Spark Streaming 结合FlumeNG使用实例
SparkStreaming是一个对实时数据流进行高通量、容错处理的流式处理系统，可以对多种数据源（如Kdfka、Flume、Twitter、Zero和TCP 套接字）进行类似map、reduce、join、window等复杂操作，并将结果保存到外部文件系统、数据库或应用到实时仪表盘。
Spark Streaming流式处理系统特点有：
- 将流式计算分解成一系列短小的批处理作业
- 将失败或者执行较慢的任务在其它节点上并行执行
- 较强的容错能力(基于RDD继承关系Lineage)
- 使用和RDD一样的语义
本文将Spark Streaming结合FlumeNG，然后以源码中的JavaFlumeEventCount作参考，建立maven工程，打包在spark standalone集群运行。
一、步骤
1.建立maven工程，写好pom.xml
需要spark streaming的flume插件包，jar的maven地址如下，填入pom.xml中
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-flume_2.10</artifactId>
<version>1.1.0</version>
</dependency>
完整的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>test</groupId>
<artifactId>hq</artifactId>
<version>0.0.1-SNAPSHOT</version>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>2.3.2</version>
<configuration>
<source>1.6</source>
<target>1.6</target>
<compilerVersion>1.6</compilerVersion>
<encoding>UTF-8</encoding>
</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-jar-plugin</artifactId>
<version>2.3.2</version>
<configuration>
<archive>
<manifest>
<addClasspath>true</addClasspath>
<classpathPrefix>.</classpathPrefix>
<mainClass>JavaFlumeEventCount</mainClass>
</manifest>
</archive>
</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-assembly-plugin</artifactId>
<version>2.4</version>
<configuration>
<descriptorRefs>
<descriptorRef>jar-with-dependencies</descriptorRef>
</descriptorRefs>
</configuration>
</plugin>
</plugins>
</build>
<dependencies>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-flume_2.10</artifactId>
<version>1.1.0</version>
</dependency>
</dependencies>
</project>
2.编码并且打包
JavaCode：
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.streaming.*;
import org.apache.spark.streaming.api.java.*;
import org.apache.spark.streaming.flume.FlumeUtils;
import org.apache.spark.streaming.flume.SparkFlumeEvent; public final class JavaFlumeEventCount {
private JavaFlumeEventCount() {
} public static void main(String[] args) { String host = args[0];
int port = Integer.parseInt(args[1]); Duration batchInterval = new Duration(Integer.parseInt(args[2]));
SparkConf sparkConf = new SparkConf().setAppName("JavaFlumeEventCount");
JavaStreamingContext ssc = new JavaStreamingContext(sparkConf,
batchInterval);
JavaReceiverInputDStream<SparkFlumeEvent> flumeStream = FlumeUtils
.createStream(ssc, host, port); flumeStream.count(); flumeStream.count().map(new Function<Long, String>() {
private static final long serialVersionUID = -572435064083746235L; public String call(Long in) {
return "Received " + in + " flume events.";
}
}).print(); ssc.start();
ssc.awaitTermination();
}
}
maven 命令：eclipse中run as -> Maven Assembly:assembly
得到工程的target目录下得到jar包：hq-0.0.1-SNAPSHOT.jar
3.将3个jar包上传到服务器，准备运行
除了自身打的jar包外，运行还需要：spark-streaming-flume_2.10-1.1.0.jar,flume-ng-sdk-1.4.0.jar 这2个jar包（我使用的flume-ng版本是1.4.0）
将3个jar包上传到服务器~/spark/test/目录下。
4.命令行提交任务，运行
[ebupt@eb174 test]$ spark-submit --master spark://eb174:7077 --name FlumeStreaming --class JavaFlumeEventCount --executor-memory 1G --total-executor-cores 2 --jars spark-streaming-flume_2.10-1.1.0.jar,flume-ng-sdk-1.4.0.jar hq.jar eb174 11000 5000
注意：参数解释：spark-submit --help。自己可以根据需要修改内存，防止OOM。另外jars可以同时加载多个jar包，逗号分隔。指定的运行类后需要指定3个参数。
5.开启flume-ng，启动数据源
书写好flume的agent配置文件spark-flumeng.conf，内容如下：
#Agent5
#List the sources, sinks and channels for the agent
agent5.sources = source1
agent5.sinks = hdfs01
agent5.channels = channel1 #set channel for sources and sinks
agent5.sources.source1.channels = channel1
agent5.sinks.hdfs01.channel = channel1 #properties of someone source
agent5.sources.source1.type = spooldir
agent5.sources.source1.spoolDir = /home/hadoop/huangq/spark-flumeng-data/
agent5.sources.source1.ignorePattern = .*(\\.index|\\.tmp|\\.xml)$
agent5.sources.source1.fileSuffix = .1
agent5.sources.source1.fileHeader = true
agent5.sources.source1.fileHeaderKey = filename # set interceptors
agent5.sources.source1.interceptors = i1 i2
agent5.sources.source1.interceptors.i1.type = org.apache.flume.interceptor.HostInterceptor$Builder
agent5.sources.source1.interceptors.i1.preserveExisting = false
agent5.sources.source1.interceptors.i1.hostHeader = hostname
agent5.sources.source1.interceptors.i1.useIP=false
agent5.sources.source1.interceptors.i2.type = org.apache.flume.interceptor.TimestampInterceptor$Builder #properties of mem-channel-1
agent5.channels.channel1.type = memory
agent5.channels.channel1.capacity = 100000
agent5.channels.channel1.transactionCapacity = 100000
agent5.channels.channel1.keep-alive = 30 #properties of sink
agent5.sinks.hdfs01.type = avro
agent5.sinks.hdfs01.hostname = eb174
agent5.sinks.hdfs01.port = 11000
启动flume-ng: [hadoop@eb170 flume]$ bin/flume-ng agent -n agent5 -c conf -f conf/spark-flumeng.conf
注意：
①flume的sink要用avro，指定要发送到的spark集群中的一个节点，我们这里是eb174:11000。
②如果没有指定Flume的sdk包，会出现错误： java.lang.NoClassDefFoundError: Lorg/apache/flume/source/avro/AvroFlumeEvent;没有找到类。这个类在flume的sdk包内，在jars参数中指定jar包位置就可以。
③将自己定义的运行jar包单独列出，不要放在jars参数指定，否则也会有错误抛出。
6.运行结果
在提交spark任务的客户端可以看到，看到大量的输出信息，然后可以看到有数据的RDD会统计出这个RDD有多少行，统计结果如下：
Spark assembly has been built with Hive, including Datanucleus jars on classpath
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
14/10/13 19:00:44 INFO SecurityManager: Changing view acls to: ebupt,
14/10/13 19:00:44 INFO SecurityManager: Changing modify acls to: ebupt,
14/10/13 19:00:44 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(ebupt, ); users with modify permissions: Set(ebupt, )
14/10/13 19:00:45 INFO Slf4jLogger: Slf4jLogger started
14/10/13 19:00:45 INFO Remoting: Starting remoting
14/10/13 19:00:45 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkDriver@eb174:51147]
14/10/13 19:00:45 INFO Remoting: Remoting now listens on addresses: [akka.tcp://sparkDriver@eb174:51147]
14/10/13 19:00:45 INFO Utils: Successfully started service 'sparkDriver' on port 51147.
14/10/13 19:00:45 INFO SparkEnv: Registering MapOutputTracker
14/10/13 19:00:45 INFO SparkEnv: Registering BlockManagerMaster
....
.....
14/10/13 19:09:21 INFO DAGScheduler: Missing parents: List()
14/10/13 19:09:21 INFO DAGScheduler: Submitting Stage 145 (MappedRDD[291] at map at MappedDStream.scala:35), which has no missing parents
14/10/13 19:09:21 INFO MemoryStore: ensureFreeSpace(3400) called with curMem=13047, maxMem=278302556
14/10/13 19:09:21 INFO MemoryStore: Block broadcast_110 stored as values in memory (estimated size 3.3 KB, free 265.4 MB)
14/10/13 19:09:21 INFO MemoryStore: ensureFreeSpace(2020) called with curMem=16447, maxMem=278302556
14/10/13 19:09:21 INFO MemoryStore: Block broadcast_110_piece0 stored as bytes in memory (estimated size 2020.0 B, free 265.4 MB)
14/10/13 19:09:21 INFO BlockManagerInfo: Added broadcast_110_piece0 in memory on eb174:41187 (size: 2020.0 B, free: 265.4 MB)
14/10/13 19:09:21 INFO BlockManagerMaster: Updated info of block broadcast_110_piece0
14/10/13 19:09:21 INFO DAGScheduler: Submitting 1 missing tasks from Stage 145 (MappedRDD[291] at map at MappedDStream.scala:35)
14/10/13 19:09:21 INFO TaskSchedulerImpl: Adding task set 145.0 with 1 tasks
14/10/13 19:09:21 INFO TaskSetManager: Starting task 0.0 in stage 145.0 (TID 190, eb175, PROCESS_LOCAL, 1132 bytes)
14/10/13 19:09:21 INFO BlockManagerInfo: Added broadcast_110_piece0 in memory on eb175:57696 (size: 2020.0 B, free: 519.6 MB)
14/10/13 19:09:21 INFO TaskSetManager: Finished task 0.0 in stage 145.0 (TID 190) in 25 ms on eb175 (1/1)
14/10/13 19:09:21 INFO DAGScheduler: Stage 145 (take at DStream.scala:608) finished in 0.026 s
14/10/13 19:09:21 INFO TaskSchedulerImpl: Removed TaskSet 145.0, whose tasks have all completed, from pool
14/10/13 19:09:21 INFO SparkContext: Job finished: take at DStream.scala:608, took 0.036589357 s
-------------------------------------------
Time: 1413198560000 ms
-------------------------------------------
Received 35300 flume events. 14/10/13 19:09:55 INFO JobScheduler: Finished job streaming job 1413198595000 ms.0 from job set of time 1413198595000 ms
14/10/13 19:09:55 INFO JobScheduler: Total delay: 0.126 s for time 1413198595000 ms (execution: 0.112 s)
14/10/13 19:09:55 INFO MappedRDD: Removing RDD 339 from persistence list
14/10/13 19:09:55 INFO BlockManager: Removing RDD 339
14/10/13 19:09:55 INFO MappedRDD: Removing RDD 338 from persistence list
14/10/13 19:09:55 INFO BlockManager: Removing RDD 338
14/10/13 19:09:55 INFO MappedRDD: Removing RDD 337 from persistence list
14/10/13 19:09:55 INFO BlockManager: Removing RDD 337
14/10/13 19:09:55 INFO ShuffledRDD: Removing RDD 336 from persistence list
14/10/13 19:09:55 INFO BlockManager: Removing RDD 336
14/10/13 19:09:55 INFO UnionRDD: Removing RDD 335 from persistence list
14/10/13 19:09:55 INFO BlockManager: Removing RDD 335
14/10/13 19:09:55 INFO MappedRDD: Removing RDD 333 from persistence list
14/10/13 19:09:55 INFO BlockManager: Removing RDD 333
14/10/13 19:09:55 INFO BlockRDD: Removing RDD 332 from persistence list
14/10/13 19:09:55 INFO BlockManager: Removing RDD 332
...
...
14/10/13 19:10:00 INFO TaskSchedulerImpl: Adding task set 177.0 with 1 tasks
14/10/13 19:10:00 INFO TaskSetManager: Starting task 0.0 in stage 177.0 (TID 215, eb175, PROCESS_LOCAL, 1132 bytes)
14/10/13 19:10:00 INFO BlockManagerInfo: Added broadcast_134_piece0 in memory on eb175:57696 (size: 2021.0 B, free: 530.2 MB)
14/10/13 19:10:00 INFO TaskSetManager: Finished task 0.0 in stage 177.0 (TID 215) in 24 ms on eb175 (1/1)
14/10/13 19:10:00 INFO DAGScheduler: Stage 177 (take at DStream.scala:608) finished in 0.024 s
14/10/13 19:10:00 INFO TaskSchedulerImpl: Removed TaskSet 177.0, whose tasks have all completed, from pool
14/10/13 19:10:00 INFO SparkContext: Job finished: take at DStream.scala:608, took 0.033844743 s
-------------------------------------------
Time: 1413198600000 ms
-------------------------------------------
Received 0 flume events.
二、结论
- flume-ng与spark的结合成功，可根据需要灵活编写相关的类来实现实时处理FlumeNG传输的数据。
- spark streaming和多种数据源结合，达到实时计算处理的能力。
三、参考资料
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