measure/src/main/scala/org/apache/griffin/measure/context/DQContext.scala

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  http://www.apache.org/licenses/LICENSE-2.0

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package org.apache.griffin.measure.context

import org.apache.spark.sql.{Encoders, SparkSession, SQLContext}

import org.apache.griffin.measure.configuration.dqdefinition._
import org.apache.griffin.measure.configuration.enums._
import org.apache.griffin.measure.datasource._
import org.apache.griffin.measure.sink.{Sink, SinkFactory}

/**
  * dq context: the context of each calculation
  * unique context id in each calculation
  * access the same spark session this app created
  */
case class DQContext(contextId: ContextId,
                     name: String,
                     dataSources: Seq[DataSource],
                     sinkParams: Seq[SinkParam],
                     procType: ProcessType
                    )(@transient implicit val sparkSession: SparkSession) {

  val sqlContext: SQLContext = sparkSession.sqlContext

  val compileTableRegister: CompileTableRegister = CompileTableRegister()
  val runTimeTableRegister: RunTimeTableRegister = RunTimeTableRegister(sqlContext)

  val dataFrameCache: DataFrameCache = DataFrameCache()

  val metricWrapper: MetricWrapper = MetricWrapper(name, sparkSession.sparkContext.applicationId)
  val writeMode = WriteMode.defaultMode(procType)

  val dataSourceNames: Seq[String] = {
    // sort data source names, put baseline data source name to the head
    val (blOpt, others) = dataSources.foldLeft((None: Option[String], Nil: Seq[String])) { (ret, ds) =>
      val (opt, seq) = ret
      if (opt.isEmpty && ds.isBaseline) (Some(ds.name), seq) else (opt, seq :+ ds.name)
    }
    blOpt match {
      case Some(bl) => bl +: others
      case _ => others
    }
  }
  dataSourceNames.foreach(name => compileTableRegister.registerTable(name))

  def getDataSourceName(index: Int): String = {
    if (dataSourceNames.size > index) dataSourceNames(index) else ""
  }

  implicit val encoder = Encoders.STRING
  val functionNames: Seq[String] = sparkSession.catalog.listFunctions.map(_.name).collect.toSeq

  val dataSourceTimeRanges = loadDataSources()

  def loadDataSources(): Map[String, TimeRange] = {
    dataSources.map { ds =>
      (ds.name, ds.loadData(this))
    }.toMap
  }

  printTimeRanges

  private val sinkFactory = SinkFactory(sinkParams, name)
  private val defaultSink: Sink = createSink(contextId.timestamp)

  def getSink(timestamp: Long): Sink = {
    if (timestamp == contextId.timestamp) getSink()
    else createSink(timestamp)
  }

  def getSink(): Sink = defaultSink

  private def createSink(t: Long): Sink = {
    procType match {
      case BatchProcessType => sinkFactory.getSinks(t, true)
      case StreamingProcessType => sinkFactory.getSinks(t, false)
    }
  }

  def cloneDQContext(newContextId: ContextId): DQContext = {
    DQContext(newContextId, name, dataSources, sinkParams, procType)(sparkSession)
  }

  def clean(): Unit = {
    compileTableRegister.unregisterAllTables()
    runTimeTableRegister.unregisterAllTables()

    dataFrameCache.uncacheAllDataFrames()
    dataFrameCache.clearAllTrashDataFrames()
  }

  private def printTimeRanges(): Unit = {
    if (dataSourceTimeRanges.nonEmpty) {
      val timeRangesStr = dataSourceTimeRanges.map { pair =>
        val (name, timeRange) = pair
        s"${name} -> (${timeRange.begin}, ${timeRange.end}]"
      }.mkString(", ")
      println(s"data source timeRanges: ${timeRangesStr}")
    }
  }

}