kafka通过spark-streaming往hbase里写数据

淡淡的烟草味﹌ 2022-03-02 16:30 191阅读 0赞

1.启动kafka：

\]\# ./bin/kafka-server-start.sh config/server.properties

2 查看topic list

./bin/kafka-topics.sh --list --zookeeper localhost:2181  
3.streaming代码：

package com.badou.streaming
    
    import org.apache.hadoop.hbase._
    import org.apache.hadoop.hbase.client._
    import org.apache.spark.SparkConf
    import org.apache.spark.streaming.{Seconds, StreamingContext}
    import org.apache.spark.streaming.kafka.KafkaUtils
    
    object HbaseHandler {
      def insert(row: String, column: String, value: String) {
        // Hbase配置
        val tableName = "sparkstream_kafkahbase_table" // 定义表名
        val hbaseConf = HBaseConfiguration.create()
        hbaseConf.set("hbase.zookeeper.quorum", "master,slave1,slave2")
        hbaseConf.set("hbase.zookeeper.property.clientPort", "2181")
        hbaseConf.set("hbase.defaults.for.version.skip", "true")
        val hTable = new HTable(hbaseConf, tableName)
        val thePut = new Put(row.getBytes)
        thePut.add("info".getBytes,column.getBytes,value.getBytes)
        hTable.setAutoFlush(false, false)
        // 写入数据缓存
        hTable.setWriteBufferSize(3*1024*1024)
        hTable.put(thePut)
        // 提交
        hTable.flushCommits()
      }
    }
    
    object kafkaStreamHbase {
      def main(args: Array[String]) {
    
        val zkQuorum = "master:2181,slave1:2181,slave2:2181"
        val group = "group_1"
        val topics = "topic_1013"
        val numThreads = 1
        var output="hdfs://master:9000/stream_out/spark-log.txt"
    
        val sparkConf = new SparkConf().setAppName("kafkaStreamHbase").setMaster("local[2]")
        val ssc =  new StreamingContext(sparkConf, Seconds(10))
        ssc.checkpoint("hdfs://master:9000/hdfs_checkpoint")
    
        val topicpMap = topics.split(",").map((_,numThreads.toInt)).toMap
        val lines = KafkaUtils.createStream(ssc, zkQuorum, group, topicpMap).map(_._2)
        // lines.print()
        // lines.saveAsTextFiles("hdfs://master:9000/stream_out/sparkstreaming_hbasetest.log")
        val line = lines.flatMap(_.split("\n"))
        val words = line.map(_.split("\\|"))
        words.foreachRDD(rdd => {
          rdd.foreachPartition(partitionOfRecords => {
            partitionOfRecords.foreach(pair => {
              val key = pair(0)
              val col = pair(1)
              val value = pair(2)
              println(key + "_" + col + " : " + value)
              HbaseHandler.insert(key, col, value)
            })
          })
        })
        ssc.start()
        ssc.awaitTermination()
      }
    }

5.启动hbase

![20190320215817642.png][]

![20190320215827408.png][]

![20190320220008709.png][]

创建表

![20190320220111564.png][]

4.测试

启动producer

./bin/kafka-console-producer.sh --broker-list localhost:9092 --topic topic\_0309

![20190320220929599.png][]

idea运行代码

结果

![20190320221145904.png][]

[20190320215817642.png]: /images/20220302/33208b9d928c4b0f9ad6d0d3eefac5f7.png
[20190320215827408.png]: /images/20220302/275f70ca282d4cc0af3b9275a5594611.png
[20190320220008709.png]: /images/20220302/9615b74df8aa4ec08ede6cd6ddb6b3d1.png
[20190320220111564.png]: /images/20220302/65fd6d6cac814e19a3d9640906d2dab3.png
[20190320220929599.png]: /images/20220302/ac42724573314699a6a1ccb825fef2eb.png
[20190320221145904.png]: /images/20220302/a57810b7ab8c470e867a16e024ce67c0.png