apache-spark - 为什么格式 ("kafka")失败并出现 "Failed to find data source: kafka."(即使使用 uber-jar )？

Question

我将 HDP-2.6.3.0 与 Spark2 包 2.2.0 一起使用。

我正在尝试使用结构化流 API 编写 Kafka 消费者，但在将作业提交到集群后出现以下错误:

Exception in thread "main" java.lang.ClassNotFoundException: Failed to find data source: kafka. Please find packages at http://spark.apache.org/third-party-projects.html
at org.apache.spark.sql.execution.datasources.DataSource$.lookupDataSource(DataSource.scala:553)
at org.apache.spark.sql.execution.datasources.DataSource.providingClass$lzycompute(DataSource.scala:89)
at org.apache.spark.sql.execution.datasources.DataSource.providingClass(DataSource.scala:89)
at org.apache.spark.sql.execution.datasources.DataSource.sourceSchema(DataSource.scala:198)
at org.apache.spark.sql.execution.datasources.DataSource.sourceInfo$lzycompute(DataSource.scala:90)
at org.apache.spark.sql.execution.datasources.DataSource.sourceInfo(DataSource.scala:90)
at org.apache.spark.sql.execution.streaming.StreamingRelation$.apply(StreamingRelation.scala:30)
at org.apache.spark.sql.streaming.DataStreamReader.load(DataStreamReader.scala:150)
at com.example.KafkaConsumer.main(KafkaConsumer.java:21)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$runMain(SparkSubmit.scala:782)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:180)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:119)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.lang.ClassNotFoundException: kafka.DefaultSource
at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
at org.apache.spark.sql.execution.datasources.DataSource$anonfun$22$anonfun$apply$14.apply(DataSource.scala:537)
at org.apache.spark.sql.execution.datasources.DataSource$anonfun$22$anonfun$apply$14.apply(DataSource.scala:537)
at scala.util.Try$.apply(Try.scala:192)
at org.apache.spark.sql.execution.datasources.DataSource$anonfun$22.apply(DataSource.scala:537)
at org.apache.spark.sql.execution.datasources.DataSource$anonfun$22.apply(DataSource.scala:537)
at scala.util.Try.orElse(Try.scala:84)
at org.apache.spark.sql.execution.datasources.DataSource$.lookupDataSource(DataSource.scala:537)
... 17 more

关注 spark-submit命令:

$SPARK_HOME/bin/spark-submit \
     ​--master yarn \
​     --deploy-mode client \
​​     --class com.example.KafkaConsumer \​
​     --executor-cores 2 \
​​     --executor-memory 512m \​           
     --driver-memory 512m \​           
     sample-kafka-consumer-0.0.1-SNAPSHOT.jar​

我的java代码:

package com.example;

import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;

public class KafkaConsumer {

    public static void main(String[] args) {

        SparkSession spark = SparkSession
                  .builder()
                  .appName("kafkaConsumerApp")
                  .getOrCreate();

        Dataset<Row> ds = spark
                  .readStream()
                  .format("kafka")
                  .option("kafka.bootstrap.servers", "dog.mercadoanalitico.com.br:6667")
                  .option("subscribe", "my-topic")
                  .load();
    }
}

pom.xml:

<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>com.example</groupId>
  <artifactId>sample-kafka-consumer</artifactId>
  <version>0.0.1-SNAPSHOT</version>
  <packaging>jar</packaging>

    <dependencies>

        <!-- spark -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>2.2.0</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.11</artifactId>
            <version>2.2.0</version>
        </dependency>


        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql-kafka-0-10_2.11</artifactId>
            <version>2.2.0</version>
        </dependency>

        <!-- kafka -->
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka_2.11</artifactId>
            <version>0.10.1.0</version>
        </dependency>


    </dependencies>  


    <repositories>
        <repository>
            <id>local-maven-repo</id>
            <url>file:///${project.basedir}/local-maven-repo</url>
        </repository>
    </repositories> 

    <build>

        <!-- Include resources folder in the .jar -->
        <resources>
            <resource>
                <directory>${basedir}/src/main/resources</directory>
            </resource>
        </resources>

        <plugins>

            <!-- Plugin to compile the source. -->
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.6.1</version>
                <configuration>
                    <source>1.8</source>
                    <target>1.8</target>
                </configuration>
            </plugin>       

            <!-- Plugin to include all the dependencies in the .jar and set the main class. -->
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-shade-plugin</artifactId>
                <version>3.0.0</version>
                <executions>
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>shade</goal>
                        </goals>
                        <configuration>
                            <filters>
                                <!-- This filter is to workaround the problem caused by included signed jars.
                                     java.lang.SecurityException: Invalid signature file digest for Manifest main attributes
                                -->
                                <filter>
                                    <artifact>*:*</artifact>
                                    <excludes>
                                        <exclude>META-INF/*.SF</exclude>
                                        <exclude>META-INF/*.DSA</exclude>
                                        <exclude>META-INF/*.RSA</exclude>
                                    </excludes>
                                </filter>
                            </filters>
                            <transformers>
                                <transformer
                                    implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
                                    <mainClass>com.example.KafkaConsumer</mainClass>
                                </transformer>
                            </transformers>
                        </configuration>
                    </execution>
                </executions>
            </plugin>

        </plugins>
    </build>    
</project>

[更新] UBER-JAR

下面是 pom.xml 中用来生成 uber-jar 的配置

            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-shade-plugin</artifactId>
                <version>3.0.0</version>
                <executions>
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>shade</goal>
                        </goals>
                        <configuration>
                            <filters>
                                <!-- This filter is to workaround the problem caused by included signed jars.
                                     java.lang.SecurityException: Invalid signature file digest for Manifest main attributes
                                -->
                                <filter>
                                    <artifact>*:*</artifact>
                                    <excludes>
                                        <exclude>META-INF/*.SF</exclude>
                                        <exclude>META-INF/*.DSA</exclude>
                                        <exclude>META-INF/*.RSA</exclude>
                                    </excludes>
                                </filter>
                            </filters>
                            <transformers>
                                <transformer
                                    implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
                                    <mainClass>com.example.KafkaConsumer</mainClass>
                                </transformer>
                            </transformers>
                        </configuration>
                    </execution>
                </executions>
            </plugin>

Answer 1

kafka数据源是 external模块，默认情况下对 Spark 应用程序不可用。

您必须在 pom.xml 中将其定义为依赖项。 (正如您所做的那样)，但这只是将它放入 Spark 应用程序的第一步。

    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-sql-kafka-0-10_2.11</artifactId>
        <version>2.2.0</version>
    </dependency>

使用该依赖项，您必须决定是否要创建一个所谓的 uber-jar 将所有依赖项捆绑在一起(这会导致一个相当大的 jar 文件并使提交时间更长)或使用 --packages (或不太灵活的 --jars )选项在 spark-submit 处添加依赖项时间。

(还有其他选项，例如在 Hadoop HDFS 上存储所需的 jar 或使用特定于 Hadoop 发行版的方式为 Spark 应用程序定义依赖项，但让我们保持简单)

我建议使用 --packages首先且仅当它起作用时才考虑其他选项。

使用 spark-submit --packages包括 Spark -sql-kafka-0-10 模块如下。

spark-submit --packages org.apache.spark:spark-sql-kafka-0-10_2.11:2.2.0

根据需要包括其他命令行选项。

Uber-Jar 方法

在所谓的 中包含所有依赖项 super jar 由于 META-INF 的原因，可能并不总是有效目录被处理。

对于 kafka要工作的数据源(以及其他一般数据源)，您必须确保 META-INF/services/org.apache.spark.sql.sources.DataSourceRegister所有数据源中的 合并 (不是 replace 或 first 或您使用的任何策略)。
kafka数据源使用自己的 META-INF/services/org.apache.spark.sql.sources.DataSourceRegister注册 org.apache.spark.sql.kafka010.KafkaSourceProvider作为 kafka 的数据源提供者格式。