生产SparkStreaming数据零丢失最佳实践(含代码)

Posted 2022-02-01

tags:

篇首语：本文由小常识网(cha138.com)小编为大家整理，主要介绍了生产SparkStreaming数据零丢失最佳实践(含代码)相关的知识，希望对你有一定的参考价值。

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

[[email protected] bin]$ ./kafka-server-start.sh -daemon /home/hadoop/app/kafka_2.11-0.10.0.1/config/server.properties
[[email protected] bin]$ ./kafka-topics.sh --list --zookeeper localhost:2181/kafka
[[email protected] 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 |
+------------+------------------+------------+------------+-------------+

以上是关于生产SparkStreaming数据零丢失最佳实践(含代码)的主要内容，如果未能解决你的问题，请参考以下文章

Spark Streaming 容错改进与零数据丢失

Spark Streaming和Kafka整合保证数据零丢失

Spark（18）——Sparkstreaming实时的数据不丢失的问题

Spark Streaming使用Kafka保证数据零丢失

聊聊 Kafka：Kafka 消息重复的场景以及最佳实践