spark streaming接收flume数据代码实现
时间: 2023-11-07 17:03:21 浏览: 100
下面是使用Spark Streaming接收Flume数据的代码实现:
1. 首先,需要在pom.xml文件中添加以下依赖:
```xml
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-flume_2.11</artifactId>
<version>2.4.4</version>
</dependency>
```
2. 接着,在Spark Streaming应用程序中,创建一个Flume事件流,并将其与对应的Flume Agent进行连接:
```scala
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.flume._
val ssc = new StreamingContext(sparkConf, Seconds(1))
val flumeStream = FlumeUtils.createStream(ssc, hostname, port)
```
其中,hostname和port分别是Flume Agent的主机名和端口号。
3. 使用DStream的transform方法对接收到的数据进行处理:
```scala
val events = flumeStream.map(event => new String(event.event.getBody.array()))
val words = events.flatMap(_.split(" "))
val wordCounts = words.map(word => (word, 1)).reduceByKey(_ + _)
```
4. 最后,使用DStream的print方法输出结果:
```scala
wordCounts.print()
```
完整代码示例:
```scala
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.flume._
val ssc = new StreamingContext(sparkConf, Seconds(1))
val flumeStream = FlumeUtils.createStream(ssc, hostname, port)
val events = flumeStream.map(event => new String(event.event.getBody.array()))
val words = events.flatMap(_.split(" "))
val wordCounts = words.map(word => (word, 1)).reduceByKey(_ + _)
wordCounts.print()
ssc.start()
ssc.awaitTermination()
```
注意:在实际应用中,需要根据实际情况设置合适的批处理间隔时间和Flume Agent的配置。
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