|NO.Z.00067|——————————|^^ 重要 ^^|——|Hadoop&Spark.V05|——|Spark.v05|Spark Streaming|与kafka整合|Redis管理Offse|

一、Redis管理的Offset

### --- 要想将Offset保存到外部存储中，关键要实现以下几个功能：

~~~     Streaming程序启动时，从外部存储获取保存的Offsets（执行一次）
~~~     在foreachRDD中，每个批次数据处理之后，更新外部存储的offsets（多次执行）

### --- 在pom.xml文件引入依赖

        <!-- jedis -->
        <dependency>
            <groupId>redis.clients</groupId>
            <artifactId>jedis</artifactId>
            <version>2.9.0</version>
        </dependency>

二、编程实现：自定义offsets：使用自定义的offsets，从kafka读数据；处理完数据后打印offsets

### --- Redis管理的Offsets：
~~~     数据结构选择：Hash；key、field、value
~~~     从 Redis 中获取保存的offsets
~~~     消费数据后将offsets保存到redis

Key：kafka:topic:TopicName:groupid
Field：partition
Value：offset

### --- 编程实现：

package cn.yanqi.kafka

import org.apache.kafka.clients.consumer.{ConsumerConfig, ConsumerRecord}
import org.apache.kafka.common.TopicPartition
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, TaskContext}
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.kafka010.{ConsumerStrategies, HasOffsetRanges, KafkaUtils, LocationStrategies, OffsetRange}
import org.apache.spark.streaming.{Seconds, StreamingContext}

object KafkaDStream2 {
  def main(args: Array[String]): Unit = {
    // 初始化
    Logger.getLogger("org").setLevel(Level.ERROR)
    val conf = new SparkConf().setAppName("FileDStream").setMaster("local[*]")
    val ssc = new StreamingContext(conf, Seconds(5))

    // 定义kafka相关参数
    val groupId: String = "mygroup01"
    val topics: Array[String] = Array("topicB")
    val kafkaParams: Map[String, Object] = getKafkaConsumerParameters(groupId)
    val offsets: Map[TopicPartition, Long] = Map(
      new TopicPartition("topicB", 0) -> 1000,
      new TopicPartition("topicB", 1) -> 1000,
      new TopicPartition("topicB", 2) -> 1000
    )

    // 创建DStream
    val dstream: InputDStream[ConsumerRecord[String, String]] = KafkaUtils.createDirectStream(
      ssc,
      LocationStrategies.PreferConsistent,
      ConsumerStrategies.Subscribe[String, String](topics, kafkaParams, offsets)
    )

    // DStream转换&输出
    dstream.foreachRDD{ (rdd, time) =>
      // 处理消息
      println(s"*********** rdd.count = ${rdd.count()}; time = $time *************")

      // 显示offsets的信息
      val offsetRanges: Array[OffsetRange] = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
      rdd.foreachPartition{ iter =>
        val range: OffsetRange = offsetRanges(TaskContext.get.partitionId)
        println(s"${range.topic} ${range.partition}  ${range.fromOffset}  ${range.untilOffset} ")
      }
    }

    // 启动作业
    ssc.start()
    ssc.awaitTermination()
  }

  def getKafkaConsumerParameters(groupid: String): Map[String, Object] = {
    Map[String, Object](
      ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG -> "hadoop01:9092,hadoop02:9092,hadoop03:9092",
      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)
//      ConsumerConfig.AUTO_OFFSET_RESET_CONFIG -> "earliest"
    )
  }
}

### --- 编译打印

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rmat\2.4.0\parquet-format-2.4.0.jar;C:\Users\Administrator\.m2\repository\org\apache\parquet\parquet-jackson\1.10.1\parquet-jackson-1.10.1.jar;C:\Users\Administrator\.m2\repository\org\apache\arrow\arrow-vector\0.10.0\arrow-vector-0.10.0.jar;C:\Users\Administrator\.m2\repository\org\apache\arrow\arrow-format\0.10.0\arrow-format-0.10.0.jar;C:\Users\Administrator\.m2\repository\org\apache\arrow\arrow-memory\0.10.0\arrow-memory-0.10.0.jar;C:\Users\Administrator\.m2\repository\com\carrotsearch\hppc\0.7.2\hppc-0.7.2.jar;C:\Users\Administrator\.m2\repository\com\vlkan\flatbuffers\1.2.0-3f79e055\flatbuffers-1.2.0-3f79e055.jar;C:\Users\Administrator\.m2\repository\joda-time\joda-time\2.9.7\joda-time-2.9.7.jar;C:\Users\Administrator\.m2\repository\mysql\mysql-connector-java\5.1.44\mysql-connector-java-5.1.44.jar;C:\Users\Administrator\.m2\repository\org\apache\spark\spark-hive_2.12\2.4.5\spark-hive_2.12-2.4.5.jar;C:\Users\Administrator\.m2\repository\com\twitter\parquet-hadoop-bundle\1.6.0\parquet-hadoop-bundle-1.6.0.jar;C:\Users\Administrator\.m2\repository\org\spark-project\hive\hive-exec\1.2.1.spark2\hive-exec-1.2.1.spark2.jar;C:\Users\Administrator\.m2\repository\commons-io\commons-io\2.4\commons-io-2.4.jar;C:\Users\Administrator\.m2\repository\javolution\javolution\5.5.1\javolution-5.5.1.jar;C:\Users\Administrator\.m2\repository\log4j\apache-log4j-extras\1.2.17\apache-log4j-extras-1.2.17.jar;C:\Users\Administrator\.m2\repository\org\antlr\antlr-runtime\3.4\antlr-runtime-3.4.jar;C:\Users\Administrator\.m2\repository\org\antlr\stringtemplate\3.2.1\stringtemplate-3.2.1.jar;C:\Users\Administrator\.m2\repository\antlr\antlr\2.7.7\antlr-2.7.7.jar;C:\Users\Administrator\.m2\repository\org\antlr\ST4\4.0.4\ST4-4.0.4.jar;C:\Users\Administrator\.m2\repository\com\googlecode\javaewah\JavaEWAH\0.3.2\JavaEWAH-0.3.2.jar;C:\Users\Administrator\.m2\repository\org\iq80\snappy\snappy\0.2\snappy-0.2.jar;C:\Users\Administrator\.m2\repository\stax\stax-api\1.0.1\stax-api-1.0.1.jar;C:\Users\Administrator\.m2\repository\net\sf\opencsv\opencsv\2.3\opencsv-2.3.jar;C:\Users\Administrator\.m2\repository\org\spark-project\hive\hive-metastore\1.2.1.spark2\hive-metastore-1.2.1.spark2.jar;C:\Users\Administrator\.m2\repository\com\jolbox\bonecp\0.8.0.RELEASE\bonecp-0.8.0.RELEASE.jar;C:\Users\Administrator\.m2\repository\commons-cli\commons-cli\1.2\commons-cli-1.2.jar;C:\Users\Administrator\.m2\repository\commons-logging\commons-logging\1.1.3\commons-logging-1.1.3.jar;C:\Users\Administrator\.m2\repository\org\datanucleus\datanucleus-api-jdo\3.2.6\datanucleus-api-jdo-3.2.6.jar;C:\Users\Administrator\.m2\repository\org\datanucleus\datanucleus-rdbms\3.2.9\datanucleus-rdbms-3.2.9.jar;C:\Users\Administrator\.m2\repository\commons-pool\commons-pool\1.5.4\commons-pool-1.5.4.jar;C:\Users\Administrator\.m2\repository\commons-dbcp\commons-dbcp\1.4\commons-dbcp-1.4.jar;C:\Users\Administrator\.m2\repository\javax\jdo\jdo-api\3.0.1\jdo-api-3.0.1.jar;C:\Users\Administrator\.m2\repository\javax\transaction\jta\1.1\jta-1.1.jar;C:\Users\Administrator\.m2\repository\commons-httpclient\commons-httpclient\3.1\commons-httpclient-3.1.jar;C:\Users\Administrator\.m2\repository\org\apache\calcite\calcite-avatica\1.2.0-incubating\calcite-avatica-1.2.0-incubating.jar;C:\Users\Administrator\.m2\repository\org\apache\calcite\calcite-core\1.2.0-incubating\calcite-core-1.2.0-incubating.jar;C:\Users\Administrator\.m2\repository\org\apache\calcite\calcite-linq4j\1.2.0-incubating\calcite-linq4j-1.2.0-incubating.jar;C:\Users\Administrator\.m2\repository\net\hydromatic\eigenbase-properties\1.1.5\eigenbase-properties-1.1.5.jar;C:\Users\Administrator\.m2\repository\org\apache\httpcomponents\httpclient\4.5.6\httpclient-4.5.6.jar;C:\Users\Administrator\.m2\repository\org\apache\httpcomponents\httpcore\4.4.10\httpcore-4.4.10.jar;C:\Users\Administrator\.m2\repository\org\codehaus\jackson\jackson-mapper-asl\1.9.13\jackson-mapper-asl-1.9.13.jar;C:\Users\Administrator\.m2\repository\commons-codec\commons-codec\1.10\commons-codec-1.10.jar;C:\Users\Administrator\.m2\repository\org\jodd\jodd-core\3.5.2\jodd-core-3.5.2.jar;C:\Users\Administrator\.m2\repository\org\datanucleus\datanucleus-core\3.2.10\datanucleus-core-3.2.10.jar;C:\Users\Administrator\.m2\repository\org\apache\thrift\libthrift\0.9.3\libthrift-0.9.3.jar;C:\Users\Administrator\.m2\repository\org\apache\thrift\libfb303\0.9.3\libfb303-0.9.3.jar;C:\Users\Administrator\.m2\repository\org\apache\derby\derby\10.12.1.1\derby-10.12.1.1.jar;C:\Users\Administrator\.m2\repository\org\apache\spark\spark-streaming_2.12\2.4.5\spark-streaming_2.12-2.4.5.jar;C:\Users\Administrator\.m2\repository\org\apache\spark\spark-streaming-kafka-0-10_2.12\2.4.5\spark-streaming-kafka-0-10_2.12-2.4.5.jar;C:\Users\Administrator\.m2\repository\org\apache\kafka\kafka-clients\2.0.0\kafka-clients-2.0.0.jar;C:\Users\Administrator\.m2\repository\redis\clients\jedis\2.9.0\jedis-2.9.0.jar;C:\Users\Administrator\.m2\repository\org\apache\commons\commons-pool2\2.4.2\commons-pool2-2.4.2.jar cn.yanqi.kafka.KafkaDStream3
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties

三、编程实现redis工具类

### --- 工具类（Redis读取/保存offsets）

package cn.yanqi.kafka

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 = "hadoop03"
  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._

      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._
        val maps: util.Map[String, String] = buffer.map(_._2).toMap.asJava

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

    jedis.close()
  }

  def main(args: Array[String]): Unit = {
    val topics = Array("topicB", "topicC")
    val groupid = "group01"
    val x: Map[TopicPartition, Long] = getOffsetsFromRedis(topics, groupid)
    x.foreach(println)
  }
}

// hmset kafka:topic:topicB:group01 0 0 1 100 2 200
// hgetall kafka:topic:topicB:group01

//    val groupId: String = "group01"
//    val topics: Array[String] = Array("topicB")
//    val offsets: Map[TopicPartition, Long] = getOffsetsFromRedis(topics, groupId)
//    offsets.foreach(println)

//    val groupid = "group01"
//    val topic = "topicB"
//    val offsets = Array(
//      OffsetRange(topic, 0,   0,  600),
//      OffsetRange(topic, 1, 100, 1000),
//      OffsetRange(topic, 2, 200, 2000)
//    )
//    saveOffsetsToRedis(offsets, groupid)

// ./redis-server /opt/yanqi/software/redis-5.0.5/redis.conf

### --- 在redis下准备数据

[root@hadoop03 ~]# cd /opt/yanqi/servers/redis/bin/
[root@hadoop03 bin]#  ./redis-cli -h 127.0.0.1 -p 6379
127.0.0.1:6379> 

~~~     # 在redis下准备数据

127.0.0.1:6379> hmset kafka:topic:topicB:group01 0 0 1 100 2 200
OK
127.0.0.1:6379> hgetall kafka:topic:topicB:group01
1) "0"
2) "0"
3) "1"
4) "100"
5) "2"
6) "200"

### --- 编译打印

D:\JAVA\jdk1.8.0_231\bin\java.exe "-javaagent:D:\IntelliJIDEA\IntelliJ IDEA 2019.3.3\lib\idea_rt.jar=50491:D:\IntelliJIDEA\IntelliJ IDEA 2019.3.3\bin" -Dfile.encoding=UTF-8 -classpath 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mons-pool2-2.4.2.jar cn.yanqi.kafka.OffsetsWithRedisUtils

四、编程实现：key从Redis 获取offsets根据该offsets从kafka读数据处理完数据后将offsets保存到 Redis

### --- 根据key从redis获取offsets
~~~     # 数据结构选择：Hash；key、field、value
~~~     # 从 Redis 中获取保存的offsets
~~~     # 消费数据后将offsets保存到redis

Key：kafka:topic:TopicName:groupid
Field：partition
Value：offset

### --- 编程实现：主程序（从kafka获取数据，使用 Redis 保存offsets）

package cn.yanqi.kafka

import org.apache.kafka.clients.consumer.{ConsumerConfig, ConsumerRecord}
import org.apache.kafka.common.TopicPartition
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.log4j.{Level, Logger}
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.kafka010._
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.{SparkConf, TaskContext}

object KafkaDStream3 {
  def main(args: Array[String]): Unit = {
    // 初始化
    Logger.getLogger("org").setLevel(Level.ERROR)
    val conf = new SparkConf().setAppName(this.getClass.getCanonicalName).setMaster("local[*]")
    val ssc = new StreamingContext(conf, Seconds(5))

    // 定义kafka相关参数
    val groupId: String = "yanqi_group01"
    val topics: Array[String] = Array("yanqi_topic01", "yanqi_topic02")
    val kafkaParams: Map[String, Object] = getKafkaConsumerParameters(groupId)

    // 从 Redis中获取offsets
    val offsets: Map[TopicPartition, Long] = OffsetsWithRedisUtils.getOffsetsFromRedis(topics, groupId)
    offsets.foreach(println)

    // 创建DStream
    val dstream: InputDStream[ConsumerRecord[String, String]] = KafkaUtils.createDirectStream(
      ssc,
      LocationStrategies.PreferConsistent,
      ConsumerStrategies.Subscribe[String, String](topics, kafkaParams, offsets)
    )

    // DStream转换&输出
    dstream.foreachRDD{ (rdd, time) =>
      if (! rdd.isEmpty()) {
        // 处理消息
        println(s"*********** rdd.count = ${rdd.count()}; time = $time *************")

        // 将offsets信息打印到控制台
        val offsetRanges: Array[OffsetRange] = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
        rdd.foreachPartition{ iter =>
          val range: OffsetRange = offsetRanges(TaskContext.get.partitionId)
          println(s"${range.topic} ${range.partition}  ${range.fromOffset}  ${range.untilOffset} ")
        }

        // 将offsets保存到Redis
        OffsetsWithRedisUtils.saveOffsetsToRedis(offsetRanges, groupId)
      }
    }

    // 启动作业
    ssc.start()
    ssc.awaitTermination()
  }

  def getKafkaConsumerParameters(groupid: String): Map[String, Object] = {
    Map[String, Object](
      ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG -> "hadoop01:9092,hadoop02:9092,hadoop03:9092",
      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)
//      ConsumerConfig.AUTO_OFFSET_RESET_CONFIG -> "earliest"
    )
  }
}

// kafka-topics.sh --zookeeper hadoop01:2181,hadoop02:2181,hadoop03:2181 --create --topic yanqi_topic01 --replication-factor 2 --partitions 3
// kafka-topics.sh --zookeeper hadoop01:2181,hadoop02:2181,hadoop03:2181 --create --topic yanqi_topic02 --replication-factor 2 --partitions 2

### --- 编译打印

D:\JAVA\jdk1.8.0_231\bin\java.exe "-javaagent:D:\IntelliJIDEA\IntelliJ IDEA 2019.3.3\lib\idea_rt.jar=50491:D:\IntelliJIDEA\IntelliJ IDEA 2019.3.3\bin" -Dfile.encoding=UTF-8 -classpath 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cn.yanqi.kafka.OffsetsWithRedisUtils

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Walter Savage Landor:strove with none,for none was worth my strife.Nature I loved and, next to Nature, Art:I warm'd both hands before the fire of life.It sinks, and I am ready to depart

                                                                                                                                                   ——W.S.Landor

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