一.准备flume配置
a1.sources = r1
a1.sinks = k1
a1.channels = c1
a1.sources.r1.type = spooldir
a1.sources.r1.channels = c1
a1.sources.r1.spoolDir = /var/log/test
a1.sources.r1.fileHeader = true
a1.channels.c1.type = memory
a1.channels.c1.capacity = 10000
a1.channels.c1.transactionCapacity = 10000
a1.channels.c1.byteCapacityBufferPercentage = 20
a1.channels.c1.byteCapacity = 800000
a1.sinks.k1.type = org.apache.flume.sink.kafka.KafkaSink
a1.sinks.k1.topic = spark
a1.sinks.k1.brokerList = master1:9092,master2:9092,slave3:9092
a1.sinks.k1.requiredAcks = 1
a1.sinks.k1.batchSize = 20
a1.sinks.k1.channel = c1
# Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
二,spark代码
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.{SparkConf, SparkContext}
object SparkStreamDemo {
def main(args: Array[String]) {
val conf = new SparkConf()
conf.setAppName("spark_streaming")
conf.setMaster("local[2]")
val sc = new SparkContext(conf)
sc.setCheckpointDir("D://checkpoints")
sc.setLogLevel("ERROR")
val ssc = new StreamingContext(sc, Seconds(5))
val topics = Map("spark" -> 2)
val lines = KafkaUtils.createStream(ssc, "master2:2181,slave2:2181,slave4:2181", "spark", topics).map(_._2)
val ds1 = lines.flatMap(_.split(" ")).map((_, 1))
val ds2 = ds1.updateStateByKey[Int]((x:Seq[Int], y:Option[Int]) => {
Some(x.sum + y.getOrElse(0))
})
ds2.print()
ssc.start()
ssc.awaitTermination()
}
}
三,注意的事项
1.kafka的topic是自动创建的,如果启动了配置没有的话,会建一个新的
2.记得flume读取文件夹是有权限的chown -R flume:flume /var/log/test
3.echo "my my last test test test" > logs5
4.sc.setCheckpointDir("D://checkpoints")这里的文件路径
Flume+Kafka+Spark Steaming demo
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转载自lakerhu.iteye.com/blog/2400377
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