高级 API

package com.atguigu.streaming.kafka

import kafka.serializer.StringDecoder
import org.apache.kafka.clients.consumer.ConsumerConfig
import org.apache.spark.SparkConf
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}

object HighKafka2 {

    def createSSC(): StreamingContext = {
        val conf: SparkConf = new SparkConf().setMaster("local[*]").setAppName("HighKafka")
        val ssc = new StreamingContext(conf, Seconds(3))
        // 偏移量保存在 checkpoint 中, 可以从上次的位置接着消费
        ssc.checkpoint("./ck1")
        // kafka 参数
        //kafka参数声明
        val brokers = "hadoop201:9092,hadoop202:9092,hadoop203:9092"
        val topic = "first"
        val group = "bigdata"
        val deserialization = "org.apache.kafka.common.serialization.StringDeserializer"
        val kafkaParams = Map(
            "zookeeper.connect" -> "hadoop201:2181,hadoop202:2181,hadoop203:2181",
            ConsumerConfig.GROUP_ID_CONFIG -> group,
            ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG -> brokers,
            ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG -> deserialization,
            ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG -> deserialization
        )
        val dStream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](
            ssc, kafkaParams, Set(topic))

        dStream.print()
        ssc
    }

    def main(args: Array[String]): Unit = {

        val ssc: StreamingContext = StreamingContext.getActiveOrCreate("./ck1", () => createSSC())
        ssc.start()
        ssc.awaitTermination()
    }
}