spark作业12

将sample.log的数据发送到Kafka中，经过Spark Streaming处理，将数据格式变为以下形式：

commandid | houseid | gathertime | srcip | destip |srcport| destport | domainname | proxytype | proxyip | proxytype | title | content | url | logid



在发送到kafka的另一个队列中

要求：

1、sample.log => 读文件，将数据发送到kafka队列中

2、从kafka队列中获取数据（0.10 接口不管理offset），变更数据格式

3、处理后的数据在发送到kafka另一个队列中

分析
1 使用课程中的redis工具类管理offset
2 读取日志数据发送数据到topic1
3 消费主题，将数据的分割方式修改为竖线分割，再次发送到topic2

package home.one

import java.util

import org.apache.kafka.common.TopicPartition
import org.apache.spark.streaming.kafka010.OffsetRange
import redis.clients.jedis.{Jedis, JedisPool, JedisPoolConfig}

import scala.collection.mutable

object OffsetsWithRedisUtils {
  // 定义Redis参数
  private val redisHost = "linux123"
  private val redisPort = 6379

  // 获取Redis的连接
  private val config = new JedisPoolConfig
  // 最大空闲数
  config.setMaxIdle(5)
  // 最大连接数
  config.setMaxTotal(10)

  private val pool = new JedisPool(config, redisHost, redisPort, 10000)
  private def getRedisConnection: Jedis = pool.getResource

  private val topicPrefix = "kafka:topic"

  // Key：kafka:topic:TopicName:groupid
  private def getKey(topic: String, groupid: String) = s"$topicPrefix:$topic:$groupid"

  // 根据 key 获取offsets
  def getOffsetsFromRedis(topics: Array[String], groupId: String): Map[TopicPartition, Long] = {
    val jedis: Jedis = getRedisConnection

    val offsets: Array[mutable.Map[TopicPartition, Long]] = topics.map { topic =>
      val key = getKey(topic, groupId)

      import scala.collection.JavaConverters._

      // 将获取到的redis数据由Java的map转换为scala的map，数据格式为{key：[{partition,offset}]}
      jedis.hgetAll(key)
        .asScala
        .map { case (partition, offset) => new TopicPartition(topic, partition.toInt) -> offset.toLong }
    }

    // 归还资源
    jedis.close()
    offsets.flatten.toMap
  }

  // 将offsets保存到Redis中
  def saveOffsetsToRedis(offsets: Array[OffsetRange], groupId: String): Unit = {
    // 获取连接
    val jedis: Jedis = getRedisConnection

    // 组织数据
    offsets.map{range => (range.topic, (range.partition.toString, range.untilOffset.toString))}
      .groupBy(_._1)
      .foreach{case (topic, buffer) =>
        val key: String = getKey(topic, groupId)

        import scala.collection.JavaConverters._
        // 同样将scala的map转换为Java的map存入redis中
        val maps: util.Map[String, String] = buffer.map(_._2).toMap.asJava

        // 保存数据
        jedis.hmset(key, maps)
      }

    jedis.close()
  }
}

package home.one

import java.util.Properties

import org.apache.kafka.clients.producer.{KafkaProducer, ProducerConfig, ProducerRecord}
import org.apache.kafka.common.serialization.StringSerializer
import org.apache.log4j.{Level, Logger}
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}

object KafkaProducer {

  def main(args: Array[String]): Unit = {
    Logger.getLogger("org").setLevel(Level.ERROR)
    val conf = new SparkConf().setAppName(this.getClass.getCanonicalName.init).setMaster("local[*]")
    val sc = new SparkContext(conf)

    // 读取sample.log文件数据
    val lines: RDD[String] = sc.textFile("data/sample.log")

    // 定义 kafka producer参数
    val prop = new Properties()
    // kafka的访问地址
    prop.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "linux121:9092")
    // key和value的序列化方式
    prop.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, classOf[StringSerializer])
    prop.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, classOf[StringSerializer])

    // 将读取到的数据发送到mytopic1
    lines.foreachPartition{iter =>
      // 初始化KafkaProducer
      val producer = new KafkaProducer[String, String](prop)
      iter.foreach{line =>
        // 封装数据
        val record = new ProducerRecord[String, String]("mytopic1", line)
        // 发送数据
        producer.send(record)
      }
      producer.close()
    }
  }
}

package home.one


import java.util.Properties
import org.apache.kafka.clients.consumer.{ConsumerConfig, ConsumerRecord}
import org.apache.kafka.clients.producer.{KafkaProducer, ProducerConfig, ProducerRecord}
import org.apache.kafka.common.serialization.{StringDeserializer, StringSerializer}
import org.apache.log4j.{Level, Logger}
import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.kafka010._
import org.apache.spark.streaming.{Seconds, StreamingContext}

object HomeOne {
  val log = Logger.getLogger(this.getClass)

  def main(args: Array[String]): Unit = {
    Logger.getLogger("org").setLevel(Level.ERROR)
    val conf = new SparkConf().setAppName(this.getClass.getCanonicalName).setMaster("local[*]")
    val ssc = new StreamingContext(conf, Seconds(5))

    // 需要消费的topic
    val topics: Array[String] = Array("mytopic1")
    val groupid = "mygroup1"
    // 定义kafka相关参数
    val kafkaParams: Map[String, Object] = getKafkaConsumerParameters(groupid)
    // 从Redis获取offset
    val fromOffsets = OffsetsWithRedisUtils.getOffsetsFromRedis(topics, groupid)

    // 创建DStream
    val dstream: InputDStream[ConsumerRecord[String, String]] = KafkaUtils.createDirectStream(
      ssc,
      LocationStrategies.PreferConsistent,
      // 从kafka中读取数据
      ConsumerStrategies.Subscribe[String, String](topics, kafkaParams, fromOffsets)
    )

    // 转换后的数据发送到另一个topic
    dstream.foreachRDD { rdd =>
      if (!rdd.isEmpty) {
        // 获取消费偏移量
        val offsetRanges: Array[OffsetRange] = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
        // 处理数据发送到topic2
        rdd.foreachPartition(process)
        // 将offset保存到Redis
        OffsetsWithRedisUtils.saveOffsetsToRedis(offsetRanges, groupid)
      }
    }

    // 启动作业
    ssc.start()
    // 持续执行
    ssc.awaitTermination()
  }

  // 将处理后的数据发送到topic2
  def process(iter: Iterator[ConsumerRecord[String, String]]) = {
    iter.map(line => parse(line.value))
      .filter(!_.isEmpty)
      .foreach(line => sendMsg2Topic(line, "mytopic2"))
  }

  // 调用kafka生产者发送消息
  def sendMsg2Topic(msg: String, topic: String): Unit = {
    val producer = new KafkaProducer[String, String](getKafkaProducerParameters())
    val record = new ProducerRecord[String, String](topic, msg)
    producer.send(record)
  }

  // 修改数据格式，将逗号分隔变成竖线分割
  def parse(text: String): String = {
    try {
      val arr = text.replace("<<<!>>>", "").split(",")
      if (arr.length != 15) return ""
      arr.mkString("|")
    } catch {
      case e: Exception =>
        log.error("解析数据出错！", e)
        ""
    }
  }

  // 定义kafka消费者的配置信息
  def getKafkaConsumerParameters(groupid: String): Map[String, Object] = {
    Map[String, Object](
      ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG -> "linux121:9092",
      ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG -> classOf[StringDeserializer],
      ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG -> classOf[StringDeserializer],
      ConsumerConfig.GROUP_ID_CONFIG -> groupid,
      ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG -> (false: java.lang.Boolean),
    )
  }

  // 定义生产者的kafka配置
  def getKafkaProducerParameters(): Properties = {
    val prop = new Properties()
    prop.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "linux121:9092")
    prop.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, classOf[StringSerializer])
    prop.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, classOf[StringSerializer])
    prop
  }

}

/*


假设机场的数据如下：

1, "SFO"

2, "ORD"

3, "DFW"

机场两两之间的航线及距离如下：

1, 2,1800

2, 3, 800

3, 1, 1400

用 GraphX 完成以下需求：

求所有的顶点
求所有的边
求所有的triplets
求顶点数
求边数
求机场距离大于1000的有几个，有哪些
按所有机场之间的距离排序（降序），输出结果
 */

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.graphx.{Edge, Graph, VertexId}
import org.apache.spark.rdd.RDD

object TwoHome {

  def main(args: Array[String]): Unit = {
    // 初始化
    val conf = new SparkConf().setAppName(this.getClass.getCanonicalName.init).setMaster("local[*]")
    val sc = new SparkContext(conf)
    sc.setLogLevel("warn")

    //初始化数据
    val vertexArray: Array[(Long, String)] = Array((1L, "SFO"), (2L, "ORD"), (3L, "DFW"))
    val edgeArray: Array[Edge[Int]] = Array(
      Edge(1L, 2L, 1800),
      Edge(2L, 3L, 800),
      Edge(3L, 1L, 1400)
    )

    //构造vertexRDD和edgeRDD
    val vertexRDD: RDD[(VertexId, String)] = sc.makeRDD(vertexArray)
    val edgeRDD: RDD[Edge[Int]] = sc.makeRDD(edgeArray)

    //构造图
    val graph: Graph[String, Int] = Graph(vertexRDD, edgeRDD)

    //所有的顶点
    println("所有顶点：")
    graph.vertices.foreach(println)

    //所有的边
    println("所有边：")
    graph.edges.foreach(println)

    //所有的triplets
    println("所有三元组信息：")
    graph.triplets.foreach(println)

    //求顶点数
    val vertexCnt = graph.vertices.count()
    println(s"总顶点数：$vertexCnt")

    //求边数
    val edgeCnt = graph.edges.count()
    println(s"总边数：$edgeCnt")

    //机场距离大于1000的
    println("机场距离大于1000的边信息：")
    graph.edges.filter(_.attr > 1000).foreach(println)

    //按所有机场之间的距离排序（降序）
    println("降序排列所有机场之间距离")
    graph.edges.sortBy(-_.attr).collect().foreach(println)
  }
}