARK_DRIVER_PYTHON is unset, set to CDH_PYTHON
55 TMP_PYSPARK_DRIVER_PYTHON=${PYSPARK_DRIVER_PYTHON:-}
56
57 if [ -n "$TMP_PYSPARK_PYTHON" ] && [ -n "$TMP_PYSPARK_DRIVER_PYTHON" ]; then
58 export PYSPARK_PYTHON="$TMP_PYSPARK_PYTHON"
59 export PYSPARK_DRIVER_PYTHON="$TMP_PYSPARK_DRIVER_PYTHON"
60 fi
61
62 # Add the Kafka jars configured by the user to the classpath.
63 SPARK_DIST_CLASSPATH=
64 SPARK_KAFKA_VERSION=${SPARK_KAFKA_VERSION:-'0.10'}
65 case "$SPARK_KAFKA_VERSION" in
66 0.9)
67 SPARK_DIST_CLASSPATH="$SPARK_HOME/kafka-0.9/*"
68 ;;
69 0.10)
70 SPARK_DIST_CLASSPATH="$SPARK_HOME/kafka-0.10/*"
71 ;;
72 None)
73 ;;
74 *)
75 echo "Invalid Kafka version: $SPARK_KAFKA_VERSION"
76 exit 1
77 ;;
78 esac
79
80 export SPARK_DIST_CLASSPATH="$SPARK_DIST_CLASSPATH:$(paste -sd: "$SELF/classpath.txt")"
View Code
接下来在/ect/profile文件最后一样追加
source /home1/opt/cloudera/parcels/SPARK2-2.3.0.cloudera3-1.cdh5.13.3.p0.458809/lib/spark2/conf/spark-env.sh
,保存,然后source /etc/profile使其生效。
2)需要修改yarn上传资源文件存储位置,否则会出现错误找不到资源文件(文件之所以找不到,是因为那些资源文件spark_lib.zip,spark_conf.zip,*.jar被上传到本地的/curent_user[root、zhangsan、lisi]/.sparkStaging/{appId}/*.jar下,在其他executor|container上找不到),必须修改yarn资源文件上传到hdfs目录下:
第一步:提交任务代码中设置SparkConf变量:
sparkConf.set("spark.yarn.stagingDir", "hdfs://vm192.168.0.141.com.cn:8020/user/");
第二步:手动创建hdfs目录 /user/.sparkStaging,给分配权限:
bash-4.1$ sudo -uhdfs hadoop fs -mkdir /user/.sparkStaging
bash-4.1$ sudo -uhdfs hadoop fs -chown zhangsan:zhangsan /user/.sparkStaging
第三步:导入pom.xml依赖包
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<es.version>6.4.2</es.version>
<spark.version>2.3.0</spark.version>
<scala.version>2.11</scala.version>
</properties>
<dependencies>
<!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-yarn-client -->
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-yarn-client</artifactId>
<version>2.6.5</version>
</dependency>
<!--Spark -->
<!-- https://mvnrepository.com/artifact/org.apache.spark/spark-yarn -->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-yarn_${scala.version}</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_${scala.version}</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_${scala.version}</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_${scala.version}</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql-kafka-0-10_${scala.version}</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-kafka-0-10_${scala.version}</artifactId>
<version>${spark.version}</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.apache.spark/spark-launcher -->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-launcher_${scala.version}</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka_2.11</artifactId>
<version>0.10.0.1</version>
</dependency>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>2.11.6</version>
</dependency>
<dependency>
<groupId>com.twitter</groupId>
<artifactId>bijection-avro_${scala.version}</artifactId>
<version>0.9.5</version>
</dependency>
<dependency>
<groupId>com.databricks</groupId>
<artifactId>spark-avro_${scala.version}</artifactId>
<version>3.2.0</version>
<type>jar</type>
</dependency>
<dependency>
<groupId>org.elasticsearch</groupId>
<artifactId>elasticsearch-spark-20_${scala.version}</artifactId>
<version>${es.version}</version>
</dependency>
<dependency>
<groupId>org.elasticsearch.client</groupId>
<artifactId>transport</artifactId>
<version>${es.version}</version>
</dependency>
<!-- https://mvnrepository.com/artifact/com.alibaba/fastjson -->
<dependency>
<groupId>com.alibaba</groupId>
<artifactId>fastjson</artifactId>
<version>1.2.54</version>
</dependency>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>3.8.1</version>
<scope>test</scope>
</dependency>
</dependencies>
spark提交任务:
参数类YarnSubmitConditions:
1 import java.util.List;
2 import java.util.Map;
3
4 public class YarnSubmitConditions {
5 private List<String> otherArgs;
6 private String applicationJar;
7 private String mainClass;
8 private String appName;
9 private String[] additionalJars;
10 private String sparkYarnJars;
11 public String[] files;
12 public String yarnResourcemanagerAddress;
13 public String sparkFsDefaultFS;
14 private String driverMemory;
15 private String numExecutors;
16 private String executorMemory;
17 private String executorCores;
18 private String sparkHome;
19 private String deployMode;
20 private String master;
21 public Map<String, String> sparkProperties;
22
23 public List<String> getOtherArgs() {
24 return otherArgs;
25 }
26
27 public void setOtherArgs(List<String> otherArgs) {
28 this.otherArgs = otherArgs;
29 }
30
31 public String getApplicationJar() {
32 return applicationJar;
33 }
34
35 public void setApplicationJar(String applicationJar) {
36 this.applicationJar = applicationJar;
37 }
38
39 public String getMainClass() {
40 return mainClass;
41 }
42
43 public void setMainClass(String mainClass) {
44 this.mainClass = mainClass;
45 }
46
47 public String getAppName() {
48 return appName;
49 }
50
51 public void setAppName(String appName) {
52 this.appName = appName;
53 }
54
55 public String[] getAdditionalJars() {
56 return additionalJars;
57 }
58
59 public void setAdditionalJars(String[] additionalJars) {
60 this.additionalJars = additionalJars;
61 }
62
63 public String getSparkYarnJars() {
64 return sparkYarnJars;
65 }
66
67 public void setSparkYarnJars(String sparkYarnJars) {
68 this.sparkYarnJars = sparkYarnJars;
69 }
70
71 public String[] getFiles() {
72 return files;
73 }
74
75 public void setFiles(String[] files) {
76 this.files = files;
77 }
78
79 public String getYarnResourcemanagerAddress() {
80 return yarnResourcemanagerAddress;
81 }
82
83 public void setYarnResourcemanagerAddress(String yarnResourcemanagerAddress) {
84 this.yarnResourcemanagerAddress = yarnResourcemanagerAddress;
85 }
86
87 public Map<String, String> getSparkProperties() {
88 return sparkProperties;
89 }
90
91 public void setSparkProperties(Map<String, String> sparkProperties) {
92 this.sparkProperties = sparkProperties;
93 }
94
95 public String getSparkFsDefaultFS() {
96 return sparkFsDefaultFS;
97 }
98
99 public void setSparkFsDefaultFS(String sparkFsDefaultFS) {
100 this.sparkFsDefaultFS = sparkFsDefaultFS;
101 }
102
103 public String getDriverMemory() {
104 return driverMemory;
105 }
106
107 public void setDriverMemory(String driverMemory) {
108 this.driverMemory = driverMemory;
109 }
110
111 public String getNumExecutors() {
112 return numExecutors;
113 }
114
115 public void setNumExecutors(String numExecutors) {
116 this.numExecutors = numExecutors;
117 }
118
119 public String getExecutorMemory() {
120 return executorMemory;
121 }
122
123 public void setExecutorMemory(String executorMemory) {
124 this.executorMemory = executorMemory;
125 }
126
127 public String getExecutorCores() {
128 return executorCores;
129 }
130
131 public void setExecutorCores(String executorCores) {
132 this.executorCores = executorCores;
133 }
134
135 public String getSparkHome() {
136 return sparkHome;
137 }
138
139 public void setSparkHome(String sparkHome) {
140 this.sparkHome = sparkHome;
141 }
142
143 public String getDeployMode() {
144 return deployMode;
145 }
146
147 public void setDeployMode(String deployMode) {
148 this.deployMode = deployMode;
149 }
150
151 public String getMaster() {
152 return master;
153 }
154
155 public void setMaster(String master) {
156 this.master = master;
157 }
158 }
View Code
提交函数:
/**
* 提交任务到yarn集群
*
* @param conditions
* yarn集群,spark,hdfs具体信息,参数等
* @return appid
*/
public static String submitSpark(YarnSubmitConditions conditions) {
logger.info("初始化spark on yarn参数");
// 初始化yarn客户端
logger.info("初始化spark on yarn客户端");
List<String> args = Lists.newArrayList(//
"--jar", conditions.getApplicationJar(),//
"--class", conditions.getMainClass()//
);
if (conditions.getOtherArgs() != null && conditions.getOtherArgs().size() > 0) {
for (String s : conditions.getOtherArgs()) {
args.add("--arg");
args.add(org.apache.commons.lang.StringUtils.join(new String[] { s }, ","));
}
}
// identify that you will be using Spark as YARN mode
System.setProperty("SPARK_YARN_MODE", "true");
System.out.println("SPARK_YARN_MODE:" + System.getenv("SPARK_YARN_MODE"));
System.out.println("SPARK_CONF_DIR:" + System.getenv("SPARK_CONF_DIR"));
System.out.println("HADOOP_CONF_DIR:" + System.getenv("HADOOP_CONF_DIR"));
System.out.println("YARN_CONF_DIR:" + System.getenv("YARN_CONF_DIR"));
System.out.println("SPARK_KAFKA_VERSION:" + System.getenv("SPARK_KAFKA_VERSION"));
System.out.println("HADOOP_HOME:" + System.getenv("HADOOP_HOME"));
System.out.println("HADOOP_COMMON_HOME:" + System.getenv("HADOOP_COMMON_HOME"));
System.out.println("SPARK_HOME:" + System.getenv("SPARK_HOME"));
System.out.println("SPARK_DIST_CLASSPATH:" + System.getenv("SPARK_DIST_CLASSPATH"));
System.out.println("SPARK_EXTRA_LIB_PATH:" + System.getenv("SPARK_EXTRA_LIB_PATH"));
System.out.println("LD_LIBRARY_PATH:" + System.getenv("LD_LIBRARY_PATH"));
SparkConf sparkConf = new SparkConf();
sparkConf.setSparkHome(conditions.getSparkHome());
sparkConf.setMaster(conditions.getMaster());
sparkConf.set("spark.submit.deployMode", conditions.getDeployMode());
sparkConf.setAppName(conditions.getAppName());
// --driver-memory
sparkConf.set("spark.driver.memory", conditions.getDriverMemory());
// --executor-memory
sparkConf.set("spark.executor.memory", conditions.getExecutorMemory());
// --executor-cores
sparkConf.set("spark.executor.cores", conditions.getExecutorCores());
// --num-executors
sparkConf.set("spark.executor.instance", conditions.getNumExecutors());
// The folder '.sparkStaging' will be created auto.
// System.out.println("SPARK_YARN_STAGING_DIR:"+System.getenv("SPARK_YARN_STAGING_DIR"))
sparkConf.set("spark.yarn.stagingDir", "hdfs://vm192.168.0.141.com.cn:8020/user/");
// sparkConf.set("spark.jars",);
// sparkConf.set("spark.yarn.jars", conditions.getSparkYarnJars());
if (conditions.getAdditionalJars() != null && conditions.getAdditionalJars().length > 0) {
sparkConf.set("spark.repl.local.jars", org.apache.commons.lang.StringUtils.join(conditions.getAdditionalJars(), ","));
sparkConf.set("spark.yarn.dist.jars", org.apache.commons.lang.StringUtils.join(conditions.getAdditionalJars(), ","));
}
// "--files","hdfs://node1:8020/user/root/yarn-site.xml",
if (conditions.getFiles() != null && conditions.getFiles().length > 0) {
sparkConf.set("spark.files", org.apache.commons.lang.StringUtils.join(conditions.getFiles(), ","));
}
for (Map.Entry<String, String> e : conditions.getSparkProperties().entrySet()) {
sparkConf.set(e.getKey().toString(), e.getValue().toString());
}
// mapred-site.xml
// 指定使用yarn框架
sparkConf.set("mapreduce.framework.name", "yarn");
// 指定historyserver
sparkConf.set("mapreduce.jobhistory.address", "vm192.168.0.141.com.cn:10020");
// yarn-site.xml
// 添加这个参数,不然spark会一直请求0.0.0.0:8030,一直重试
sparkConf.set("yarn.resourcemanager.hostname", conditions.getYarnResourcemanagerAddress().split(":")[0]);
// 指定资源分配器
sparkConf.set("yarn.resourcemanager.scheduler.address", "vm192.168.0.141.com.cn:8030");
// 设置为true,不删除缓存的jar包,因为现在提交yarn任务是使用的代码配置,没有配置文件,删除缓存的jar包有问题,
sparkConf.set("spark.yarn.preserve.staging.files", "false");
// spark2.2
// 初始化 yarn的配置
// Configuration cf = new Configuration();
// String cross_platform = "false";
// String os = System.getProperty("os.name");
// if (os.contains("Windows")) {
// cross_platform = "true";
// }
// 配置使用跨平台提交任务
// cf.set("mapreduce.app-submission.cross-platform", cross_platform);
// 设置yarn资源,不然会使用localhost:8032
// cf.set("yarn.resourcemanager.address",
// conditions.getYarnResourcemanagerAddress());
// 设置namenode的地址,不然jar包会分发,非常恶心
// cf.set("fs.defaultFS", conditions.getSparkFsDefaultFS());
// spark2.2
// Client client = new Client(cArgs, cf, sparkConf);
// spark2.3
ClientArguments cArgs = new ClientArguments(args.toArray(new String[args.size()]));
org.apache.spark.deploy.yarn.Client client = new Client(cArgs, sparkConf);
logger.info("提交任务,任务名称:" + conditions.getAppName());
try {
ApplicationId appId = client.submitApplication();
return appId.toString();
} catch (Exception e) {
logger.error("提交spark任务失败", e);
return null;
} finally {
if (client != null) {
client.stop();
}
}
}
测试函数
private static final org.slf4j.Logger logger = org.slf4j.LoggerFactory.getLogger(TestSubmit.class);
public static void main(String[] args) {
YarnSubmitConditions conditions = new YarnSubmitConditions();
conditions.setAppName("test yarn submit app");
conditions.setMaster("yarn");
conditions.setSparkHome("/home1/opt/cloudera/parcels/SPARK2/lib/spark2/");
conditions.setDeployMode("cluster");
conditions.setDriverMemory("3g");
conditions.setExecutorMemory("3g");
conditions.setExecutorCores("1");
conditions.setNumExecutors("5");
// /etc/hadoop/conf.cloudera.yarn/core-site.xml
conditions.setYarnResourcemanagerAddress("vm192.168.0.141.com.cn:8032");
// /etc/hadoop/conf.cloudera.yarn/yarn-site.xml
conditions.setSparkFsDefaultFS("hdfs://vm192.168.0.141.com.cn:8020");
conditions.setFiles(new String[] { "/etc/hadoop/conf.cloudera.yarn/hdfs-site.xml",//
"/etc/hadoop/conf.cloudera.yarn/mapred-site.xml",//
"/etc/hadoop/conf.cloudera.yarn/yarn-site.xml",//
});
conditions.setApplicationJar("/home1/zhangsan/mrs-streaming-driver.jar");
conditions.setMainClass("com.boco.mrs.streaming.Main");
conditions.setOtherArgs(Arrays.asList("RSRP", "TestBroadcastDriver"));
List<String> sparkJars = getSparkJars("/home1/zhangsan/sparkjars/");
conditions.setAdditionalJars(sparkJars.toArray(new String[sparkJars.size()]));
Map<String, String> propertiesMap = null;
try {
propertiesMap = getSparkProperties("/home1/zhangsan/conf/spark-properties-mrs.conf");
} catch (IOException e) {
e.printStackTrace();
}
conditions.setSparkProperties(propertiesMap);
String appId = submitSpark(conditions);
System.out.println("application id is " + appId);
System.out.println("Complete ....");
}
/**
* 加载sparkjars下的jar文件
* */
private static List<String> getSparkJars(String dir) {
List<String> items = new ArrayList<String>();
File file = new File(dir);
for (File item : file.listFiles()) {
items.add(item.getPath());
}
return items;
}
/**
* 加载spark-properties.conf配置文件
* */
private static Map<String, String> getSparkProperties(String filePath) throws IOException {
Map<String, String> propertiesMap = new HashMap<String, String>();
BufferedReader reader = new BufferedReader(new FileReader(filePath));
String line = null;
while ((line = reader.readLine()) != null) {
if (line.trim().length() > 0 && !line.startsWith("#") && line.indexOf("=") != -1) {
String[] fields = line.split("=");
propertiesMap.put(fields[0], fields[1]);
}
}
reader.close();
return propertiesMap;
}
测试函数执行脚本:
bash-4.1$ more test.sh
#/bin/sh
#LANG=zh_CN.utf8
#export LANG
export SPARK_KAFKA_VERSION=0.10
export LANG=zh_CN.UTF-8
java -cp ./sparkjars/*:./mrs-streaming-driver.jar com.dx.mrs.streaming.batchmodule.TestSubmit
执行日志:
1 bash-4.1$ ./test.sh
2 log4j:WARN No appenders could be found for logger (com.dx.mrs.streaming.batchmodule.TestSubmit).
3 log4j:WARN Please initialize the log4j system properly.
4 log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.
5 SPARK_YARN_MODE:null
6 SPARK_CONF_DIR:/home1/opt/cloudera/parcels/SPARK2-2.3.0.cloudera3-1.cdh5.13.3.p0.458809/lib/spark2/conf
7 HADOOP_CONF_DIR:/home1/opt/cloudera/parcels/SPARK2-2.3.0.cloudera3-1.cdh5.13.3.p0.458809/lib/spark2/conf/yarn-conf
8 YARN_CONF_DIR:null
9 SPARK_KAFKA_VERSION:0.10
10 HADOOP_HOME:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/lib/hadoop
11 HADOOP_COMMON_HOME:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/lib/hadoop
12 SPARK_HOME:/home1/opt/cloudera/parcels/SPARK2-2.3.0.cloudera3-1.cdh5.13.3.p0.458809/lib/spark2
13 SPARK_DIST_CLASSPATH:/home1/opt/cloudera/parcels/SPARK2-2.3.0.cloudera3-1.cdh5.13.3.p0.458809/lib/spark2/kafka-0.10/*:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/activation-1.1.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/aopalliance-1.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/apacheds-i18n-2.0.0-M15.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/apacheds-kerberos-codec-2.0.0-M15.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/api-asn1-api-1.0.0-M20.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/api-util-1.0.0-M20.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/asm-3.2.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/avro-1.7.6-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/aws-java-sdk-bundle-1.11.134.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/azure-data-lake-store-sdk-2.2.3.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/commons-beanutils-1.9.2.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/commons-beanutils-core-1.8.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/commons-codec-1.4.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/commons-configuration-1.6.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/commons-daemon-1.0.13.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/commons-digester-1.8.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/commons-el-1.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/commons-math3-3.1.1.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/commons-net-3.1.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/curator-client-2.7.1.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/curator-framework-2.7.1.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/curator-recipes-2.7.1.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/guava-11.0.2.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/guice-3.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-annotations-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-ant-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-archive-logs-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-archives-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-auth-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-aws-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-azure-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-azure-datalake-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-common-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-datajoin-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-distcp-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-extras-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-gridmix-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-hdfs-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-hdfs-nfs-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-mapreduce-client-app-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-mapreduce-client-common-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-mapreduce-client-core-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-mapreduce-client-hs-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-mapreduce-client-hs-plugins-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-mapreduce-client-jobclient-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-mapreduce-client-nativetask-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-mapreduce-client-shuffle-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-mapreduce-examples-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-nfs-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-openstack-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-rumen-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-sls-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-streaming-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-yarn-api-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-yarn-applications-distributedshell-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-yarn-applications-unmanaged-am-launcher-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-yarn-client-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-yarn-common-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-yarn-registry-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-yarn-server-applicationhistoryservice-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-yarn-server-common-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-yarn-server-nodemanager-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-yarn-server-resourcemanager-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hadoop-yarn-server-web-proxy-2.6.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hamcrest-core-1.3.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/htrace-core4-4.0.1-incubating.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/httpclient-4.2.5.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/httpcore-4.2.5.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/hue-plugins-3.9.0-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jackson-annotations-2.2.3.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jackson-core-2.2.3.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jackson-core-asl-1.8.8.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jackson-databind-2.2.3.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jackson-mapper-asl-1.8.8.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jasper-compiler-5.5.23.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jasper-runtime-5.5.23.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/java-xmlbuilder-0.4.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/javax.inject-1.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jaxb-api-2.2.2.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jaxb-impl-2.2.3-1.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jets3t-0.9.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jettison-1.1.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jline-2.11.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jsch-0.1.42.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/jsr305-3.0.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/leveldbjni-all-1.8.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/log4j-1.2.17.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/metrics-core-3.0.2.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/microsoft-windowsazure-storage-sdk-0.6.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/mockito-all-1.8.5.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/netty-3.10.5.Final.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/okhttp-2.4.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/okio-1.4.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/paranamer-2.3.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/protobuf-java-2.5.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/slf4j-api-1.7.5.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/slf4j-log4j12-1.7.5.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/snappy-java-1.0.4.1.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/spark-1.6.0-cdh5.13.0-yarn-shuffle.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/stax-api-1.0-2.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/xercesImpl-2.9.1.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/xml-apis-1.3.04.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/xmlenc-0.52.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/jars/zookeeper-3.4.5-cdh5.13.0.jar:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/lib/hadoop/LICENSE.txt:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/lib/hadoop/NOTICE.txt:/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/lib/hadoop/lib/jsp-api-2.1.jar:/home1/opt/cloudera/parcels/GPLEXTRAS-5.13.0-1.cdh5.13.0.p0.29/lib/hadoop/lib/COPYING.hadoop-lzo:/home1/opt/cloudera/parcels/GPLEXTRAS-5.13.0-1.cdh5.13.0.p0.29/lib/hadoop/lib/hadoop-lzo-0.4.15-cdh5.13.0.jar
14 SPARK_EXTRA_LIB_PATH:null
15 LD_LIBRARY_PATH::/home1/opt/cloudera/parcels/CDH-5.13.0-1.cdh5.13.0.p0.29/lib/hadoop/lib/native:/home1/opt/cloudera/parcels/GPLEXTRAS-5.13.0-1.cdh5.13.0.p0.29/lib/hadoop/lib/native
16 Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
17 19/01/10 22:30:26 WARN SparkConf: The configuration key 'spark.yarn.executor.memoryOverhead' has been deprecated as of Spark 2.3 and may be removed in the future. Please use the new key 'spark.executor.memoryOverhead' instead.
18 19/01/10 22:30:27 INFO TestSubmit: 提交任务,任务名称:test yarn submit app
19 19/01/10 22:30:27 INFO RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
20 19/01/10 22:30:29 INFO Client: Requesting a new application from cluster with 6 NodeManagers
21 19/01/10 22:30:29 INFO Client: Verifying our application has not requested more than the maximum memory capability of the cluster (30282 MB per container)
22 19/01/10 22:30:29 INFO Client: Will allocate AM container, with 3456 MB memory including 384 MB overhead
23 19/01/10 22:30:29 INFO Client: Setting up container launch context for our AM
24 19/01/10 22:30:29 INFO Client: Setting up the launch environment for our AM container
25 19/01/10 22:30:29 INFO Client: Preparing resources for our AM container
26 19/01/10 22:30:34 WARN Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.
27 19/01/10 22:30:36 INFO Client: Uploading resource file:/tmp/spark-03699598-b859-4a74-a65f-bc63e9fae733/__spark_libs__4116956896087694051.zip -> hdfs://vm192.168.0.141.com.cn:8020/user/.sparkStaging/application_1543820999543_0236/__spark_libs__4116956896087694051.zip
28 19/01/10 22:30:43 INFO Client: Uploading resource file:/home1/zhangsan/mrs-streaming-driver.jar -> hdfs://vm192.168.0.141.com.cn:8020/user/.sparkStaging/application_1543820999543_0236/mrs-streaming-driver.jar
29 19/01/10 22:31:33 INFO Client: Uploading resource file:/home1/zhangsan/sparkjars/elasticsearch-cli-6.4.2.jar -> hdfs://vm192.168.0.141.com.cn:8020/user/.sparkStaging/application_1543820999543_0236/elasticsearch-cli-6.4.2.jar
30 19/01/10 22:31:33 INFO Client: Uploading resource file:/home1/zhangsan/sparkjars/elasticsearch-6.4.2.jar -> hdfs://vm192.168.0.141.com.cn:8020/user/.sparkStaging/application_1543820999543_0236/elasticsearch-6.4.2.jar
31 ......
32 19/01/10 22:31:33 INFO Client: Uploading resource file:/tmp/spark-03699598-b859-4a74-a65f-bc63e9fae733/__spark_conf__339930271770719398.zip -> hdfs://vm192.168.0.141.com.cn:8020/user/.sparkStaging/application_1543820999543_0236/__spark_conf__.zip
33 19/01/10 22:31:34 INFO SecurityManager: Changing view acls to: zhangsan
34 19/01/10 22:31:34 INFO SecurityManager: Changing modify acls to: zhangsan
35 19/01/10 22:31:34 INFO SecurityManager: Changing view acls groups to:
36 19/01/10 22:31:34 INFO SecurityManager: Changing modify acls groups to:
37 19/01/10 22:31:34 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(zhangsan); groups with view permissions: Set(); users with modify permissions: Set(zhangsan); groups with modify permissions: Set()
38 19/01/10 22:31:34 INFO Client: Submitting application application_1543820999543_0236 to ResourceManager
39 19/01/10 22:31:34 INFO YarnClientImpl: Submitted application application_1543820999543_0236
40 application id is application_1543820999543_0236
41 Complete ....
42 19/01/10 22:31:34 INFO ShutdownHookManager: Shutdown hook called
43 19/01/10 22:31:34 INFO ShutdownHookManager: Deleting directory /tmp/spark-03699598-b859-4a74-a65f-bc63e9fae733
44 bash-4.1$
View Code
目前调试通之后,测试通过yarn的cluster方式,client模式下任务提交到yarn上去无响应。
spark任务状态:
任务状态封装类
1 public class SparkTaskState{
2 private String appId;
3 private String state;
4 private float progress;
5 private String finalStatus;
6
7 public String getAppId() {
8 return appId;
9 }
10 public void setAppId(String appId) {
11 this.appId = appId;
12 }
13
14 public String getState() {
15 return state;
16 }
17 public void setState(String state) {
18 this.state = state;
19 }
20
21 public float getProgress() {
22 return progress;
23 }
24 public void setProgress(float progress) {
25 this.progress = progress;
26 }
27
28 public String getFinalStatus() {
29 return finalStatus;
30 }
31 public void setFinalStatus(String finalStatus) {
32 this.finalStatus = finalStatus;
33 }
34 }
View Code
/**
* 获取spark任务状态
*
* @param yarnResourcemanagerAddress
* yarn资源管理器地址, 例如:master:8032,查看yarn集群获取具体地址
* @param appIdStr
* 需要取消的任务id
*/
public static SparkTaskState getStatus(String yarnResourcemanagerAddress, String appIdStr) {
logger.info("获取任务状态启动,任务id:" + appIdStr);
// 初始化 yarn的配置
Configuration cf = new Configuration();
boolean cross_platform = false;
String os = System.getProperty("os.name");
if (os.contains("Windows")) {
cross_platform = true;
}
cf.setBoolean("mapreduce.app-submission.cross-platform", cross_platform);// 配置使用跨平台提交任务
// 设置yarn资源,不然会使用localhost:8032
cf.set("yarn.resourcemanager.address", yarnResourcemanagerAddress);
logger.info("获取任务状态,任务id:" + appIdStr);
SparkTaskState taskState = new SparkTaskState();
// 设置任务id
taskState.setAppId(appIdStr);
YarnClient yarnClient = YarnClient.createYarnClient();
// 初始化yarn的客户端
yarnClient.init(cf);
// yarn客户端启动
yarnClient.start();
ApplicationReport report = null;
try {
report = yarnClient.getApplicationReport(getAppId(appIdStr));
} catch (Exception e) {
logger.error("获取spark任务状态失败");
}
if (report != null) {
YarnApplicationState state = report.getYarnApplicationState();
taskState.setState(state.name());
// 任务执行进度
float progress = report.getProgress();
taskState.setProgress(progress);
// 最终状态
FinalApplicationStatus status = report.getFinalApplicationStatus();
taskState.setFinalStatus(status.name());
} else {
taskState.setState("failed");
taskState.setProgress(0.0f);
taskState.setFinalStatus("failed");
}
// 关闭yarn客户端
yarnClient.stop();
logger.info("获取任务状态结束,任务状态:" + JSON.toJSONString(taskState));
return taskState;
}
private static ApplicationId getAppId(String appIdStr) {
return ConverterUtils.toApplicationId(appIdStr);
}
spark日志跟踪:
请参考《》
spark关闭任务:
/**
* 停止spark任务
*
* @param yarnResourcemanagerAddress
* yarn资源管理器地址, 例如:master:8032,查看yarn集群获取具体地址
* @param appIdStr
* 需要取消的任务id
*/
public static void killJob(String yarnResourcemanagerAddress, String appIdStr) {
logger.info("取消spark任务,任务id:" + appIdStr);
// 初始化 yarn的配置
Configuration cf = new Configuration();
boolean cross_platform = false;
String os = System.getProperty("os.name");
if (os.contains("Windows")) {
cross_platform = true;
}
// 配置使用跨平台提交任务
cf.setBoolean("mapreduce.app-submission.cross-platform", cross_platform);
// 设置yarn资源,不然会使用localhost:8032
cf.set("yarn.resourcemanager.address", yarnResourcemanagerAddress);
// 创建yarn的客户端,此类中有杀死任务的方法
YarnClient yarnClient = YarnClient.createYarnClient();
// 初始化yarn的客户端
yarnClient.init(cf);
// yarn客户端启动
yarnClient.start();
try {
// 根据应用id,杀死应用
yarnClient.killApplication(getAppId(appIdStr));
} catch (Exception e) {
logger.error("取消spark任务失败", e);
}
// 关闭yarn客户端
yarnClient.stop();
}