Spark Streaming Join
多数据源Join思路
多数据源Join大致有以下三种思路：
-
数据源端Join，如Android/IOS客户端在上报用户行为数据时就获取并带上用户基础信息。
-
计算引擎上Join,如用Spark Streaming、Flink做Join。
-
结果端Join，如用HBase/ES做Join,Join键做Rowkey/_id，各字段分别写入列簇、列或field。
三种思路各有优劣，使用时注意一下。这里总结在计算引擎Spark Streaming上做Join。
Stream-Static Join
流与完全静态数据Join
流与完全静态数据Join。有两种方式，一种是RDD Join方式，另一种是Broadcast Join(也叫Map-Side Join)方式。
RDD Join 方式
思路：RDD Join RDD 。
package com.bigData.spark
import com.alibaba.fastjson.{JSON, JSONException, JSONObject}
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.log4j.{Level, Logger}
import org.apache.spark.SparkConf
import org.apache.spark.streaming.kafka010.{ConsumerStrategies, KafkaUtils, LocationStrategies}
import org.apache.spark.streaming.{Durations, StreamingContext}
/**
* Author: Wang Pei
* License: Copyright(c) Pei.Wang
* Summary:
*
* Stream-Static Join
*
* spark 2.2.2
*
*/
case class UserInfo(userID:String,userName:String,userAddress:String)
object StreamStaicJoin {
def main(args: Array[String]): Unit = {
//设置日志等级
Logger.getLogger("org").setLevel(Level.WARN)
//Kafka 参数
val kafkaParams= Map[String, Object](
"bootstrap.servers" -> "localhost:9092",
"key.deserializer" -> classOf[StringDeserializer],
"value.deserializer" -> classOf[StringDeserializer],
"auto.offset.reset" -> "latest",
"enable.auto.commit" -> (true: java.lang.Boolean),
"group.id" -> "testTopic3_consumer_v1")
//spark环境
val sparkConf = new SparkConf().setAppName(this.getClass.getSimpleName.replace("$","")).setMaster("local[3]")
val ssc = new StreamingContext(sparkConf,Durations.seconds(10))
/** 1) 静态数据: 用户基础信息*/
val userInfo=ssc.sparkContext.parallelize(Array(
UserInfo("user_1","name_1","address_1"),
UserInfo("user_2","name_2","address_2"),
UserInfo("user_3","name_3","address_3"),
UserInfo("user_4","name_4","address_4"),
UserInfo("user_5","name_5","address_5")
)).map(item=>(item.userID,item))
/** 2) 流式数据: 用户发的tweet数据*/
/** 数据示例：
* eventTime：事件时间、retweetCount：转推数、language：语言、userID：用户ID、favoriteCount：点赞数、id：事件ID
* {"eventTime": "2018-11-05 10:04:00", "retweetCount": 1, "language": "chinese", "userID": "user_1", "favoriteCount": 1, "id": 4909846540155641457} */
val kafkaDStream=KafkaUtils.createDirectStream[String,String](
ssc,
LocationStrategies.PreferConsistent,
ConsumerStrategies.Subscribe[String,String](Set("testTopic3"),kafkaParams)
).map(item=>parseJson(item.value())).map(item=>{
val userID = item.getString("userID")
val eventTime = item.getString("eventTime")
val language= item.getString("language")
val favoriteCount = item.getInteger("favoriteCount")
val retweetCount = item.getInteger("retweetCount")
(userID,(userID,eventTime,language,favoriteCount,retweetCount))
})
/** 3) 流与静态数据做Join (RDD Join 方式)*/
kafkaDStream.foreachRDD(_.join(userInfo).foreach(println))
ssc.start()
ssc.awaitTermination()
}
/**json解析*/
def parseJson(log:String):JSONObject={
var ret:JSONObject=null
try{
ret=JSON.parseObject(log)
}catch {
//异常json数据处理
case e:JSONException => println(log)
}
ret
}
}
Broadcast Join 方式
思路：RDD遍历每一条数据，去匹配广播变量中的值。
package com.bigData.spark
import com.alibaba.fastjson.{JSON, JSONException, JSONObject}
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.kafka010.{ConsumerStrategies, KafkaUtils, LocationStrategies}
import org.apache.spark.streaming.{Durations, StreamingContext}
/**
* Author: Wang Pei
* License: Copyright(c) Pei.Wang
* Summary:
*
* Stream-Static Join
*
* spark 2.2.2
*
*/
case class UserInfo(userID:String,userName:String,userAddress:String)
object StreamStaticJoin2 {
def main(args: Array[String]): Unit = {
//设置日志等级
Logger.getLogger("org").setLevel(Level.WARN)
//Kafka 参数
val kafkaParams= Map[String, Object](
"bootstrap.servers" -> "localhost:9092",
"key.deserializer" -> classOf[StringDeserializer],
"value.deserializer" -> classOf[StringDeserializer],
"auto.offset.reset" -> "latest",
"enable.auto.commit" -> (true: java.lang.Boolean),
"group.id" -> "testTopic3_consumer_v1")
//spark环境
val sparkConf = new SparkConf().setAppName(this.getClass.getSimpleName.replace("$","")).setMaster("local[3]")
val ssc = new StreamingContext(sparkConf,Durations.seconds(10))
/** 1) 静态数据: 用户基础信息。 将用户基础信息广播出去。*/
val broadcastUserInfo=ssc.sparkContext.broadcast(
Map(
"user_1"->UserInfo("user_1","name_1","address_1"),
"user_2"->UserInfo("user_2","name_2","address_2"),
"user_3"->UserInfo("user_3","name_3","address_3"),
"user_4"->UserInfo("user_4","name_4","address_4"),
"user_5"->UserInfo("user_5","name_5","address_5")
))
/** 2) 流式数据: 用户发的tweet数据*/
/** 数据示例：
* eventTime：事件时间、retweetCount：转推数、language：语言、userID：用户ID、favoriteCount：点赞数、id：事件ID
* {"eventTime": "2018-11-05 10:04:00", "retweetCount": 1, "language": "chinese", "userID": "user_1", "favoriteCount": 1, "id": 4909846540155641457} */
val kafkaDStream=KafkaUtils.createDirectStream[String,String](
ssc,
LocationStrategies.PreferConsistent,
ConsumerStrategies.Subscribe[String,String](List("testTopic3"),kafkaParams)
).map(item=>parseJson(item.value())).map(item=>{
val userID = item.getString("userID")
val eventTime = item.getString("eventTime")
val language= item.getString("language")
val favoriteCount = item.getInteger("favoriteCount")
val retweetCount = item.getInteger("retweetCount")
(userID,(userID,eventTime,language,favoriteCount,retweetCount))
})
/** 3) 流与静态数据做Join (Broadcast Join 方式)*/
val result=kafkaDStream.mapPartitions(part=>{
val userInfo = broadcastUserInfo.value
part.map(item=>{
(item._1,(item._2,userInfo.getOrElse(item._1,null)))})
})
result.foreachRDD(_.foreach(println))
ssc.start()
ssc.awaitTermination()
}
/**json解析*/
def parseJson(log:String):JSONObject={
var ret:JSONObject=null
try{
ret=JSON.parseObject(log)
}catch {
//异常json数据处理
case e:JSONException => println(log)
}
ret
}
}
流与半静态数据Join
半静态数据指的是放在Redis等的数据，会被更新。
思路：RDD 每个Partition连接一次Redis,遍历Partition中每条数据，根据k,去Redis中查找v。
package com.bigData.spark
import com.alibaba.fastjson.{JSON, JSONException, JSONObject}
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.log4j.{Level, Logger}
import org.apache.spark.SparkConf
import org.apache.spark.streaming.kafka010.{ConsumerStrategies, KafkaUtils, LocationStrategies}
import org.apache.spark.streaming.{Durations, StreamingContext}
import redis.clients.jedis.Jedis
/**
* Author: Wang Pei
* License: Copyright(c) Pei.Wang
* Summary:
*
* Stream-Static Join
*
* spark 2.2.2
*
*/
object StreamStaicJoin3 {
def main(args: Array[String]): Unit = {
//设置日志等级
Logger.getLogger("org").setLevel(Level.WARN)
//Kafka 参数
val kafkaParams= Map[String, Object](
"bootstrap.servers" -> "localhost:9092",
"key.deserializer" -> classOf[StringDeserializer],
"value.deserializer" -> classOf[StringDeserializer],
"auto.offset.reset" -> "latest",
"enable.auto.commit" -> (true: java.lang.Boolean),
"group.id" -> "testTopic3_consumer_v1")
//spark环境
val sparkConf = new SparkConf().setAppName(this.getClass.getSimpleName.replace("$","")).setMaster("local[3]")
val ssc = new StreamingContext(sparkConf,Durations.seconds(10))
/** 1) 半静态数据: 用户基础信息,在Redis中*/
/** HMSET user_1 userID "user_1" name "name_1" address "address_1" */
/** HMSET user_2 userID "user_2" name "name_2" address "address_2" */
/** 2) 流式数据: 用户发的tweet数据*/
/** 数据示例：
* eventTime：事件时间、retweetCount：转推数、language：语言、userID：用户ID、favoriteCount：点赞数、id：事件ID
* {"eventTime": "2018-11-05 10:04:00", "retweetCount": 1, "language": "chinese", "userID": "user_1", "favoriteCount": 1, "id": 4909846540155641457} */
val kafkaDStream=KafkaUtils.createDirectStream[String,String](
ssc,
LocationStrategies.PreferConsistent,
ConsumerStrategies.Subscribe[String,String](Set("testTopic3"),kafkaParams)
).map(item=>parseJson(item.value())).map(item=>{
val userID = item.getString("userID")
val eventTime = item.getString("eventTime")
val language= item.getString("language")
val favoriteCount = item.getInteger("favoriteCount")
val retweetCount = item.getInteger("retweetCount")
(userID,(userID,eventTime,language,favoriteCount,retweetCount))
})
/** 3) 流与半静态数据做Join (RDD Join 方式)*/
val result=kafkaDStream.mapPartitions(part=>{
val redisCli=connToRedis("localhost",6379,3000,10)
part.map(item=>{
(item._1,(item._2,redisCli.hmget(item._1,"userID","name","address")))
})
})
result.foreachRDD(_.foreach(println))
ssc.start()
ssc.awaitTermination()
}
/**json解析*/
def parseJson(log:String):JSONObject={
var ret:JSONObject=null
try{
ret=JSON.parseObject(log)
}catch {
//异常json数据处理
case e:JSONException => println(log)
}
ret
}
/**连接到redis*/
def connToRedis(redisHost:String,redisPort:Int,timeout:Int,dbNum:Int): Jedis ={
val redisCli=new Jedis(redisHost,redisPort,timeout)
redisCli.connect()
redisCli.select(dbNum)
redisCli
}
}
Stream-Stream Join
流与流Join。
思路：DStream Join DStream。
package com.bigData.spark
import com.alibaba.fastjson.{JSON, JSONException, JSONObject}
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.kafka010.{ConsumerStrategies, KafkaUtils, LocationStrategies}
import org.apache.spark.streaming.{Durations, StreamingContext}
/**
* Author: Wang Pei
* License: Copyright(c) Pei.Wang
* Summary:
*
* Stream-Stream Join
*
* spark 2.2.2
*
*/
object StreamStreamJoin {
def main(args: Array[String]): Unit = {
//设置日志等级
Logger.getLogger("org").setLevel(Level.WARN)
//Kafka 参数
val kafkaParams1= Map[String, Object](
"bootstrap.servers" -> "localhost:9092",
"key.deserializer" -> classOf[StringDeserializer],
"value.deserializer" -> classOf[StringDeserializer],
"auto.offset.reset" -> "latest",
"enable.auto.commit" -> (true: java.lang.Boolean),
"group.id" -> "testTopic3_consumer_v1")
val kafkaParams2= Map[String, Object](
"bootstrap.servers" -> "localhost:9092",
"key.deserializer" -> classOf[StringDeserializer],
"value.deserializer" -> classOf[StringDeserializer],
"auto.offset.reset" -> "latest",
"enable.auto.commit" -> (true: java.lang.Boolean),
"group.id" -> "testTopic4_consumer_v1")
//spark环境
val sparkConf = new SparkConf().setAppName(this.getClass.getSimpleName.replace("$","")).setMaster("local[3]")
val ssc = new StreamingContext(sparkConf,Durations.seconds(10))
/** 1) 流式数据: 用户发的tweet数据*/
/** 数据示例：
* eventTime：事件时间、retweetCount：转推数、language：语言、userID：用户ID、favoriteCount：点赞数、id：事件ID
* {"eventTime": "2018-11-05 10:04:00", "retweetCount": 1, "language": "chinese", "userID": "user_1", "favoriteCount": 1, "id": 4909846540155641457} */
val kafkaDStream1=KafkaUtils.createDirectStream[String,String](
ssc,
LocationStrategies.PreferConsistent,
ConsumerStrategies.Subscribe[String,String](List("testTopic3"),kafkaParams1)
).map(item=>parseJson(item.value())).map(item=>{
val userID = item.getString("userID")
val eventTime = item.getString("eventTime")
val language= item.getString("language")
val favoriteCount = item.getInteger("favoriteCount")
val retweetCount = item.getInteger("retweetCount")
(userID,(userID,eventTime,language,favoriteCount,retweetCount))
})
/** 2) 流式数据: 用户发的tweet数据*/
/** 数据示例：
* eventTime：事件时间、retweetCount：转推数、language：语言、userID：用户ID、favoriteCount：点赞数、id：事件ID
* {"eventTime": "2018-11-05 10:04:00", "retweetCount": 1, "language": "chinese", "userID": "user_1", "favoriteCount": 1, "id": 4909846540155641457} */
val kafkaDStream2=KafkaUtils.createDirectStream[String,String](
ssc,
LocationStrategies.PreferConsistent,
ConsumerStrategies.Subscribe[String,String](List("testTopic4"),kafkaParams2)
).map(item=>parseJson(item.value())).map(item=>{
val userID = item.getString("userID")
val eventTime = item.getString("eventTime")
val language= item.getString("language")
val favoriteCount = item.getInteger("favoriteCount")
val retweetCount = item.getInteger("retweetCount")
(userID,(userID,eventTime,language,favoriteCount,retweetCount))
})
/** 3) Stream-Stream Join*/
val joinedDStream = kafkaDStream1.leftOuterJoin(kafkaDStream2)
joinedDStream.foreachRDD(_.foreach(println))
ssc.start()
ssc.awaitTermination()
}
/**json解析*/
def parseJson(log:String):JSONObject={
var ret:JSONObject=null
try{
ret=JSON.parseObject(log)
}catch {
//异常json数据处理
case e:JSONException => println(log)
}
ret
}
}