sparkstreaming 读取mysql_第十篇|SparkStreaming手动维护Kafka Offset的几种方式

Spark Streaming No Receivers 方式的createDirectStream 方法不使用接收器，而是创建输入流直接从Kafka 集群节点拉取消息。输入流保证每个消息从Kafka 集群拉取以后只完全转换一次，保证语义一致性。但是当作业发生故障或重启时，要保障从当前的消费位点去处理数据(即Exactly Once语义)，单纯的依靠SparkStreaming本身的机制是不太理想的，生产环境中通常借助手动管理offset的方式来维护kafka的消费位点。本文分享将介绍如何手动管理Kafka的Offset，希望对你有所帮助。本文主要包括以下内容：

如何使用MySQL管理Kafka的Offset
如何使用Redis管理Kafka的OffSet

如何使用MySQL管理Kafka的Offset

我们可以从Spark Streaming 应用程序中编写代码来手动管理Kafka偏移量，偏移量可以从每一批流处理中生成的RDDS偏移量来获取，获取方式为：

KafkaUtils.createDirectStream(...).foreachRDD { rdd =>
// 获取偏移量
val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges...}

当获取到偏移量之后，可以将将其保存到外部存储设备中(MySQL、Redis、Zookeeper、HBase等)。

使用案例代码

MySQL中用于保存偏移量的表

CREATE TABLE `topic_par_group_offset` (`topic` varchar(255) NOT NULL,`partition` int(11) NOT NULL,`groupid` varchar(255) NOT NULL,`offset` bigint(20) DEFAULT NULL,PRIMARY KEY (`topic`,`partition`,`groupid`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8 ;

常量配置类:ConfigConstants

object ConfigConstants {// Kafka配置val kafkaBrokers = "kms-2:9092,kms-3:9092,kms-4:9092"val groupId = "group_test"val kafkaTopics = "test"val batchInterval = Seconds(5)val streamingStorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2val kafkaKeySer = "org.apache.kafka.common.serialization.StringSerializer"val kafkaValueSer = "org.apache.kafka.common.serialization.StringSerializer"val sparkSerializer = "org.apache.spark.serializer.KryoSerializer"val batchSize = 16384val lingerMs = 1val bufferMemory = 33554432// MySQL配置val user = "root"val password = "123qwe"val url = "jdbc:mysql://localhost:3306/kafka_offset"val driver = "com.mysql.jdbc.Driver"// 检查点配置val checkpointDir = "file:///e:/checkpoint"val checkpointInterval = Seconds(10)// Redis配置val redisAddress = "192.168.10.203"val redisPort = 6379val redisAuth = "123qwe"val redisTimeout = 3000
}

JDBC连接工具类:JDBCConnPool

object JDBCConnPool {val log: Logger = Logger.getLogger(JDBCConnPool.getClass)var dataSource: BasicDataSource = null/*** 创建数据源** @return*/def getDataSource(): BasicDataSource = {if (dataSource == null) {dataSource = new BasicDataSource()dataSource.setDriverClassName(ConfigConstants.driver)dataSource.setUrl(ConfigConstants.url)dataSource.setUsername(ConfigConstants.user)dataSource.setPassword(ConfigConstants.password)dataSource.setMaxTotal(50)dataSource.setInitialSize(3)dataSource.setMinIdle(3)dataSource.setMaxIdle(10)dataSource.setMaxWaitMillis(2 * 10000)dataSource.setRemoveAbandonedTimeout(180)dataSource.setRemoveAbandonedOnBorrow(true)dataSource.setRemoveAbandonedOnMaintenance(true)dataSource.setTestOnReturn(true)dataSource.setTestOnBorrow(true)}return dataSource}/*** 释放数据源*/def closeDataSource() = {if (dataSource != null) {dataSource.close()}}/*** 获取数据库连接** @return*/def getConnection(): Connection = {var conn: Connection = nulltry {if (dataSource != null) {conn = dataSource.getConnection()} else {conn = getDataSource().getConnection()}} catch {case e: Exception =>log.error(e.getMessage(), e)}conn}/*** 关闭连接*/def closeConnection (ps:PreparedStatement , conn:Connection ) {if (ps != null) {try {ps.close();} catch  {case e:Exception =>log.error("预编译SQL语句对象PreparedStatement关闭异常！" + e.getMessage(), e);}}if (conn != null) {try {conn.close();} catch  {case e:Exception =>log.error("关闭连接对象Connection异常！" + e.getMessage(), e);}}}
}

Kafka生产者：KafkaProducerTest

object KafkaProducerTest {def main(args: Array[String]): Unit = {val  props : Properties = new Properties()props.put("bootstrap.servers", ConfigConstants.kafkaBrokers)props.put("batch.size", ConfigConstants.batchSize.asInstanceOf[Integer])props.put("linger.ms", ConfigConstants.lingerMs.asInstanceOf[Integer])props.put("buffer.memory", ConfigConstants.bufferMemory.asInstanceOf[Integer])props.put("key.serializer",ConfigConstants.kafkaKeySer)props.put("value.serializer", ConfigConstants.kafkaValueSer)val  producer :  Producer[String, String] = new KafkaProducer[String, String](props)val startTime : Long  = System.currentTimeMillis()for ( i <- 1 to 100) {producer.send(new ProducerRecord[String, String](ConfigConstants.kafkaTopics, "Spark", Integer.toString(i)))}println("消耗时间：" + (System.currentTimeMillis() - startTime))producer.close()}
}

读取和保存Offset：

该对象的作用是从外部设备中读取和写入Offset，包括MySQL和Redis

object OffsetReadAndSave {/*** 从MySQL中获取偏移量** @param groupid* @param topic* @return*/def getOffsetMap(groupid: String, topic: String): mutable.Map[TopicPartition, Long] = {val conn = JDBCConnPool.getConnection()val selectSql = "select * from topic_par_group_offset where groupid = ? and topic = ?"val ppst = conn.prepareStatement(selectSql)ppst.setString(1, groupid)ppst.setString(2, topic)val result: ResultSet = ppst.executeQuery()// 主题分区偏移量val topicPartitionOffset = mutable.Map[TopicPartition, Long]()while (result.next()) {val topicPartition: TopicPartition = new TopicPartition(result.getString("topic"), result.getInt("partition"))topicPartitionOffset += (topicPartition -> result.getLong("offset"))}JDBCConnPool.closeConnection(ppst, conn)topicPartitionOffset}/*** 从Redis中获取偏移量** @param groupid* @param topic* @return*/def getOffsetFromRedis(groupid: String, topic: String): Map[TopicPartition, Long] = {val jedis: Jedis = JedisConnPool.getConnection()var offsets = mutable.Map[TopicPartition, Long]()val key = s"${topic}_${groupid}"val fields : java.util.Map[String, String] = jedis.hgetAll(key)for (partition <- JavaConversions.mapAsScalaMap(fields)) {offsets.put(new TopicPartition(topic, partition._1.toInt), partition._2.toLong)}offsets.toMap}/*** 将偏移量写入MySQL** @param groupid     消费者组ID* @param offsetRange 消息偏移量范围*/def saveOffsetRanges(groupid: String, offsetRange: Array[OffsetRange]) = {val conn = JDBCConnPool.getConnection()val insertSql = "replace into topic_par_group_offset(`topic`, `partition`, `groupid`, `offset`) values(?,?,?,?)"val ppst = conn.prepareStatement(insertSql)for (offset <- offsetRange) {ppst.setString(1, offset.topic)ppst.setInt(2, offset.partition)ppst.setString(3, groupid)ppst.setLong(4, offset.untilOffset)ppst.executeUpdate()}JDBCConnPool.closeConnection(ppst, conn)}/*** 将偏移量保存到Redis中* @param groupid* @param offsetRange*/def saveOffsetToRedis(groupid: String, offsetRange: Array[OffsetRange]) = {val jedis :Jedis = JedisConnPool.getConnection()for(offsetRange<-offsetRange){val topic=offsetRange.topicval partition=offsetRange.partitionval offset=offsetRange.untilOffset// key为topic_groupid,field为partition，value为offsetjedis.hset(s"${topic}_${groupid}",partition.toString,offset.toString)}}
}

业务处理类

该对象是业务处理逻辑，主要是消费Kafka数据，再处理之后进行手动将偏移量保存到MySQL中。在启动程序时，会判断外部存储设备中是否存在偏移量，如果是首次启动则从最初的消费位点消费，如果存在Offset，则从当前的Offset去消费。

观察现象：当首次启动时会从头消费数据，手动停止程序，然后再次启动，会发现会从当前提交的偏移量消费数据。

object ManualCommitOffset {def main(args: Array[String]): Unit = {val brokers = ConfigConstants.kafkaBrokersval groupId = ConfigConstants.groupIdval topics = ConfigConstants.kafkaTopicsval batchInterval = ConfigConstants.batchIntervalval conf = new SparkConf().setAppName(ManualCommitOffset.getClass.getSimpleName).setMaster("local[1]").set("spark.serializer",ConfigConstants.sparkSerializer)val ssc = new StreamingContext(conf, batchInterval)// 必须开启checkpoint,否则会报错ssc.checkpoint(ConfigConstants.checkpointDir)ssc.sparkContext.setLogLevel("OFF")//使用broker和topic创建direct kafka streamval topicSet = topics.split(" ").toSet// kafka连接参数val kafkaParams = Map[String, Object](ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG -> brokers,ConsumerConfig.GROUP_ID_CONFIG -> groupId,ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG -> classOf[StringDeserializer],ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG -> classOf[StringDeserializer],ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG -> (false: java.lang.Boolean),ConsumerConfig.AUTO_OFFSET_RESET_CONFIG -> "earliest")// 从MySQL中读取该主题对应的消费者组的分区偏移量val offsetMap = OffsetReadAndSave.getOffsetMap(groupId, topics)var inputDStream: InputDStream[ConsumerRecord[String, String]] = null//如果MySQL中已经存在了偏移量,则应该从该偏移量处开始消费if (offsetMap.size > 0) {println("存在偏移量，从该偏移量处进行消费！！")inputDStream = KafkaUtils.createDirectStream[String, String](ssc,LocationStrategies.PreferConsistent,ConsumerStrategies.Subscribe[String, String](topicSet, kafkaParams, offsetMap))} else {//如果MySQL中没有存在了偏移量，从最早开始消费inputDStream = KafkaUtils.createDirectStream[String, String](ssc,LocationStrategies.PreferConsistent,ConsumerStrategies.Subscribe[String, String](topicSet, kafkaParams))}// checkpoint时间间隔，必须是batchInterval的整数倍inputDStream.checkpoint(ConfigConstants.checkpointInterval)// 保存batch的offsetvar offsetRanges = Array[OffsetRange]()// 获取当前DS的消息偏移量val transformDS = inputDStream.transform { rdd =>// 获取offsetoffsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRangesrdd}/*** 状态更新函数* @param newValues:新的value值* @param stateValue：状态值* @return*/def updateFunc(newValues: Seq[Int], stateValue: Option[Int]): Option[Int] = {var oldvalue = stateValue.getOrElse(0) // 获取状态值// 遍历当前数据，并更新状态for (newValue <- newValues) {oldvalue += newValue}// 返回最新的状态Option(oldvalue)}// 业务逻辑处理// 该示例统计消息key的个数，用于查看是否是从已经提交的偏移量消费数据transformDS.map(meg => ("spark", meg.value().toInt)).updateStateByKey(updateFunc).print()// 打印偏移量和数据信息，观察输出的结果transformDS.foreachRDD { (rdd, time) =>// 遍历打印该RDD数据rdd.foreach { record =>println(s"key=${record.key()},value=${record.value()},partition=${record.partition()},offset=${record.offset()}")}// 打印消费偏移量信息for (o <- offsetRanges) {println(s"topic=${o.topic},partition=${o.partition},fromOffset=${o.fromOffset},untilOffset=${o.untilOffset},time=${time}")}//将偏移量保存到到MySQL中OffsetReadAndSave.saveOffsetRanges(groupId, offsetRanges)}ssc.start()ssc.awaitTermination()}
}

如何使用Redis管理Kafka的OffSet

Redis连接类

object JedisConnPool {val config = new JedisPoolConfig//最大连接数config.setMaxTotal(60)//最大空闲连接数config.setMaxIdle(10)config.setTestOnBorrow(true)//服务器ipval redisAddress :String = ConfigConstants.redisAddress.toString// 端口号val redisPort:Int = ConfigConstants.redisPort.toInt//访问密码val redisAuth :String = ConfigConstants.redisAuth.toString//等待可用连接的最大时间val redisTimeout:Int = ConfigConstants.redisTimeout.toIntval pool = new JedisPool(config,redisAddress,redisPort,redisTimeout,redisAuth)def getConnection():Jedis = {pool.getResource}}

业务逻辑处理

该对象与上面的基本类似，只不过使用的是Redis来进行存储Offset，存储到Redis的数据类型是Hash，基本格式为：[key field value] -> [ topic_groupid partition offset]，即 key为topic_groupid,field为partition，value为offset。

object ManualCommitOffsetToRedis {def main(args: Array[String]): Unit = {val brokers = ConfigConstants.kafkaBrokersval groupId = ConfigConstants.groupIdval topics = ConfigConstants.kafkaTopicsval batchInterval = ConfigConstants.batchIntervalval conf = new SparkConf().setAppName(ManualCommitOffset.getClass.getSimpleName).setMaster("local[1]").set("spark.serializer", ConfigConstants.sparkSerializer)val ssc = new StreamingContext(conf, batchInterval)// 必须开启checkpoint,否则会报错ssc.checkpoint(ConfigConstants.checkpointDir)ssc.sparkContext.setLogLevel("OFF")//使用broker和topic创建direct kafka streamval topicSet = topics.split(" ").toSet// kafka连接参数val kafkaParams = Map[String, Object](ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG -> brokers,ConsumerConfig.GROUP_ID_CONFIG -> groupId,ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG -> classOf[StringDeserializer],ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG -> classOf[StringDeserializer],ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG -> (false: java.lang.Boolean),ConsumerConfig.AUTO_OFFSET_RESET_CONFIG -> "earliest")// 从Redis中读取该主题对应的消费者组的分区偏移量val offsetMap = OffsetReadAndSave.getOffsetFromRedis(groupId, topics)var inputDStream: InputDStream[ConsumerRecord[String, String]] = null//如果Redis中已经存在了偏移量,则应该从该偏移量处开始消费if (offsetMap.size > 0) {println("存在偏移量，从该偏移量处进行消费！！")inputDStream = KafkaUtils.createDirectStream[String, String](ssc,LocationStrategies.PreferConsistent,ConsumerStrategies.Subscribe[String, String](topicSet, kafkaParams, offsetMap))} else {//如果Redis中没有存在了偏移量，从最早开始消费inputDStream = KafkaUtils.createDirectStream[String, String](ssc,LocationStrategies.PreferConsistent,ConsumerStrategies.Subscribe[String, String](topicSet, kafkaParams))}// checkpoint时间间隔，必须是batchInterval的整数倍inputDStream.checkpoint(ConfigConstants.checkpointInterval)// 保存batch的offsetvar offsetRanges = Array[OffsetRange]()// 获取当前DS的消息偏移量val transformDS = inputDStream.transform { rdd =>// 获取offsetoffsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRangesrdd}/*** 状态更新函数** @param newValues  :新的value值* @param stateValue ：状态值* @return*/def updateFunc(newValues: Seq[Int], stateValue: Option[Int]): Option[Int] = {var oldvalue = stateValue.getOrElse(0) // 获取状态值// 遍历当前数据，并更新状态for (newValue <- newValues) {oldvalue += newValue}// 返回最新的状态Option(oldvalue)}// 业务逻辑处理// 该示例统计消息key的个数，用于查看是否是从已经提交的偏移量消费数据transformDS.map(meg => ("spark", meg.value().toInt)).updateStateByKey(updateFunc).print()// 打印偏移量和数据信息，观察输出的结果transformDS.foreachRDD { (rdd, time) =>// 遍历打印该RDD数据rdd.foreach { record =>println(s"key=${record.key()},value=${record.value()},partition=${record.partition()},offset=${record.offset()}")}// 打印消费偏移量信息for (o <- offsetRanges) {println(s"topic=${o.topic},partition=${o.partition},fromOffset=${o.fromOffset},untilOffset=${o.untilOffset},time=${time}")}//将偏移量保存到到Redis中OffsetReadAndSave.saveOffsetToRedis(groupId, offsetRanges)}ssc.start()ssc.awaitTermination()}}

总结

本文介绍了如何使用外部存储设备来保存Kafka的消费位点，通过详细的代码示例说明了使用MySQL和Redis管理消费位点的方式。当然，外部存储设备很多，用户也可以使用其他的存储设备进行管理Offset，比如Zookeeper和HBase等，其基本处理思路都十分相似。

大数据技术与数仓