标签：coder target 一个 ase top 分布 direct 2.0 com
Kakfa起初是由LinkedIn公司开发的一个分布式的消息系统，后成为Apache的一部分，它使用Scala编写，以可水平扩展和高吞吐率而被广泛使用。目前越来越多的开源分布式处理系统如Cloudera、Apache Storm、Spark等都支持与Kafka集成。
Spark streaming集成kafka是企业应用中最为常见的一种场景。
参考文档：
http://kafka.apache.org/quickstart#quickstart_createtopic
1、安装java
略
2、安装zookeeper集群
参考：http://www.cnblogs.com/wcwen1990/p/6652105.html
3、安装scala
略
4、安装kafka
下载kafka安装文件：
https://archive.apache.org/dist/kafka/0.8.2.1/kafka_2.10-0.8.2.1.tgz
解压kafka安装包：
# tar -zxvf kafka_2.10-0.8.2.1.tgz -C /opt/cdh-5.3.6/
# chown -R hadoop:hadoop /opt/cdh-5.3.6/kafka_2.10-0.8.2.1/
删除kafka libs/zookeeper jar包,拷贝自己安装集群zookeeper jar包到kafka libs目录下：
$ rm libs/zookeeper-3.4.6.jar –rf
$ cp /opt/cdh-5.3.6/zookeeper-3.4.5-cdh5.3.6/zookeeper-3.4.5-cdh5.3.6.jar libs/
5、定义kafka配置文件
5.1）定义server.properties：
host.name=chavin.king
log.dirs=/opt/cdh-5.3.6/kafka_2.10-0.8.2.1/kafka-logs
zookeeper.connect=chavin.king:2181
定义producer.properties：
metadata.broker.list=chavin.king:9092
定义consumer.properties：
zookeeper.connect=chavin.king:2181
5.2）启动kafka server
$ bin/kafka-server-start.sh config/server.properties
$ jps
14020 NameNode
57749 Jps
14776 QuorumPeerMain
57690 Kafka
14507 NodeManager
14235 ResourceManager
14093 DataNode
14686 JobHistoryServer
57663 ZooKeeperMain
[zk: localhost:2181(CONNECTED) 3] ls /
[controller, controller_epoch, brokers, zookeeper, admin, consumers, config, hbase]
5.3）创建一个topic
$ bin/kafka-topics.sh --create --zookeeper chavin.king:2181 --replication-factor 1 --partitions 1 --topic test
$ bin/kafka-topics.sh --list --zookeeper chavin.king:2181
5.4）创建一个生产者，产生数据
$ bin/kafka-console-producer.sh --broker-list chavin.king:9092 --topic test
5.5）创建一个消费者，消费数据
$ bin/kafka-console-consumer.sh --zookeeper chavin.king:2181 --topic test --from-beginning
在生产者shell窗口输入数据，在消费者窗口可以看到数据输出到界面上。
二、spark streaming与kafka集成参考文档：http://spark.apache.org/docs/1.3.0/streaming-kafka-integration.html
1、编译spark，获得集成kafka jar包：
参考文档：http://www.cnblogs.com/wcwen1990/p/7688027.html
说明：spark streaming集成flume或者kafka需要一些支持jar包，这些jar包在编译spark过程中会自动在external目录下生成相应的jar文件，因此，这里需要编译spark来获得这些jar包。
Spark streaming集成kafka主要需要：spark-streaming-kafka_2.10-1.3.0.jar包。
2、集成相关jar包
$ cp external/kafka/target/spark-streaming-kafka_2.10-1.3.0.jar /opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs/
$ cp libs/kafka_2.10-0.8.2.1.jar libs/kafka-clients-0.8.2.1.jar libs/zkclient-0.3.jar libs/metrics-core-2.2.0.jar /opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs/
[externalLibs]$ ls
kafka_2.10-0.8.2.1.jar
kafka-clients-0.8.2.1.jar
metrics-core-2.2.0.jar
spark-streaming-kafka_2.10-1.3.0.jar
zkclient-0.3.jar
1、编写spark streaming集成kafka的wordcount
import java.util.HashMap
import org.apache.spark._
import org.apache.spark.streaming._
import org.apache.spark.streaming.StreamingContext._
import org.apache.spark.streaming.kafka._
val ssc = new StreamingContext(sc, Seconds(5))
val topicMap = Map("test" -> 1)
// read data
val lines = KafkaUtils.createStream(ssc, "chavin.king:2181", "testWordCountGroup", topicMap).map(_._2)
val words = lines.flatMap(_.split(" "))
val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)
wordCounts.print()
ssc.start() // Start the computation
ssc.awaitTermination() // Wait for the computation to terminate
2、spark-shell local模式启动，并运行步骤1程序
bin/spark-shell --master local[2] --jars \
/opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs/spark-streaming-kafka_2.10-1.3.0.jar,/opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs/kafka_2.10-0.8.2.1.jar,/opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs/kafka-clients-0.8.2.1.jar,/opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs/zkclient-0.3.jar,/opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs/metrics-core-2.2.0.jar
scala> import java.util.HashMap
import java.util.HashMap
scala> import org.apache.spark._
import org.apache.spark._
scala> import org.apache.spark.streaming._
import org.apache.spark.streaming._
scala> import org.apache.spark.streaming.StreamingContext._
import org.apache.spark.streaming.StreamingContext._
scala> import org.apache.spark.streaming.kafka._
import org.apache.spark.streaming.kafka._
scala> val ssc = new StreamingContext(sc, Seconds(5))
ssc: org.apache.spark.streaming.StreamingContext = org.apache.spark.streaming.StreamingContext@1a28f9a0
scala> val topicMap = Map("test" -> 1)
topicMap: scala.collection.immutable.Map[String,Int] = Map(test -> 1)
scala> val lines = KafkaUtils.createStream(ssc, "chavin.king:2181", "testWordCountGroup", topicMap).map(_._2)
lines: org.apache.spark.streaming.dstream.DStream[String] = org.apache.spark.streaming.dstream.MappedDStream@27267641
scala>
scala> val words = lines.flatMap(_.split(" "))
words: org.apache.spark.streaming.dstream.DStream[String] = org.apache.spark.streaming.dstream.FlatMappedDStream@169b0639
scala> val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)
wordCounts: org.apache.spark.streaming.dstream.DStream[(String, Int)] = org.apache.spark.streaming.dstream.ShuffledDStream@14f2b1ba
scala> wordCounts.print()
scala> ssc.start()
scala>ssc.awaitTermination()
3、测试
在kafka生产者shell端输入：
hadoop oracle mysql mysql mysql
这是我们在kafka消费者端可以看到如下输出：
hadoop oracle mysql mysql mysql
同时在spark streaming端也可以看到如下输出：
-------------------------------------------
Time: 1500021590000 ms
-------------------------------------------
(mysql,3)
(oracle,1)
(hadoop,1)
1、编写spark streaming集成kafka的wordcount
import kafka.serializer.StringDecoder
import org.apache.spark._
import org.apache.spark.streaming._
import org.apache.spark.streaming.StreamingContext._
import org.apache.spark.streaming.kafka._
val ssc = new StreamingContext(sc, Seconds(5))
val kafkaParams = Map[String, String]("metadata.broker.list" -> "chavin.king:9092")
val topicsSet = Set("test")
// read data
val messages = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topicsSet)
val lines = messages.map(_._2)
val words = lines.flatMap(_.split(" "))
val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)
wordCounts.print()
ssc.start() // Start the computation
ssc.awaitTermination() // Wait for the computation to terminate
2、spark-shell local模式启动，并运行步骤1程序
bin/spark-shell --master local[2] --jars \
/opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs/spark-streaming-kafka_2.10-1.3.0.jar,/opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs/kafka_2.10-0.8.2.1.jar,/opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs/kafka-clients-0.8.2.1.jar,/opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs/zkclient-0.3.jar,/opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs/metrics-core-2.2.0.jar
scala> import kafka.serializer.StringDecoder
import kafka.serializer.StringDecoder
scala> import org.apache.spark._
import org.apache.spark._
scala> import org.apache.spark.streaming._
import org.apache.spark.streaming._
scala> import org.apache.spark.streaming.StreamingContext._
import org.apache.spark.streaming.StreamingContext._
scala> import org.apache.spark.streaming.kafka._
import org.apache.spark.streaming.kafka._
scala>
scala> val ssc = new StreamingContext(sc, Seconds(5))
ssc: org.apache.spark.streaming.StreamingContext = org.apache.spark.streaming.StreamingContext@2d05daca
scala>
scala> val kafkaParams = Map[String, String]("metadata.broker.list" -> "chavin.king:9092")
kafkaParams: scala.collection.immutable.Map[String,String] = Map(metadata.broker.list -> chavin.king:9092)
scala> val topicsSet = Set("test")
topicsSet: scala.collection.immutable.Set[String] = Set(test)
scala>
scala> // read data
scala> val messages = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topicsSet)
17/07/14 16:59:31 INFO VerifiableProperties: Verifying properties
17/07/14 16:59:31 INFO VerifiableProperties: Property group.id is overridden to
17/07/14 16:59:31 INFO VerifiableProperties: Property zookeeper.connect is overridden to
messages: org.apache.spark.streaming.dstream.InputDStream[(String, String)] = org.apache.spark.streaming.kafka.DirectKafkaInputDStream@375c2870
scala>
scala> val lines = messages.map(_._2)
lines: org.apache.spark.streaming.dstream.DStream[String] = org.apache.spark.streaming.dstream.MappedDStream@1dda179e
scala> val words = lines.flatMap(_.split(" "))
words: org.apache.spark.streaming.dstream.DStream[String] = org.apache.spark.streaming.dstream.FlatMappedDStream@996294c
scala> val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)
wordCounts: org.apache.spark.streaming.dstream.DStream[(String, Int)] = org.apache.spark.streaming.dstream.ShuffledDStream@19cd9e6a
scala> wordCounts.print()
scala> ssc.start()
scala>ssc.awaitTermination()
3、测试
在kafka生产者shell端输入：
hadoop oracle mysql mysql mysql
这是我们在kafka消费者端可以看到如下输出：
hadoop oracle mysql mysql mysql
同时在spark streaming端也可以看到如下输出：
-------------------------------------------
Time: 1500021590000 ms
-------------------------------------------
(mysql,3)
(oracle,1)
(hadoop,1)
至此，spark streaming集成kafka两种方式演示OK。但是通过上述案例我们可以发现，目前的spark streaming仅仅对每次的输入值进行一次计算，而企业应用中，可能更需要将多次的输入值进行累加，那么该怎么实现呢？看下面案例？
1、创建文件udsb.scala文件，输入如下内容：
$ cat udsb.scala
import kafka.serializer.StringDecoder
import org.apache.spark._
import org.apache.spark.streaming._
import org.apache.spark.streaming.StreamingContext._
import org.apache.spark.streaming.kafka._
val ssc = new StreamingContext(sc, Seconds(5))
ssc.checkpoint(".")
val kafkaParams = Map[String, String]("metadata.broker.list" -> "chavin.king:9092")
val topicsSet = Set("test")
val updateFunc = (values: Seq[Int], state: Option[Int]) => {
val currentCount = values.sum
val previousCount = state.getOrElse(0)
Some(currentCount + previousCount)
}
// read data
val messages = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topicsSet)
val lines = messages.map(_._2)
val words = lines.flatMap(_.split(" "))
val wordDstream = words.map(x => (x, 1))
val stateDstream = wordDstream.updateStateByKey[Int](updateFunc)
stateDstream.print()
ssc.start()
ssc.awaitTermination()
2、spark-shell local模式启动，并运行步骤1程序
bin/spark-shell --master local[2] --jars \
/opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs/spark-streaming-kafka_2.10-1.3.0.jar,/opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs/kafka_2.10-0.8.2.1.jar,/opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs/kafka-clients-0.8.2.1.jar,/opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs/zkclient-0.3.jar,/opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/externalLibs/metrics-core-2.2.0.jar
scala> :load /opt/cdh-5.3.6/spark-1.3.0-bin-2.5.0-cdh5.3.6/udsb.scala
3、测试
在kafka生产者shell端输入：
3.1）第一次输入：hadoop oracle mysql
Spark streaming端可以看到如下输出：
-------------------------------------------
Time: 1500023985000 ms
-------------------------------------------
(mysql,1)
(oracle,1)
(hadoop,1)
3.2）第二次输入：hadoop oracle mysql
Spark streaming端可以看到如下输出：
-------------------------------------------
Time: 1500023985000 ms
-------------------------------------------
(mysql,2)
(oracle,2)
(hadoop,2)
3.3）第三次输入：hadoop oracle mysql
Spark streaming端可以看到如下输出：
-------------------------------------------
Time: 1500023985000 ms
-------------------------------------------
(mysql,3)
(oracle,3)
(hadoop,3)
标签：coder target 一个 ase top 分布 direct 2.0 com
原文地址：http://www.cnblogs.com/wcwen1990/p/7899184.html
