Spark中的多线程并发处理

Spark中的多任务处理

Spark的一个很是常见的用例是并行运行许多做业。构建做业DAG后，Spark将这些任务分配到多个Executor上并行处理。
但这并不能帮助咱们在同一个Spark应用程序中同时运行两个彻底独立的做业，例如同时从多个数据源读取数据并将它们写到对应的存储，或同时处理多个文件等。java

每一个spark应用程序都须要一个SparkSession(Context)来配置和执行操做。 SparkSession对象是线程安全的，能够根据须要传递给你的Spark应用程序。sql

顺序执行的例子

import org.apache.spark.sql.SparkSession object FancyApp { def def appMain(args: Array[String]) = { // configure spark
    val spark = SparkSession .builder .appName("parjobs") .getOrCreate() val df = spark.sparkContext.parallelize(1 to 100).toDF doFancyDistinct(df, "hdfs:///dis.parquet") doFancySum(df, "hdfs:///sum.parquet") } def doFancyDistinct(df: DataFrame, outPath: String) = df.distinct.write.parquet(outPath) def doFancySum(df: DataFrame, outPath: String) = df.agg(sum("value")).write.parquet(outPath) }

优化后的例子

import org.apache.spark.sql.SparkSession import import java.util.concurrent.Executors import scala.concurrent._ import scala.concurrent.duration._ object FancyApp { def def appMain(args: Array[String]) = { // configure spark
    val spark = SparkSession .builder .appName("parjobs") .getOrCreate() // Set number of threads via a configuration property
    val pool = Executors.newFixedThreadPool(5) // create the implicit ExecutionContext based on our thread pool
    implicit val xc = ExecutionContext.fromExecutorService(pool) val df = spark.sparkContext.parallelize(1 to 100).toDF val taskA = doFancyDistinct(df, "hdfs:///dis.parquet") val taskB = doFancySum(df, "hdfs:///sum.parquet") // Now wait for the tasks to finish before exiting the app
    Await.result(Future.sequence(Seq(taskA,taskB)), Duration(1, MINUTES)) } def doFancyDistinct(df: DataFrame, outPath: String)(implicit xc: ExecutionContext) = Future { df.distinct.write.parquet(outPath) } def doFancySum(df: DataFrame, outPath: String)(implicit xc: ExecutionContext) = Future { df.agg(sum("value")).write.parquet(outPath) } }

java 实现例子

val executors = Executors.newFixedThreadPool(threadPoolNum) val completionService = new ExecutorCompletionService[String](executors) for ((branch_id, dataList) <- summary) { logInfo(s"************** applicationId is ${applicationId} about Multi-threading starting: file is ${branch_id}") completionService.submit(new Callable[String] { override def call(): String = { new VerificationTest(spark, branch_id, dataList, separator).runJob() branch_id } }) }