Spark kafka + streaming自适应topic partitions变化

爱被打了一巴掌 2022-04-24 11:48 202阅读 0赞

### 背景 ###

1.  spark streaming + kafka 有两种方案接收kafka数据-基于receiver的方案和direct方案(no receiver方案)。

*  基于receiver的方案，属于比较老的方案，其采用Kafka’s high-level API通过专门的Rceiver去接收kafka数据。  
    采用 **KafkaUtils.createStream**
 *  direct方案，是当前的主流用法，其采用Kafka’s simple consumer API，创建的RDD partitions数与kafka partitions数一致。性能比前者好。  
    采用 **KafkaUtils.createDirectStream**
    
    具体的介绍可以查看官方介绍：[Spark Streaming + Kafka Integration Guide][Spark Streaming _ Kafka Integration Guide]

1.  对于第二种方案，spark streaming启动后当kafka的topic partition数从A升到B时，新增的分区上的数据会丢失。  
    spark streaming只从topic中读取原来的A个分区数据，新增的分区并不能被spark streaming感知到。  
    更具体的原因是

DirectKafkaInputDStream#compute生成的KafkaRDD, 其partitions数与spark streaming启动时topic的partitions数一致，topic的partitions和offset保存在currentOffsets map变量中，
    这个变量在启动时初始化，后续不会根据topic的partition变化进行更新。所以导致kafka新增的partitions数据
    会丢失。

更详细的分析，可以参考下面的文章：  
[Spark Streaming 自适应上游 kafka topic partition 数目变化][Spark Streaming _ kafka topic partition]

**因为spark streaming是24x7运行的，如何让spark streaming不重启的情况下，自适应topic partitions的变化？**  
且能够符合如下要求:

1.  不需要重新编译spark源码, 因为源代码不是我们维护的。
2.  能够灵活的部署

### 解决方案 ###

思路可以参考  
[Spark Streaming 自适应上游 kafka topic partition 数目变化][Spark Streaming _ kafka topic partition]

总的解决方案如下：

1.  MTDirectKafkaInputDStream继承DirectKafkaInputDStream，override compute方法，在每次生成KafkaRDD时，更新currentOffsets中的分区信息。
2.  在org.apache.spark.streaming.kafka路径下,新建一个KafkaUtils.scala文件，里面的代码直接将spark源码中的KafkaUtils源码复制过来。 修改新建的KafkaUtils.scala，将createDirectStream中new DirectKafkaInputDStream，替换为 new MTDirectKafkaInputDStream.

具体的实现步骤如下

1.  新建maven工程 kafkastreamingadpter, 配置好scala相关的pom配置。
2.  新建package org.apache.spark.streaming.kafka
    
    **备注**：这里需要说明，因为下面创建的MTDirectKafkaInputDStream需要继承DirectKafkaInputDStream，而DirectKafkaInputDStream是private\[streaming\]修饰的。**这里的规避技巧就是，将需要继承DirectKafkaInputDStream的子类对应的package设置为和DirectKafkaInputDStream一致，即可规避private无法继承的问题。**
3.  新建MTDirectKafkaInputDStream继承DirectKafkaInputDStream

override compute方法，在每次生成KafkaRDD时，更新currentOffsets中的分区信息。

1.  在org.apache.spark.streaming.kafka路径下,新建一个KafkaUtils.scala文件，里面的代码直接复制spark源码中的KafkaUtils源码。修改新建的KafkaUtils.scala，将createDirectStream中new DirectKafkaInputDStream，替换为 new MTDirectKafkaInputDStream
    
    **备注**：streaming代码中是通过 KafkaUtils.createDirectStream来创建stream的。因此希望KafkaUtils.createDirectStream返回的是MTDirectKafkaInputDStream。所以要修改KafkaUtils代码，将createDirectStream中new DirectKafkaInputDStream，修改为 new MTDirectKafkaInputDStream。  
    **我们不希望修改spark源码，所以这里用来个小技巧，直接将KafkaUtils代码copy一份出来进行修改，需要保持KafkaUtils的package路径一致**
    
    2,4中用到的小技巧参考了
    
    \[2016中国云计算技术大会-腾讯林立伟-Spark-Streaming在腾讯广点通的应用.pdf\]
    
    [Spark Streaming 自适应上游 kafka topic partition 数目变化][Spark Streaming _ kafka topic partition]
2.  新建spark streaming + kafka应用实例DirectKafkaWordCount1  
    实例需要配置 spark、kafka streaming相关的依赖。

<dependency>
                <groupId>org.apache.spark</groupId>
                <artifactId>spark-streaming-kafka-0-8_2.11</artifactId>
                <version>2.2.0</version>
            </dependency>
            <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-streaming_2.11</artifactId>
                <version>2.2.0</version>
            </dependency>

1.  mvn clean package 打包 生成kafka-streaming-adpter-1.0.jar  
    对应的配置文件如下

<?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>com.sensetime.iva</groupId>
        <artifactId>kafka-streaming-adpter</artifactId>
        <version>1.0</version>
        <packaging>jar</packaging>
        <properties>
            <scala.version>2.11.8</scala.version>
            <commons.codec.version>1.8</commons.codec.version>
            <grizzled.version>1.0.1</grizzled.version>
            <slf4j-log4j12.version>1.7.5</slf4j-log4j12.version>
        </properties>
    
        <dependencies>
            <dependency>
                <groupId>org.apache.spark</groupId>
                <artifactId>spark-streaming-kafka-0-8_2.11</artifactId>
                <version>2.2.0</version>
            </dependency>
            <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-streaming_2.11</artifactId>
                <version>2.2.0</version>
            </dependency>
            <dependency>
                <groupId>org.scala-lang</groupId>
                <artifactId>scala-library</artifactId>
                <version>${scala.version}</version>
            </dependency>
            <dependency>
                <groupId>junit</groupId>
                <artifactId>junit</artifactId>
                <version>3.8.1</version>
                <scope>test</scope>
            </dependency>
            <dependency>
                <groupId>commons-codec</groupId>
                <artifactId>commons-codec</artifactId>
                <version>${commons.codec.version}</version>
            </dependency>
            <dependency>
                <groupId>org.clapper</groupId>
                <artifactId>grizzled-slf4j_2.10</artifactId>
                <version>${grizzled.version}</version>
            </dependency>
            <dependency>
                <groupId>org.slf4j</groupId>
                <artifactId>slf4j-log4j12</artifactId>
                <version>${slf4j-log4j12.version}</version>
            </dependency>
        </dependencies>
        <build>
            <sourceDirectory>src/main/scala</sourceDirectory>
            <testSourceDirectory>src/test/scala</testSourceDirectory>
            <plugins>
                <plugin>
                    <groupId>org.scala-tools</groupId>
                    <artifactId>maven-scala-plugin</artifactId>
                    <executions>
                        <execution>
                            <goals>
                                <goal>compile</goal>
                                <goal>testCompile</goal>
                            </goals>
                        </execution>
                    </executions>
                    <configuration>
                        <scalaVersion>${scala.version}</scalaVersion>
                    </configuration>
                </plugin>
            </plugins>
        </build>
    </project>

1.  上传jar包到spark on k8s的根路径下的jars，进行镜像打包

node 73节点
    cd /home/huangchuibi/k8s/spark-2.2.0-k8s-0.5.0-bin-2.7.3
    ./sbin/build-push-docker-images.sh -r register.sensetime.sz.test-portus.com/spark-on-k8s -t 0.1 build
    ./sbin/build-push-docker-images.sh -r register.sensetime.sz.test-portus.com/spark-on-k8s -t 0.1 push

1.  在k8s各个节点上pull 镜像，因为spark on k8s，配置镜像拉取策略为本地有就不再拉取。所以需要手动进行拉取。

docker pull register.sensetime.sz.test-portus.com/spark-on-k8s/spark-driver:0.1
    docker pull register.sensetime.sz.test-portus.com/spark-on-k8s/spark-executor:0.1

1.  创建topic

ssh node91
    kubectl -n snappydata exec -it kafka-0 /bin/bash
    cd /opt/kafka_2.11-0.10.2.0
    bin/kafka-topics.sh --create --zookeeper zookeeper-0.zookeeper.snappydata.svc.cluster.local:2181,zookeeper-1.zookeeper.snappydata.svc.cluster.local:2181,zookeeper-2.zookeeper.snappydata.svc.cluster.local:2181/kafka --replication-factor 1 --partitions 1 --topic test8

1.  运行streaming 应用

bin/spark-submit \
      --deploy-mode cluster \
      --class org.apache.spark.examples.streaming.DirectKafkaWordCount1 \
      --master k8s://http://172.20.2.91:8001 \
      --kubernetes-namespace snappydata \
      --conf spark.executor.instances=3 \
      --conf spark.app.name=spark-streaming \
      --conf spark.kubernetes.driver.docker.image=register.sensetime.sz.test-portus.com/spark-on-k8s/spark-driver:0.1 \
      --conf spark.kubernetes.authenticate.driver.serviceAccountName=spark \
      --conf spark.kubernetes.executor.docker.image=register.sensetime.sz.test-portus.com/spark-on-k8s/spark-executor:0.1 \
      --conf spark.kubernetes.initcontainer.docker.image=register.sensetime.sz.test-portus.com/spark-on-k8s/spark-init:0.1 \
      --conf "spark.driver.extraJavaOptions=-Xss10m" \
      --conf "spark.executor.extraJavaOptions=-Xss10m" \
      --conf "spark.driver.extraClassPath=/opt/spark/examples/jars/kafka-streaming-adpter-1.0.jar" \
      --conf "spark.executor.extraClassPath=/opt/spark/examples/jars/kafka-streaming-adpter-1.0.jar" \
      --conf "spark.executor.userClassPathFirst=true" \
      --conf "spark.driver.userClassPathFirst=true" \
      local:///opt/spark/examples/jars/kafka-streaming-adpter-1.0.jar kafka-0.kafka.snappydata.svc.cluster.local:9092,kafka-1.kafka.snappydata.svc.cluster.local:9092,kafka-2.kafka.snappydata.svc.cluster.local:9092 test8

**备注** 这里增加的四个conf配置，是为了让kafka-streaming-adpter-1.0.jar里的KafkaUtils优先级高于spark源码中的KafkaUtils。但是实际测试发现，应用还是以同一个jar包中的KafkaUtils为优先级最高。所以下面的四个配置也可以去掉。

--conf "spark.driver.extraClassPath=/opt/spark/examples/jars/kafka-streaming-adpter-1.0.jar" \
    --conf "spark.executor.extraClassPath=/opt/spark/examples/jars/kafka-streaming-adpter-1.0.jar" \
    --conf "spark.executor.userClassPathFirst=true" \
    --conf "spark.driver.userClassPathFirst=true" \

1.  动态增加分区

kafka-topics.sh --zookeeper zookeeper-0.zookeeper.snappydata.svc.cluster.local:2181,zookeeper-1.zookeeper.snappydata.svc.cluster.local:2181,zookeeper-2.zookeeper.snappydata.svc.cluster.local:2181/kafka -alter -partitions 2 --topic test8

[Spark Streaming _ Kafka Integration Guide]: https://link.jianshu.com?t=https%3A%2F%2Fspark.apache.org%2Fdocs%2Flatest%2Fstreaming-kafka-0-8-integration.html
[Spark Streaming _ kafka topic partition]: https://link.jianshu.com?t=http%3A%2F%2Fwww.klion26.com%2F2016%2F11%2F01%2Fspark-streaming-topic-partition-change-auto-adaptive%2F