为什么format("kafka")失败并显示"Failed to find data source:kafka". (甚至使用uber-jar)? [英] Why does format("kafka") fail with "Failed to find data source: kafka." (even with uber-jar)?
问题描述
我在Spark2软件包2.2.0中使用HDP-2.6.3.0.
I use HDP-2.6.3.0 with Spark2 package 2.2.0.
我正在尝试使用结构化流API编写Kafka使用者，但是在将作业提交到集群后出现以下错误:
I'm trying to write a Kafka consumer, using the Structured Streaming API, but I'm getting the following error after submit the job to the cluster:
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
以下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>
[UPDATE] UBER-JAR
在pom.xml中用于生成uber-jar的配置下面
Below the configuration used in the pom.xml to generate the uber-jar
<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>
推荐答案
kafka data source is an external module and is not available to Spark applications by default.
您必须在pom.xml中将其定义为依赖项(如您所做的那样)，但这只是将其包含在Spark应用程序中的第一步.
You have to define it as a dependency in your pom.xml (as you have done), but that's just the very first step to have it in your Spark application.
<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 ，它将所有依赖项全部捆绑在一起(这将导致一个相当大的jar文件并进行提交时间更长)或使用--packages(或不太灵活的--jars)选项在spark-submit时间添加依赖项.
With that dependency you have to decide whether you want to create a so-called uber-jar that would have all the dependencies bundled altogether (that results in a fairly big jar file and makes the submission time longer) or use --packages (or less flexible --jars) option to add the dependency at spark-submit time.
(还有其他选项，例如将所需的jar存储在Hadoop HDFS上，或使用特定于Hadoop发行版的方式来定义Spark应用程序的依赖关系，但让我们保持简单)
(There are other options like storing the required jars on Hadoop HDFS or using Hadoop distribution-specific ways of defining dependencies for Spark applications, but let's keep things simple)
我建议先使用--packages，只有在可行时，才考虑使用其他选项.
I'd recommend using --packages first and only when it works consider the other options.
使用spark-submit --packages包含 spark-sql-kafka-0-10 模块，如下所示.
Use spark-submit --packages to include the spark-sql-kafka-0-10 module as follows.
spark-submit --packages org.apache.spark:spark-sql-kafka-0-10_2.11:2.2.0
根据需要添加其他命令行选项.
Include the other command-line options as you wish.
由于处理META-INF目录的方式，可能无法始终将所有依赖项包含在所谓的 uber-jar 中.
Including all the dependencies in a so-called uber-jar may not always work due to how META-INF directories are handled.
要使kafka数据源(通常是其他数据源)正常工作，您必须确保所有数据源中的META-INF/services/org.apache.spark.sql.sources.DataSourceRegister被合并(不是replace或first或您使用的任何策略).
For kafka data source to work (and other data sources in general) you have to ensure that META-INF/services/org.apache.spark.sql.sources.DataSourceRegister of all the data sources are merged (not replace or first or whatever strategy you use).
kafka data sources uses its own META-INF/services/org.apache.spark.sql.sources.DataSourceRegister that registers org.apache.spark.sql.kafka010.KafkaSourceProvider as the data source provider for kafka format.
这篇关于为什么format("kafka")失败并显示"Failed to find data source:kafka". (甚至使用uber-jar)?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
