spark sql执行 phoenix 插入upsert 过程代码分析

spark2.2
phoenix 5.8.0

spark中执行phoenix代码如下：

    public void save2Table(SparkSession sparkSession, Dataset<Row> df, String tableName) {
        df
                .write()
                .format("org.apache.phoenix.spark")
                .mode(SaveMode.Overwrite)
                .option("table", tableName)
                .option("zkUrl", phoenixConfig.getUrl())
                .save();
    }

1. 先成save()方法开始入手，进入save方法，执行了DataFrameWriter.scala的runCommand方法，其中参数为SaveIntoDataSourceCommand类的构造()方法：

runCommand(df.sparkSession, "save") {
      SaveIntoDataSourceCommand(
        query = df.logicalPlan,
        provider = source,
        partitionColumns = partitioningColumns.getOrElse(Nil),
        options = extraOptions.toMap,
        mode = mode)

2. 而DataFrameWriter类的runCommand方法，执行了executePlan(command)方法，

private def runCommand(session: SparkSession, name: String)(command: LogicalPlan): Unit = {
    val qe = session.sessionState.executePlan(command)
    try {
      val start = System.nanoTime()
      // call `QueryExecution.toRDD` to trigger the execution of commands.
      qe.toRdd
      val end = System.nanoTime()
      session.listenerManager.onSuccess(name, qe, end - start)
    } catch {
      case e: Exception =>
        session.listenerManager.onFailure(name, qe, e)
        throw e
    }
  }

3. 继续执行SessionState的executePlan()

 def executePlan(plan: LogicalPlan): QueryExecution = createQueryExecution(plan

4. QueryExecution类 执行如下代码：

lazy val toRdd: RDD[InternalRow] = executedPlan.execute()

5. SparkSplan类的execute（）

  final def execute(): RDD[InternalRow] = executeQuery {
    doExecute()
  }

6. SparkSplan类的executeQuery()

  protected final def executeQuery[T](query: => T): T = {
    RDDOperationScope.withScope(sparkContext, nodeName, false, true) {
      prepare()
      waitForSubqueries()
      query
    }
  }

7. ExecutedCommandExec类的doExecute()

  protected override def doExecute(): RDD[InternalRow] = {
    sqlContext.sparkContext.parallelize(sideEffectResult, 1)
  }

8. sideEffectResult的cmd.run()

  protected[sql] lazy val sideEffectResult: Seq[InternalRow] = {
    val converter = CatalystTypeConverters.createToCatalystConverter(schema)
    cmd.run(sqlContext.sparkSession).map(converter(_).asInstanceOf[InternalRow])
  }

9. SaveIntoDataSourceCommand类的run()方法

override def run(sparkSession: SparkSession): Seq[Row] = {
    DataSource(
      sparkSession,
      className = provider,
      partitionColumns = partitionColumns,
      options = options).write(mode, Dataset.ofRows(sparkSession, query))

    Seq.empty[Row]
  }

10. DataSource类的write()方法：

def write(mode: SaveMode, data: DataFrame): Unit = {
    if (data.schema.map(_.dataType).exists(_.isInstanceOf[CalendarIntervalType])) {
      throw new AnalysisException("Cannot save interval data type into external storage.")
    }

    providingClass.newInstance() match {
      case dataSource: CreatableRelationProvider =>
        dataSource.createRelation(sparkSession.sqlContext, mode, caseInsensitiveOptions, data)
      case format: FileFormat =>
        writeInFileFormat(format, mode, data)
      case _ =>
        sys.error(s"${providingClass.getCanonicalName} does not allow create table as select.")
    }
  }

11. Datasource的createRelation()

    providingClass.newInstance() match {
      case dataSource: CreatableRelationProvider =>
        dataSource.createRelation(sparkSession.sqlContext, mode, caseInsensitiveOptions, data)
      case format: FileFormat =>
        writeInFileFormat(format, mode, data)
      case _ =>
        sys.error(s"${providingClass.getCanonicalName} does not allow create table as select.")
    }

12. 终于到了spark-phoenix的类出场了。到这里就开始执行spark-phoenix的DefaultSource的createRelation()

override def createRelation(sqlContext: SQLContext, mode: SaveMode,
                              parameters: Map[String, String], data: DataFrame): BaseRelation = {

    if (!mode.equals(SaveMode.Overwrite)) {
      throw new Exception("SaveMode other than SaveMode.OverWrite is not supported")
    }

    verifyParameters(parameters)

    // Save the DataFrame to Phoenix
    data.saveToPhoenix(parameters("table"), zkUrl = parameters.get("zkUrl"))

    // Return a relation of the saved data
    createRelation(sqlContext, parameters)
  }

13. DataFrameFunctions类的。saveToPhoenix方法。