Spark-Streaming kafka count 案例

Streaming 统计来自 kafka 的数据，这里涉及到的比较，kafka 的数据是使用从 flume 获取到的，这里相当于一个小的案例。

1. 启动 kafka

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Spark-Streaming hdfs count 案例

2. 启动 flume

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flume-ng agent -c conf -f conf/kafka_test.conf -n a1 -Dflume.root.logger=INFO,console

　　flume 配置文件如下

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# Name the components on this agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1
 
# Describe/configure the source
a1.sources.r1.type = exec
a1.sources.r1.command = tail -f /root/code/flume_exec_test.txt
 
# Describe the sink
a1.sinks.k1.type = org.apache.flume.sink.kafka.KafkaSink
a1.sinks.k1.brokerList=master:9092
a1.sinks.k1.topic=kaka
a1.sinks.k1.serializer.class=kafka.serializer.StringEncoder
 
# Use a channel which buffers events in memory
a1.channels.c1.type = memory
a1.channels.c1.capacity = 10000
a1.channels.c1.transactionCapacity = 1000
 
# Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1

　　这里 flume 是的数据是来自一个文件，只要这个文件有数据进入，就会被flume监控到，测试的时候只需要往这个文件里写数据就可以了。

3. 启动 kafka 消费者来观察

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kafka-console-consumer.sh --bootstrap-server master:9092 --topic kaka

4. 下面就是 Streaming 的统计代码

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package com.hw.streaming
 
import org.apache.spark.SparkConf
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Minutes, Seconds, StreamingContext}
 
object KafkaWordCount {
  def main(args: Array[String]): Unit = {
    if (args.length < 4) {
      System.err.println("Usage: KafkaWordCount <zkQuorum> <group> <topics> <numThreads>")
      System.exit(1)
    }
 
    val Array(zkQuorum, group, topics, numThreads) = args
    val sparkConf = new SparkConf().setAppName("KafkaWordCount")
    val ssc = new StreamingContext(sparkConf, Seconds(2))
 
    val topicMap = topics.split(",").map((_, numThreads.toInt)).toMap
    val lines = KafkaUtils.createStream(ssc, zkQuorum, group, topicMap).map(_._2)
    val words = lines.flatMap(_.split(",")(1))
//    窗口大小10秒，滑动大小2秒，这里的窗口大小一定要是滑动大小的倍数关系才行
    val wordCounts = words.map((_, 1L)).reduceByKeyAndWindow(_ + _,_ - _,Seconds(10), Seconds(2))
    wordCounts.print()
 
    ssc.start()
    ssc.awaitTermination()
  }
 
}

5. 执行脚本

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# kafka count bash
$SPARK_HOME/bin/spark-submit\
        --class com.hw.streaming.KafkaWordCount\
        --master yarn-cluster \
        --executor-memory 1G \
        --total-executor-cores 2 \
        --files $HIVE_HOME/conf/hive-site.xml \
        --jars $HIVE_HOME/lib/mysql-connector-java-5.1.25-bin.jar,$SPARK_HOME/jars/datanucleus-api-jdo-3.2.6.jar,$SPARK_HOME/jars/datanucleus-core-3.2.10.jar,$SPARK_HOME/jars/datanucleus-rdbms-3.2.9.jar,$SPARK_HOME/jars/guava-14.0.1.jar \
        ./SparkPro-1.0-SNAPSHOT-jar-with-dependencies.jar \
        master:2181 group_id_1 kaka 1

6. 写数据，写到对应flume 监控的文件就行

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import random
import time
readFileName="/root/orders.csv"
writeFileName="/root/code/flume_exec_test.txt"
with open(writeFileName,'a+')as wf:
    with open(readFileName,'rb') as f:
        for line in f.readlines():
            for word in line.split(" "):
                ss = line.strip()
                if len(ss)<1:
                    continue
                wf.write(ss+'\n')
            rand_num = random.random()
            time.sleep(rand_num)

7. 观察消费者是否消费到数据，在执行脚本的时候发现以下错误，一个是窗口时间的问题，一个是要设置 checkpoint。

窗口时间设置不对，会报以下错误

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User class threw exception: java.lang.IllegalArgumentException: requirement failed: The window duration of ReducedWindowedDStream (3000 ms) must be multiple of the slide duration of parent DStream (10000 ms)
at scala.Predef$.require(Predef.scala:224)
at org.apache.spark.streaming.dstream.ReducedWindowedDStream.<init>(ReducedWindowedDStream.scala:39)
at org.apache.spark.streaming.dstream.PairDStreamFunctions$$anonfun$reduceByKeyAndWindow$6.apply(PairDStreamFunctions.scala:348)
at org.apache.spark.streaming.dstream.PairDStreamFunctions$$anonfun$reduceByKeyAndWindow$6.apply(PairDStreamFunctions.scala:343)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.SparkContext.withScope(SparkContext.scala:693)
at org.apache.spark.streaming.StreamingContext.withScope(StreamingContext.scala:265)
at org.apache.spark.streaming.dstream.PairDStreamFunctions.reduceByKeyAndWindow(PairDStreamFunctions.scala:343)
at org.apache.spark.streaming.dstream.PairDStreamFunctions$$anonfun$reduceByKeyAndWindow$5.apply(PairDStreamFunctions.scala:311)
at org.apache.spark.streaming.dstream.PairDStreamFunctions$$anonfun$reduceByKeyAndWindow$5.apply(PairDStreamFunctions.scala:311)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.SparkContext.withScope(SparkContext.scala:693)
at org.apache.spark.streaming.StreamingContext.withScope(StreamingContext.scala:265)
at org.apache.spark.streaming.dstream.PairDStreamFunctions.reduceByKeyAndWindow(PairDStreamFunctions.scala:310)
at com.badou.streaming.KafkaWordCount$.main(KafkaWordCount.scala:22)
at com.badou.streaming.KafkaWordCount.main(KafkaWordCount.scala)
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.yarn.ApplicationMaster$$anon$4.run(ApplicationMaster.scala:721)

错误修改，需要将窗口时间设置成滑动时间的倍数。上面给出的脚本已经是修改过的，如果安装上面的步骤操作，就不会报这个错误了。

如果没有增加 checkpoint，也会报错，报错如下：

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requirement failed: The checkpoint directory has not been set. Please set it by StreamingContext.checkpoint().

设置相应的 checkpoint 即可。

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# 在统计代码中加入下面这个语句
# val ssc = new StreamingContext(sparkConf, Seconds(2))
ssc.setCheckPoint("/root/checkpoint")

如果以上执行完成，可以在浏览器中查看日志，会看到对应的统计信息。　

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# 登录 192.168.56.122:8080
# 查看对应的日志信息

总结，在测试的时候，启动 flume 的时候遇到了一个错误，错误如下：

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[WARN - kafka.utils.Logging$class.warn(Logging.scala:83)]
Error while fetching metadata     partition 4     leader: none    replicas:       isr
:    isUnderReplicated: false for topic partition [default-flume-topic,4]:
[class kafka.common.LeaderNotAvailableException]

遇到这个错误的原因主要是 flume 配置文件中，设置的 kafka sink 不对导致的，可以看到本应该监听的 topic 是 kaka，但是这里监控的却是默认的 default-flume-topic，经过检查终于发现错误是由于不细心导致的，把 sinks 写成 sink 了，一定要注意细节，一定要学会看日志。

 

posted @   听~雨  阅读(610)  评论(0)  编辑  收藏  举报
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