Spark因org.apache.kafka.common.serialization.StringDeserializer的NoClassDefFoundError而失败 [英] Spark fails with NoClassDefFoundError for org.apache.kafka.common.serialization.StringDeserializer

问题描述

我正在开发一个通用的Spark应用程序，该应用程序使用Spark和Java侦听Kafka流.

I am developing a generic Spark application that listens to a Kafka stream using Spark and Java.

我正在使用kafka_2.11-0.10.2.2，spark-2.3.2-bin-hadoop2.7-在发布此问题之前，我还尝试了其他几种kafka/spark组合.

I am using kafka_2.11-0.10.2.2, spark-2.3.2-bin-hadoop2.7 - I also tried several other kafka/spark combinations before posting this question.

代码在加载StringDeserializer类时失败:

The code fails at loading StringDeserializer class:

 SparkConf sparkConf = new SparkConf().setAppName("JavaDirectKafkaWordCount");
    JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, Durations.seconds(2));

    Set<String> topicsSet = new HashSet<>();
    topicsSet.add(topics);
    Map<String, Object> kafkaParams = new HashMap<>();
    kafkaParams.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, brokers);
    kafkaParams.put(ConsumerConfig.GROUP_ID_CONFIG, groupId);
    kafkaParams.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
    kafkaParams.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);

我得到的错误是:

Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/kafka/common/serialization/StringDeserializer

来自为什么Spark应用程序失败，线程"main"中出现"Exception". java.lang.NoClassDefFoundError:... StringDeserializer"?看来这可能是scala版本不匹配的问题，但是我的pom.xml没有该问题:

From Why does Spark application fail with "Exception in thread "main" java.lang.NoClassDefFoundError: ...StringDeserializer"? it seems that this could be a scala version mismatch issue, but my pom.xml doesn't have that issue:

<?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>yyy.iot.ckc</groupId>
<artifactId>sparkpoc</artifactId>
<version>1.0-SNAPSHOT</version>

<name>sparkpoc</name>
<!-- FIXME change it to the project's website -->
<url>http://www.example.com</url>

<properties>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    <maven.compiler.source>1.8</maven.compiler.source>
    <maven.compiler.target>1.8</maven.compiler.target>
    <java.version>1.8</java.version>

    <spark.scala.version>2.11</spark.scala.version>
    <spark.version>2.3.2</spark.version>
</properties>

<dependencies>
    <dependency>
        <groupId>junit</groupId>
        <artifactId>junit</artifactId>
        <version>4.11</version>
        <scope>test</scope>
    </dependency>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-core_${spark.scala.version}</artifactId>
        <version>${spark.version}</version>
    </dependency>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-streaming_${spark.scala.version}</artifactId>
        <version>${spark.version}</version>
    </dependency>

    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-streaming-kafka-0-10_${spark.scala.version}</artifactId>
        <version>${spark.version}</version>
    </dependency>

</dependencies>

<build>
    <pluginManagement><!-- lock down plugins versions to avoid using Maven defaults (may be moved to parent pom) -->
        <plugins>
            <!-- clean lifecycle, see https://maven.apache.org/ref/current/maven-core/lifecycles.html#clean_Lifecycle -->
            <plugin>
                <artifactId>maven-clean-plugin</artifactId>
                <version>3.1.0</version>
            </plugin>
            <!-- default lifecycle, jar packaging: see https://maven.apache.org/ref/current/maven-core/default-bindings.html#Plugin_bindings_for_jar_packaging -->
            <plugin>
                <artifactId>maven-resources-plugin</artifactId>
                <version>3.0.2</version>
            </plugin>
            <plugin>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.8.0</version>
            </plugin>
            <plugin>
                <artifactId>maven-surefire-plugin</artifactId>
                <version>2.22.1</version>
            </plugin>
            <plugin>
                <artifactId>maven-jar-plugin</artifactId>
                <version>3.0.2</version>
            </plugin>
            <plugin>
                <artifactId>maven-install-plugin</artifactId>
                <version>2.5.2</version>
            </plugin>
            <plugin>
                <artifactId>maven-deploy-plugin</artifactId>
                <version>2.8.2</version>
            </plugin>
            <!-- site lifecycle, see https://maven.apache.org/ref/current/maven-core/lifecycles.html#site_Lifecycle -->
            <plugin>
                <artifactId>maven-site-plugin</artifactId>
                <version>3.7.1</version>
            </plugin>
            <plugin>
                <artifactId>maven-project-info-reports-plugin</artifactId>
                <version>3.0.0</version>
            </plugin>
        </plugins>
    </pluginManagement>
</build>
</project>

我使用的提交脚本是:

./bin/spark-submit \
    --class "yyy.iot.ckc.KafkaDataModeler" \
    --master local[2] \
    ../sparkpoc/target/sparkpoc-1.0-SNAPSHOT.jar

有人能指出我要去哪里的正确方向吗?

Can anyone please point me in the right direction as to where I am going wrong?

推荐答案

Spark通过运行JVM实例来运行程序.因此，如果库(JAR)不在该JVM的类路径中，我们将遇到此运行时异常.解决方案是将所有从属JAR与主JAR打包在一起.以下构建脚本将适用于此.

Spark runs the program as by running an instance of a JVM. So if the libraries (JARs) are not in the classpath of that JVM we run into this runtime exception. The solution is to package all the dependent JARs along with main JAR. The following build script will work for that.

此外，如 https://stackoverflow.com/a/54583941/1224075 所述，火花的范围需要声明-core和spark-streaming库.这是因为某些库是由Spark JVM隐式提供的.

Also, as mentioned in https://stackoverflow.com/a/54583941/1224075 the scope of the spark-core and spark-streaming libraries need to be declared as provided. This is because some of the libraries are implicitly provided by the Spark JVM.

对我有用的POM的构建部分-

The build section of the POM which worked for me -

<build>
    <plugins>
        <plugin>
            <groupId>org.apache.maven.plugins</groupId>
            <artifactId>maven-assembly-plugin</artifactId>
            <version>2.2.1</version>
            <configuration>
                <descriptorRefs>
                    <descriptorRef>jar-with-dependencies</descriptorRef>
                </descriptorRefs>
            </configuration>
            <executions>
                <execution>
                    <id>make-assembly</id>
                    <phase>package</phase>
                    <goals>
                        <goal>single</goal>
                    </goals>
                </execution>
            </executions>
        </plugin>
    </plugins>
</build>

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