sparkstreaming集成kafka的方式
时间: 2023-06-17 11:05:57 浏览: 106
在Spark Streaming中集成Kafka通常有两种方式,一种是直接使用KafkaUtils.createDirectStream()方法,另一种是使用Kafka的Receiver API。下面分别介绍这两种方式的实现。
1. 直接使用KafkaUtils.createDirectStream()方法
```scala
import org.apache.spark.streaming.kafka._
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.SparkConf
val sparkConf = new SparkConf().setAppName("KafkaDirectStream")
val ssc = new StreamingContext(sparkConf, Seconds(5))
// Kafka相关配置
val kafkaParams = Map[String, Object](
"bootstrap.servers" -> "localhost:9092",
"key.deserializer" -> classOf[StringDeserializer],
"value.deserializer" -> classOf[StringDeserializer],
"group.id" -> "test",
"auto.offset.reset" -> "latest",
"enable.auto.commit" -> (false: java.lang.Boolean)
)
// 订阅的Topic
val topics = Array("test_topic")
// 创建Direct Stream
val stream = KafkaUtils.createDirectStream[String, String](
ssc,
PreferConsistent,
Subscribe[String, String](topics, kafkaParams)
)
// 对接收到的数据进行处理
stream.map(record => (record.key, record.value)).print()
ssc.start()
ssc.awaitTermination()
```
2. 使用Kafka的Receiver API
```scala
import org.apache.spark.streaming.kafka._
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.SparkConf
val sparkConf = new SparkConf().setAppName("KafkaReceiverStream")
val ssc = new StreamingContext(sparkConf, Seconds(5))
// Kafka相关配置
val kafkaParams = Map[String, String](
"zookeeper.connect" -> "localhost:2181",
"group.id" -> "test",
"auto.offset.reset" -> "smallest"
)
// 订阅的Topic
val topics = Map("test_topic" -> 1)
// 创建Kafka Stream
val stream = KafkaUtils.createStream[String, String, StringDecoder, StringDecoder](
ssc,
kafkaParams,
topics,
StorageLevel.MEMORY_AND_DISK_SER_2
)
// 对接收到的数据进行处理
stream.map(_._2).print()
ssc.start()
ssc.awaitTermination()
```
需要注意的是,使用Kafka的Receiver API需要依赖Zookeeper,而且在使用过程中可能会出现一些性能问题,因此建议使用直接使用KafkaUtils.createDirectStream()方法。
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