SparkStreaming（SparkSQL）+Kafka+Oracle 使用SQL完成的實時累加統計
阿新 • • 發佈：2018-12-18
Kafka+SparkStreaming已經發展為一個比較成熟的實時日誌收集與計算架構，利用Kafka，即可以支援將用於離線分析的資料流到HDFS，又可以同時支撐多個消費者實時消費資料，包括SparkStreaming。然而，在SparkStreaming程式中如果有複雜業務邏輯的統計，使用scala程式碼實現起來比較困難，也不易於別人理解。但如果在SparkSteaming中也使用SQL來做統計分析，是不是就簡單的多呢？
本文介紹將SparkSQL與SparkStreaming結合起來，使用SQL完成實時的日誌資料統計。SparkStreaming程式以yarn-cluster模式執行在YARN上，不單獨部署Spark叢集。
環境部署 Hadoop-2.6.0-cdh5.8.0（YARN） spark-2.1.0-bin-hadoop2.6 kafka-0.10.2+kafka2.2.0
實時統計需求 以10秒為間隔，統計10秒內的各大區潛客的數量 pom
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_${spark.artifact}</artifactId>
<version SparkStreaming程式程式碼
package com.chumi.dac.sp.stream.sparksqlcount
import com.chumi.dac.sp.stream.jdbc.DBCustomerStream
import com.chumi.dac.sp.stream.utils.DateUtil
import org.apache.kafka.clients.consumer.ConsumerRecord
import org.apache.spark.streaming._
import org.apache.spark.streaming.kafka010.{ConsumerStrategies, KafkaUtils, LocationStrategies}
import org.apache.spark.{ SparkConf, SparkContext}
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.sql.{DataFrame, SQLContext}
import org.apache.spark.streaming.dstream. InputDStream
/**
* Created by LHX on 2018/8/24 14:37.
*/
object CustomerStreamRsscCount {
/**
* BroadcastWrapper,用來註冊廣播變數。
*/
object BroadcastWrapper {
@volatile private var instance:Broadcast[String]=null
def getInstance(sc: SparkContext): Broadcast[String] = {
val point_time: String = DateUtil.getPointTime()
if (instance == null) {
synchronized {
if (instance == null) {
instance = sc.broadcast(point_time)
println("==初始化全域性變數=="+point_time)
}
}
}
instance
}
def update(sc: SparkContext, blocking: Boolean = false,hv:String): Broadcast[String] = {
if (instance != null)
instance.unpersist(blocking)
instance = sc.broadcast(hv)
println("==更新=="+hv)
instance
}
}
/**
* SQLContextSingleton
*/
object SQLContextSingleton {
@transient private var instance: SQLContext = _
def getInstance(sparkContext: SparkContext): SQLContext = {
if (instance == null) {
instance = new SQLContext(sparkContext)
}
instance
}
}
case class DapLog(CIM_ID:String, ENTITY_CODE:String, CARD_FOUND_TIME:String)
def main(args: Array[String]) {
def functionToCreateContext(): StreamingContext = {
val conf = new SparkConf().setAppName("CustomerStreamRsscCount").setMaster("local[2]")
val ssc = new StreamingContext(conf, Seconds(10))
val sqlContext = SQLContextSingleton.getInstance(ssc.sparkContext)
//要使用updateStateByKey方法，必須設定Checkpoint。
ssc.checkpoint("C:/tmp/checkPointPath")
//TM_SST
val jdbcMaps = Map("url" -> "jdbc:oracle:thin:@//IP:1521/test",
"user" -> "user",
"password" -> "password",
"dbtable" -> "TM_SST",
"driver" -> "oracle.jdbc.driver.OracleDriver")
val jdbcDFs = sqlContext.read.options(jdbcMaps).format("jdbc").load
jdbcDFs.createOrReplaceTempView("TM_SST")
//TM_RSSC
val jdbcMapc = Map("url" -> "jdbc:oracle:thin:@//IP:1521/test",
"user" -> "user",
"password" -> "password",
"dbtable" -> "TM_RSSC",
"driver" -> "oracle.jdbc.driver.OracleDriver")
val jdbcDFv = sqlContext.read.options(jdbcMapc).format("jdbc").load
jdbcDFv.createOrReplaceTempView("TM_RSSC")
val topics = "topic1" //stream_test01 topic1
val topicsSet = topics.split(",").toSet
val brokers = "IP:9095"
val kafkaParams = Map[String, Object]("bootstrap.servers" -> brokers
, "auto.offset.reset" -> "latest"
, "sasl.kerberos.service.name" -> "kafka"
, "key.deserializer" -> classOf[StringDeserializer]
, "value.deserializer" -> classOf[StringDeserializer]
, "group.id" -> "testgroup"
)
val dStream: InputDStream[ConsumerRecord[String, String]] = KafkaUtils.createDirectStream[String, String](ssc,
LocationStrategies.PreferConsistent,
ConsumerStrategies.Subscribe[String, String](topicsSet, kafkaParams))
val value = dStream.transform(rdd => {
val sqlC = SQLContextSingleton.getInstance(rdd.sparkContext)
import sqlContext.implicits._
val logDataFrame = rdd.map(w => {
val m: Array[String] = w.value().split(",")
DapLog(m(0), m(1), m(9))
}).toDF()
// 註冊為tempTable
logDataFrame.createOrReplaceTempView("TT_CUSTOMER")
val sql = "select R.RSSC_ID,R.RSSC_NAME,COUNT(1) FROM TT_CUSTOMER T join TM_SST S on T.ENTITY_CODE = S.ENTITYCODE join TM_RSSC R ON S.RSSC_ID = R.RSSC_ID GROUP BY R.RSSC_ID,R.RSSC_NAME"
val data1: DataFrame = sqlC.sql(sql)
val a =data1.rdd.map{r =>(r(1).toString,r(2).toString.toInt) }
a
})
//將以前的資料和最新10s的資料進行求和
val addFunction = (currValues : Seq[Int],preVauleState : Option[Int]) => {
val currentSum = currValues.sum
val previousSum = preVauleState.getOrElse(0)
Some(currentSum + previousSum)
}
val total = value.updateStateByKey[Int](addFunction)
//輸出總計的結果
total.print()
ssc
}
//重啟streamingContext,讀取以前儲存的資料，否則建立新的StreamingContext
val context = StreamingContext.getOrCreate("checkPoint", functionToCreateContext _)
context.start()
context.awaitTermination()
}
}
總結 其中廣播變數是後期根據時間篩選時候使用的，整體思路是先讀取oracle資料並註冊成臨時表，後獲取kafka資料，根據dStream.transform()方法把資料轉換成想要的結果，最後用updateStateByKey()方法累加上一批次的統計結果。 對於初學者很多sparkstream方法還不是很熟悉，所以寫程式碼想不到使用，如果對大家有所幫助，記得點贊哦~