基于Flume+Kafka+Spark-Streaming的实时流式处理完整流程

基于Flume+Kafka+Spark-Streaming的实时流式处理完整流程

1、环境准备，四台测试服务器

spark集群三台，spark1,spark2,spark3

kafka集群三台，spark1,spark2,spark3

zookeeper集群三台，spark1,spark2,spark3

日志接收服务器， spark1

日志收集服务器，redis (这台机器用来做redis开发的，现在用来做日志收集的测试，主机名就不改了)

日志收集流程：

日志收集服务器->日志接收服务器->kafka集群->spark集群处理

说明： 日志收集服务器，在实际生产中很有可能是应用系统服务器，日志接收服务器为大数据服务器中一台，日志通过网络传输到日志接收服务器，再入集群处理。

因为，生产环境中，往往网络只是单向开放给某台服务器的某个端口访问的。

Flume版本： apache-flume-1.5.0-cdh5.4.9 ，该版本已经较好地集成了对kafka的支持

2、日志收集服务器（汇总端）

配置flume动态收集特定的日志，collect.conf  配置如下：

# Name the components on this agent
a1.sources = tailsource-1
a1.sinks = remotesink
a1.channels = memoryChnanel-1

# Describe/configure the source
a1.sources.tailsource-1.type = exec
a1.sources.tailsource-1.command = tail -F /opt/modules/tmpdata/logs/1.log

a1.sources.tailsource-1.channels = memoryChnanel-1

# Describe the sink
a1.sinks.k1.type = logger

# Use a channel which buffers events in memory
a1.channels.memoryChnanel-1.type = memory
a1.channels.memoryChnanel-1.keep-alive = 10
a1.channels.memoryChnanel-1.capacity = 100000
a1.channels.memoryChnanel-1.transactionCapacity = 100000

# Bind the source and sink to the channel
a1.sinks.remotesink.type = avro
a1.sinks.remotesink.hostname = spark1
a1.sinks.remotesink.port = 666
a1.sinks.remotesink.channel = memoryChnanel-1

日志实时监控日志后，通过网络avro类型，传输到spark1服务器的666端口上

启动日志收集端脚本：

bin/flume-ng agent --conf conf --conf-file conf/collect.conf --name a1 -Dflume.root.logger=INFO,console

3、日志接收服务器

配置flume实时接收日志，collect.conf  配置如下：

#agent section  
producer.sources = s  
producer.channels = c  
producer.sinks = r  
  
#source section  
producer.sources.s.type = avro
producer.sources.s.bind = spark1
producer.sources.s.port = 666

producer.sources.s.channels = c  
  
# Each sink's type must be defined  
producer.sinks.r.type = org.apache.flume.sink.kafka.KafkaSink
producer.sinks.r.topic = mytopic
producer.sinks.r.brokerList = spark1:9092,spark2:9092,spark3:9092
producer.sinks.r.requiredAcks = 1
producer.sinks.r.batchSize = 20
producer.sinks.r.channel = c1
 
#Specify the channel the sink should use  
producer.sinks.r.channel = c  
  
# Each channel's type is defined.  
producer.channels.c.type   = org.apache.flume.channel.kafka.KafkaChannel
producer.channels.c.capacity = 10000
producer.channels.c.transactionCapacity = 1000
producer.channels.c.brokerList=spark1:9092,spark2:9092,spark3:9092
producer.channels.c.topic=channel1
producer.channels.c.zookeeperConnect=spark1:2181,spark2:2181,spark3:2181

关键是指定如源为接收网络端口的666来的数据，并输入kafka的集群，需配置好topic及zk的地址

启动接收端脚本：

bin/flume-ng agent --conf conf --conf-file conf/receive.conf --name producer -Dflume.root.logger=INFO,console

4、spark集群处理接收数据

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import kafka.serializer.StringDecoder
import scala.collection.immutable.HashMap
import org.apache.log4j.Level
import org.apache.log4j.Logger

/**
 * @author Administrator
 */
object KafkaDataTest {
  def main(args: Array[String]): Unit = {

    Logger.getLogger("org.apache.spark").setLevel(Level.WARN);
    Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.ERROR);

    val conf = new SparkConf().setAppName("stocker").setMaster("local[2]")
    val sc = new SparkContext(conf)

    val ssc = new StreamingContext(sc, Seconds(1))

    // Kafka configurations

    val topics = Set("mytopic")

    val brokers = "spark1:9092,spark2:9092,spark3:9092"

    val kafkaParams = Map[String, String]("metadata.broker.list" -> brokers, "serializer.class" -> "kafka.serializer.StringEncoder")

    // Create a direct stream
    val kafkaStream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topics)

    val urlClickLogPairsDStream = kafkaStream.flatMap(_._2.split(" ")).map((_, 1))

    val urlClickCountDaysDStream = urlClickLogPairsDStream.reduceByKeyAndWindow(
      (v1: Int, v2: Int) => {
        v1 + v2
      },
      Seconds(60),
      Seconds(5));

    urlClickCountDaysDStream.print();

    ssc.start()
    ssc.awaitTermination()
  }
}

spark-streaming接收到kafka集群后的数据，每5s计算60s内的wordcount值

5、测试结果

往日志中依次追加三次日志

spark-streaming处理结果如下：

(hive,1)
(spark,2)
(hadoop,2)
(storm,1)

---------------------------------------

(hive,1)
(spark,3)
(hadoop,3)
(storm,1)

---------------------------------------

(hive,2)
(spark,5)
(hadoop,5)
(storm,2)

与预期一样，充分体现了spark-streaming滑动窗口的特性