生产SparkStreaming数据零丢失最佳实践(含代码)
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) ); Maven依赖包 <scala.version>2.11.8</scala.version> <spark.version>2.3.1</spark.version> <scalikejdbc.version>2.5.0</scalikejdbc.version> -------------------------------------------------- <dependency> <groupId>org.scala-lang</groupId> <artifactId>scala-library</artifactId> <version>${scala.version}</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-core_2.11</artifactId> <version>${spark.version}</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-sql_2.11</artifactId> <version>${spark.version}</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-streaming_2.11</artifactId> <version>${spark.version}</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-streaming-kafka-0-8_2.11</artifactId> <version>${spark.version}</version> </dependency> <dependency> <groupId>mysql</groupId> <artifactId>mysql-connector-java</artifactId> <version>5.1.27</version> </dependency> <!-- https://mvnrepository.com/artifact/org.scalikejdbc/scalikejdbc --> <dependency> <groupId>org.scalikejdbc</groupId> <artifactId>scalikejdbc_2.11</artifactId> <version>2.5.0</version> </dependency> <dependency> <groupId>org.scalikejdbc</groupId> <artifactId>scalikejdbc-config_2.11</artifactId> <version>2.5.0</version> </dependency> <dependency> <groupId>com.typesafe</groupId> <artifactId>config</artifactId> <version>1.3.0</version> </dependency> <dependency> <groupId>org.apache.commons</groupId> <artifactId>commons-lang3</artifactId> <version>3.5</version> </dependency> 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 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},${x.untilOffset}---") //将最新的偏移量信息保存到MySQL表中 DB.autoCommit( implicit session => { SQL("replace into hlw_offset(topic,groupid,partitions,fromoffset,untiloffset) values (?,?,?,?,?)") .bind(x.topic,ValueUtils.getStringValue("group.id"),x.partition,x.fromOffset,x.untilOffset) .update().apply() }) }) } }) ssc.start() ssc.awaitTermination() } } 自定义的ValueUtils工具类如下 import com.typesafe.config.ConfigFactory import org.apache.commons.lang3.StringUtils object ValueUtils { val load = ConfigFactory.load() def getStringValue(key:String, defaultValue:String="") = { val value = load.getString(key) if(StringUtils.isNotEmpty(value)) { value } else { defaultValue } } } application.conf内容如下 metadata.broker.list = "192.168.137.251:9092" auto.offset.reset = "smallest" group.id = "hlw_offset_group" kafka.topics = "hlw_offset" serializer.class = "kafka.serializer.StringEncoder" request.required.acks = "1" # JDBC settings db.default.driver = "com.mysql.jdbc.Driver" db.default.url="jdbc:mysql://hadoop000:3306/test" db.default.user="root" db.default.password="123456" 自定义kafka producer import java.util.{Date, Properties} import kafka.producer.{KeyedMessage, Producer, ProducerConfig} object KafkaProducer { def main(args: Array[String]): Unit = { val properties = new Properties() properties.put("serializer.class",ValueUtils.getStringValue("serializer.class")) properties.put("metadata.broker.list",ValueUtils.getStringValue("metadata.broker.list")) properties.put("request.required.acks",ValueUtils.getStringValue("request.required.acks")) val producerConfig = new ProducerConfig(properties) val producer = new Producer[String,String](producerConfig) val topic = ValueUtils.getStringValue("kafka.topics") //每次产生100条数据 var i = 0 for (i <- 1 to 100) { val runtimes = new Date().toString val messages = new KeyedMessage[String, String](topic,i+"","hlw: "+runtimes) producer.send(messages) } println("数据发送完毕...") } } 测试 启动kafka服务，并创建主题 [hadoop@hadoop000 bin]$ ./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 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) 通过kafka producer产生500条数据 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 | +------------+------------------+------------+------------+-------------+ 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） //控制台结果输出： 从已存在记录开始消费... 数据统计记录为：300 ---hlw_offset,0,500,800--- 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 | +------------+------------------+------------+------------+-------------+
