大数据基础之Spark(3)Spark Thrift实现原理及代码实现

spark 2.1.1

一 启动命令

启动spark thrift命令

$SPARK_HOME/sbin/start-thriftserver.sh

然后会执行

org.apache.spark.deploy.SparkSubmit --class org.apache.spark.sql.hive.thriftserver.HiveThriftServer2

二 启动过程及代码分析

hive thrift代码详见:https://www.cnblogs.com/barneywill/p/10185168.html

HiveThriftServer2是spark thrift核心类,继承自Hive的HiveServer2

org.apache.spark.sql.hive.thriftserver.HiveThriftServer2 extends org.apache.hive.service.server.HiveServer2

 

启动过程:

HiveThriftServer2.main

         SparkSQLEnv.init (sparkConf sparkSession sparkContext sqlContext)

         HiveThriftServer2.init

                  addService(ThriftBinaryCLIService)

         HiveThriftServer2.start

                  ThriftBinaryCLIService.run

                          TServer.serve

 

类结构:【接口或父类->子类】

TServer->TThreadPoolServer

         TProcessorFactory->SQLPlainProcessorFactory

                  TProcessor->TSetIpAddressProcessor

                          ThriftCLIService->ThriftBinaryCLIService

                                   CLIService->SparkSQLCLIService (核心子类)

 

服务初始化过程:

CLIService.init

         SparkSQLCLIService.init

                  addService(SparkSQLSessionManager)

                  initCompositeService

                          SparkSQLSessionManager.init

                                   addService(SparkSQLOperationManager)

                                   initCompositeService

                                            SparkSQLOperationManager.init

三 DDL执行过程

ddl执行过程需要和hive metastore交互

从执行计划开始:

spark-sql> explain create table test_table(id string);
== Physical Plan ==
ExecutedCommand
+- CreateTableCommand CatalogTable(
Table: `test_table`
Created: Wed Dec 19 18:04:15 CST 2018
Last Access: Thu Jan 01 07:59:59 CST 1970
Type: MANAGED
Schema: [StructField(id,StringType,true)]
Provider: hive
Storage(InputFormat: org.apache.hadoop.mapred.TextInputFormat, OutputFormat: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat)), false
Time taken: 0.28 seconds, Fetched 1 row(s)

从执行计划里可以找到具体的Command,这里是CreateTableCommand 

 

org.apache.spark.sql.execution.command.tables

case class CreateTableCommand(table: CatalogTable, ifNotExists: Boolean) extends RunnableCommand {

  override def run(sparkSession: SparkSession): Seq[Row] = {
    sparkSession.sessionState.catalog.createTable(table, ifNotExists)
    Seq.empty[Row]
  }
}

这里可以看到是直接将请求分发给sparkSession.sessionState.catalog

 

org.apache.spark.sql.internal.SessionState

  /**
   * Internal catalog for managing table and database states.
   */
  lazy val catalog = new SessionCatalog(
    sparkSession.sharedState.externalCatalog,
    sparkSession.sharedState.globalTempViewManager,
    functionResourceLoader,
    functionRegistry,
    conf,
    newHadoopConf())

取的是sparkSession.sharedState.externalCatalog

 

org.apache.spark.sql.internal.SharedState

  /**
   * A catalog that interacts with external systems.
   */
  val externalCatalog: ExternalCatalog =
    SharedState.reflect[ExternalCatalog, SparkConf, Configuration](
      SharedState.externalCatalogClassName(sparkContext.conf),
      sparkContext.conf,
      sparkContext.hadoopConfiguration)
...
  private val HIVE_EXTERNAL_CATALOG_CLASS_NAME = "org.apache.spark.sql.hive.HiveExternalCatalog"

  private def externalCatalogClassName(conf: SparkConf): String = {
    conf.get(CATALOG_IMPLEMENTATION) match {
      case "hive" => HIVE_EXTERNAL_CATALOG_CLASS_NAME
      case "in-memory" => classOf[InMemoryCatalog].getCanonicalName
    }
  }

这里可以看到是通过externalCatalogClassName反射实例化的,代码里硬编码使用的是org.apache.spark.sql.hive.HiveExternalCatalog

 

org.apache.spark.sql.hive.HiveExternalCatalog

  /**
   * A Hive client used to interact with the metastore.
   */
  val client: HiveClient = {
    HiveUtils.newClientForMetadata(conf, hadoopConf)
  }

  private def withClient[T](body: => T): T = synchronized {
    try {
      body
    } catch {
      case NonFatal(exception) if isClientException(exception) =>
        val e = exception match {
          // Since we are using shim, the exceptions thrown by the underlying method of
          // Method.invoke() are wrapped by InvocationTargetException
          case i: InvocationTargetException => i.getCause
          case o => o
        }
        throw new AnalysisException(
          e.getClass.getCanonicalName + ": " + e.getMessage, cause = Some(e))
    }
  }

  override def createDatabase(
      dbDefinition: CatalogDatabase,
      ignoreIfExists: Boolean): Unit = withClient {
    client.createDatabase(dbDefinition, ignoreIfExists)
  }

这个类里执行任何ddl方法都会执行withClient,而withClient有synchronized,执行过程是直接把请求分发给client,下面看client是什么

 

org.apache.spark.sql.hive.client.IsolatedClientLoader

  /** The isolated client interface to Hive. */
  private[hive] def createClient(): HiveClient = {
    if (!isolationOn) {
      return new HiveClientImpl(version, sparkConf, hadoopConf, config, baseClassLoader, this)
    }
    // Pre-reflective instantiation setup.
    logDebug("Initializing the logger to avoid disaster...")
    val origLoader = Thread.currentThread().getContextClassLoader
    Thread.currentThread.setContextClassLoader(classLoader)

    try {
      classLoader
        .loadClass(classOf[HiveClientImpl].getName)
        .getConstructors.head
        .newInstance(version, sparkConf, hadoopConf, config, classLoader, this)
        .asInstanceOf[HiveClient]
    } catch {

可见client直接用的是org.apache.spark.sql.hive.client.HiveClientImpl

 

org.apache.spark.sql.hive.client.HiveClientImpl

  def withHiveState[A](f: => A): A = retryLocked {
    val original = Thread.currentThread().getContextClassLoader
    // Set the thread local metastore client to the client associated with this HiveClientImpl.
    Hive.set(client)
    // The classloader in clientLoader could be changed after addJar, always use the latest
    // classloader
    state.getConf.setClassLoader(clientLoader.classLoader)
    // setCurrentSessionState will use the classLoader associated
    // with the HiveConf in `state` to override the context class loader of the current
    // thread.
    shim.setCurrentSessionState(state)
    val ret = try f finally {
      Thread.currentThread().setContextClassLoader(original)
      HiveCatalogMetrics.incrementHiveClientCalls(1)
    }
    ret
  }
  private def retryLocked[A](f: => A): A = clientLoader.synchronized {
...

  override def createDatabase(
      database: CatalogDatabase,
      ignoreIfExists: Boolean): Unit = withHiveState {
    client.createDatabase(
      new HiveDatabase(
        database.name,
        database.description,
        database.locationUri,
        Option(database.properties).map(_.asJava).orNull),
        ignoreIfExists)
  }

这个类执行任何ddl方法都会执行withHiveState,withHiveState会执行retryLocked,retryLocked上有synchronized;而且这里也是直接将请求分发给client,这里的client是hive的类org.apache.hadoop.hive.ql.metadata.Hive

 

四 DML执行过程

dml执行过程最后会执行到spark.sql

sql执行过程:

CLIService.executeStatement (返回OperationHandle)

         SessionManager.getSession

         SessionManager.openSession

                  SparkSQLSessionManager.openSession

                          SparkSQLOperationManager.sessionToContexts.set (openSession时:session和sqlContext建立映射)

         HiveSession.executeStatement

                  HiveSessionImpl.executeStatementInternal

                          OperationManager.newExecuteStatementOperation

                                   SparkSQLOperationManager.newExecuteStatementOperation

                                            SparkSQLOperationManager.sessionToContexts.get (通过session取到sqlContext)

                          ExecuteStatementOperation.run

                                   SparkExecuteStatementOperation.run

                                            SparkExecuteStatementOperation.execute

                                                     SQLContext.sql (熟悉的spark sql)

可见从SparkSQLCLIService初始化开始,逐个将各个类的实现类改为spark的子类比如:

org.apache.spark.sql.hive.thriftserver.SparkSQLSessionManager extends org.apache.hive.service.cli.session.SessionManager
org.apache.spark.sql.hive.thriftserver.server.SparkSQLOperationManager extends org.apache.hive.service.cli.operation.OperationManager
org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation extends org.apache.hive.service.cli.operation.ExecuteStatementOperation

从而实现底层实现的替换;

 

hive的HiveServer2为什么这么容易的被扩展,详见spark代码的sql/hive-thriftserver,这里应该是将hive1.2代码做了很多修改,以后升级就不那么容易了;
至于spark为什么要花这么大力气扩展HiveServer2而不是重新实现,可能是为了保持接口一致,这样有利于原来使用hive thrift的用户平滑的迁移到spark thrift,因为唯一的改动就是切换url,实际上,相同sql下的spark thrift和hive thrift表现还是有很多不同的。

 

posted @ 2018-12-18 15:54  匠人先生  阅读(3415)  评论(0编辑  收藏  举报