SparkStreaming+kafka保存offset的偏移量到mysql案例

1. MySQL创建存储offset的表格

mysql> use test
mysql> create table hlw_offset(
        topic varchar(32),
        groupid varchar(50),
        partitions int,
        fromoffset bigint,
        untiloffset bigint,
        primary key(topic,groupid,partitions)
        );

2.Maven依赖包

2.11.8
2.3.1
2.5.0
--------------------------------------------------

    org.scala-lang
    scala-library
    ${scala.version}


    org.apache.spark
    spark-core_2.11
    ${spark.version}


    org.apache.spark
    spark-sql_2.11
    ${spark.version}


    org.apache.spark
    spark-streaming_2.11
    ${spark.version}


    org.apache.spark
    spark-streaming-kafka-0-8_2.11
    ${spark.version}


    mysql
    mysql-connector-java
    5.1.27



    org.scalikejdbc
    scalikejdbc_2.11
    2.5.0


    org.scalikejdbc
    scalikejdbc-config_2.11
    2.5.0


    com.typesafe
    config
    1.3.0


    org.apache.commons
    commons-lang3
    3.5

实现思路

1）StreamingContext
2）从kafka中获取数据(从外部存储获取offset-->根据offset获取kafka中的数据)
3）根据业务进行逻辑处理
4）将处理结果存到外部存储中--保存offset
5）启动程序，等待程序结束

代码实现

1：SparkStreaming主体代码如下

import kafka.common.TopicAndPartition
import kafka.message.MessageAndMetadata
import kafka.serializer.StringDecoder
import org.apache.spark.SparkConf
import org.apache.spark.streaming.kafka.{HasOffsetRanges, KafkaUtils}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import scalikejdbc._
import scalikejdbc.config._
object JDBCOffsetApp {
  def main(args: Array[String]): Unit = {
    //创建SparkStreaming入口
    val conf = new SparkConf().setMaster("local[2]").setAppName("JDBCOffsetApp")
    val ssc = new StreamingContext(conf,Seconds(5))
    //kafka消费主题
    val topics = ValueUtils.getStringValue("kafka.topics").split(",").toSet
    //kafka参数
    //这里应用了自定义的ValueUtils工具类，来获取application.conf里的参数，方便后期修改
    val kafkaParams = Map[String,String](
      "metadata.broker.list"->ValueUtils.getStringValue("metadata.broker.list"),
      "auto.offset.reset"->ValueUtils.getStringValue("auto.offset.reset"),
      "group.id"->ValueUtils.getStringValue("group.id")
    )
    //先使用scalikejdbc从MySQL数据库中读取offset信息
    //+------------+------------------+------------+------------+-------------+
    //| topic      | groupid          | partitions | fromoffset | untiloffset |
    //+------------+------------------+------------+------------+-------------+
    //MySQL表结构如上，将“topic”，“partitions”，“untiloffset”列读取出来
    //组成 fromOffsets: Map[TopicAndPartition, Long]，后面createDirectStream用到
    DBs.setup()
    val fromOffset = DB.readOnly( implicit session => {
      SQL("select * from hlw_offset").map(rs => {
        (TopicAndPartition(rs.string("topic"),rs.int("partitions")),rs.long("untiloffset"))
      }).list().apply()
    }).toMap
    //如果MySQL表中没有offset信息，就从0开始消费；如果有，就从已经存在的offset开始消费
      val messages = if (fromOffset.isEmpty) {
        println("从头开始消费...")
        KafkaUtils.createDirectStream[String,String,StringDecoder,StringDecoder](ssc,kafkaParams,topics)
      } else {
        println("从已存在记录开始消费...")
        val messageHandler = (mm:MessageAndMetadata[String,String]) => (mm.key(),mm.message())
        KafkaUtils.createDirectStream[String,String,StringDecoder,StringDecoder,(String,String)](ssc,kafkaParams,fromOffset,messageHandler)
      }
      messages.foreachRDD(rdd=>{
        if(!rdd.isEmpty()){
          //输出rdd的数据量
          println("数据统计记录为："+rdd.count())
          //官方案例给出的获得rdd offset信息的方法，offsetRanges是由一系列offsetRange组成的数组
//          trait HasOffsetRanges {
//            def offsetRanges: Array[OffsetRange]
//          }
          val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
          offsetRanges.foreach(x => {
            //输出每次消费的主题，分区，开始偏移量和结束偏移量
            println(s"---${x.topic},${x.partition},${x.fromOffset},${x.untilOffset}---")
           //将最新的偏移量信息保存到MySQL表中
            DB.autoCommit( implicit session => {
              SQL("replace into hlw_offset(topic,groupid,partitions,fromoffset,untiloffset) values (?,?,?,?,?)")
            .bind(x.topic,ValueUtils.getStringValue("group.id"),x.partition,x.fromOffset,x.untilOffset)
              .update().apply()
            })
          })
        }
      })
    ssc.start()
    ssc.awaitTermination()
  }
}

2：自定义的ValueUtils工具类如下

import com.typesafe.config.ConfigFactory
import org.apache.commons.lang3.StringUtils
object ValueUtils {
val load = ConfigFactory.load()
  def getStringValue(key:String, defaultValue:String="") = {
val value = load.getString(key)
    if(StringUtils.isNotEmpty(value)) {
      value
    } else {
      defaultValue
    }
  }
}

3：application.conf内容如下

metadata.broker.list = "192.168.137.251:9092"
auto.offset.reset = "smallest"
group.id = "hlw_offset_group"
kafka.topics = "hlw_offset"
serializer.class = "kafka.serializer.StringEncoder"
request.required.acks = "1"
# JDBC settings
db.default.driver = "com.mysql.jdbc.Driver"
db.default.url="jdbc:mysql://hadoop000:3306/test"
db.default.user="root"
db.default.password="123456"

4：自定义kafka producer

import java.util.{Date, Properties}
import kafka.producer.{KeyedMessage, Producer, ProducerConfig}
object KafkaProducer {
  def main(args: Array[String]): Unit = {
    val properties = new Properties()
    properties.put("serializer.class",ValueUtils.getStringValue("serializer.class"))
    properties.put("metadata.broker.list",ValueUtils.getStringValue("metadata.broker.list"))
    properties.put("request.required.acks",ValueUtils.getStringValue("request.required.acks"))
    val producerConfig = new ProducerConfig(properties)
    val producer = new Producer[String,String](producerConfig)
    val topic = ValueUtils.getStringValue("kafka.topics")
    //每次产生100条数据
    var i = 0
    for (i <- 1 to 100) {
      val runtimes = new Date().toString
     val messages = new KeyedMessage[String, String](topic,i+"","hlw: "+runtimes)
      producer.send(messages)
    }
    println("数据发送完毕...")
  }
}

测试

1：启动kafka服务，并创建主题

[hadoop@hadoop000 bin]$ ./kafka-server-start.sh -daemon /home/hadoop/app/kafka_2.11-0.10.0.1/config/server.properties

[hadoop@hadoop000 bin]$ ./kafka-topics.sh --list --zookeeper localhost:2181/kafka

[hadoop@hadoop000 bin]$ ./kafka-topics.sh --create --zookeeper localhost:2181/kafka --replication-factor 1 --partitions 1 --topic hlw_offset

2：测试前查看MySQL中offset表，刚开始是个空表

mysql> select * from hlw_offset;
Empty set (0.00 sec)

3：通过kafka producer产生500条数据
4：启动SparkStreaming程序

//控制台输出结果：
从头开始消费...
数据统计记录为：500
---hlw_offset,0,0,500---

查看MySQL表，offset记录成功
mysql> select * from hlw_offset;
+------------+------------------+------------+------------+-------------+
| topic      | groupid          | partitions | fromoffset | untiloffset |
+------------+------------------+------------+------------+-------------+
| hlw_offset | hlw_offset_group |          0 |          0 |         500 |
+------------+------------------+------------+------------+-------------+

5：关闭SparkStreaming程序，再使用kafka producer生产300条数据,再次启动spark程序（如果spark从500开始消费，说明成功读取了offset，做到了只读取一次语义）
6：查看更新后的offset MySQL数据

mysql> select * from hlw_offset;
+------------+------------------+------------+------------+-------------+
| topic      | groupid          | partitions | fromoffset | untiloffset |
+------------+------------------+------------+------------+-------------+
| hlw_offset | hlw_offset_group |          0 |        500 |         800 |
+------------+------------------+------------+------------+-------------+