利用idea对spark程序进行远程提交和调试 - 好文

利用idea对spark程序进行远程提交和调试

本文以WordCount程序来实现idea对spark程序进行远程提交和调试
环境
- 利用虚拟机搭建拥有3台主机的spark集群
spark1：192.168.6.137
spark2：192.168.6.138
spark3：192.168.6.139
- idea-IU-2016.3.7

前提是集群和调试的主机在同一个网段内。

一、利用idea对spark程序进行远程提交

WordCount scala程序
/** * Created by cuiyufei on 2018/2/13. */ import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._ import org.apache.spark.SparkConf object
WordCount { private val master = "spark://spark1:7077" private val remote_file =
"hdfs://spark1:9000/user/spark/data/spark.txt" def main(args: Array[String]) {
val conf = new SparkConf() .setAppName("WordCount") .setMaster(master) .set(
"spark.executor.memory", "512m") .setJars(List(
"D:\\JetBrains\\workspace\\WordCount\\out\\artifacts\\WordCount_jar\\WordCount.jar"
))val sc = new SparkContext(conf) val textFile = sc.textFile(remote_file) val
wordCount = textFile.flatMap(line => line.split(" ")).map(word => (word, 1
)).reduceByKey((a, b) => a + b) wordCount.foreach(println) } }
pom.xml文件
<?xml version="1.0" encoding="UTF-8"?> <project xmlns=
"http://maven.apache.org/POM/4.0.0" xmlns:xsi=
"http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation=
"http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion> <groupId>WODAS</groupId> <artifactId>
WordCount</artifactId> <version>1.0-SNAPSHOT</version> <properties> <
spark.version>2.1.0</spark.version> <scala.version>2.11</scala.version> </
properties> <repositories> <repository> <id>nexus-aliyun</id> <name>Nexus aliyun
</name> <url>http://maven.aliyun.com/nexus/content/groups/public</url> </
repository> </repositories> <dependencies> <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-hive_${scala.version}</
artifactId> <version>${spark.version}</version> </dependency> <dependency> <
groupId>org.apache.spark</groupId> <artifactId>spark-mllib_${scala.version}</
artifactId> <version>${spark.version}</version> </dependency> </dependencies> <
build> <plugins> <plugin> <groupId>org.scala-tools</groupId> <artifactId>
maven-scala-plugin</artifactId> <version>2.15.2</version> <executions> <
execution> <goals> <goal>compile</goal> <goal>testCompile</goal> </goals> </
execution> </executions> </plugin> <plugin> <artifactId>maven-compiler-plugin</
artifactId> <version>3.6.0</version> <configuration> <source>1.8</source> <
target>1.8</target> </configuration> </plugin> <plugin> <groupId>
org.apache.maven.plugins</groupId> <artifactId>maven-surefire-plugin</artifactId
> <version>2.19</version> <configuration> <skip>true</skip> </configuration> </
plugin> </plugins> </build> </project>
进行远程提交，注意两点
- setMaster(master)：master变量必须为远程集群
-
setJars(List(“D:\JetBrains\workspace\WordCount\out\artifacts\WordCount_jar\WordCount.jar”))：设置本地jar的目录

设置好后，点击运行即可

二、对程序进行远程调试

1.首先，在集群配置文件sparkk-env.sh中加入一下代码
export SPARK_SUBMIT_OPTS=
"-agentlib:jdwp=transport=dt_socket,server=y,suspend=y,address=5005"
这里对上面的几个参数进行说明： -Xdebug 启用调试特性 -Xrunjdwp 启用JDWP实现，包含若干子选项： transport=dt_socket
JPDA front-end和back-end之间的传输方法。dt_socket表示使用套接字传输。 address=8888
JVM在8888端口上监听请求，这个设定为一个不冲突的端口即可。 server=y y表示启动的JVM是被调试者。如果为n，则表示启动的JVM是调试器。
suspend=y y表示启动的JVM会暂停等待，直到调试器连接上才继续执行。suspend=n，则JVM不会暂停等待。
2.scala代码和远程提交的代码一样
3.idea的设置
对运行进行配置

添加远程设置

根据spark集群中spark-env.sh的SPARK_SUBMIT_OPTS的变量，对远程执行进行配置

配置完成后，设置断点，在scala程序右键debug即可

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