标签：fetch 统计 print efault oda boot 消费者 poi executor
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
Spark-Streaming hdfs count 案例
2. 启动 flume
flume-ng agent -c conf -f conf/kafka_test.conf -n a1 -Dflume.root.logger=INFO,console
flume 配置文件如下
# 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
kafka-console-consumer.sh --bootstrap-server master:9092 --topic kaka
4. 下面就是 Streaming 的统计代码
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
# 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,$SPARK_HOME/jars/guava-14.0.1.jar ./SparkPro-1.0-SNAPSHOT-jar-with-dependencies.jar master:2181 group_id_1 kaka 1
6. 写数据，写到对应flume 监控的文件就行
import random
import time
readFileName="/root/orders.csv"
writeFileName="/root/code/flume_exec_test.txt"
with open(writeFileName,‘a+‘)as wf:
with open(readFileName,‘rb‘) as f:
for line in f.readlines():
for word in line.split(" "):
ss = line.strip()
if len(ss)<1:
continue
wf.write(ss+‘\n‘)
rand_num = random.random()
time.sleep(rand_num)
7. 观察消费者是否消费到数据，在执行脚本的时候发现以下错误，一个是窗口时间的问题，一个是要设置 checkpoint。
窗口时间设置不对，会报以下错误
User class threw exception: java.lang.IllegalArgumentException: requirement failed: The window duration of ReducedWindowedDStream (3000 ms) must be multiple of the slide duration of parent DStream (10000 ms) at scala.Predef$.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$4.run(ApplicationMaster.scala:721)
错误修改，需要将窗口时间设置成滑动时间的倍数。上面给出的脚本已经是修改过的，如果安装上面的步骤操作，就不会报这个错误了。
如果没有增加 checkpoint，也会报错，报错如下：
requirement failed: The checkpoint directory has not been set. Please set it by StreamingContext.checkpoint().
设置相应的 checkpoint 即可。
# 在统计代码中加入下面这个语句
# val ssc = new StreamingContext(sparkConf, Seconds(2))
ssc.setCheckPoint("/root/checkpoint")
如果以上执行完成，可以在浏览器中查看日志，会看到对应的统计信息。
# 登录 192.168.56.122:8080 # 查看对应的日志信息
总结，在测试的时候，启动 flume 的时候遇到了一个错误，错误如下：
[WARN - kafka.utils.Logging$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。
Spark-Streaming kafka count 案例
标签：fetch 统计 print efault oda boot 消费者 poi executor
原文地址：https://www.cnblogs.com/hanwen1014/p/11260456.html
