measure/src/main/scala/org/apache/griffin/measure/step/transform/DataFrameOps.scala

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to you under the Apache License, Version 2.0 (the
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  http://www.apache.org/licenses/LICENSE-2.0

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package org.apache.griffin.measure.step.transform

import java.util.Date

import org.apache.spark.sql.{Encoders, Row, SQLContext, _}
import org.apache.spark.sql.types._
import org.apache.griffin.measure.context.ContextId
import org.apache.griffin.measure.context.streaming.metric._
import org.apache.griffin.measure.context.streaming.metric.CacheResults.CacheResult
import org.apache.griffin.measure.step.builder.ConstantColumns
import org.apache.griffin.measure.utils.ParamUtil._

/**
  * pre-defined data frame operations
  */
object DataFrameOps {

  final val _fromJson = "from_json"
  final val _accuracy = "accuracy"
  final val _clear = "clear"

  object AccuracyOprKeys {
    val _dfName = "df.name"
    val _miss = "miss"
    val _total = "total"
    val _matched = "matched"
    val _matchedFraction = "matchedFraction"
  }

  def fromJson(sqlContext: SQLContext,
               inputDfName: String,
               details: Map[String, Any]): DataFrame = {
    val _colName = "col.name"
    val colNameOpt = details.get(_colName).map(_.toString)

    implicit val encoder = Encoders.STRING

    val df: DataFrame = sqlContext.table(s"`${inputDfName}`")
    val rdd = colNameOpt match {
      case Some(colName: String) => df.map(r => r.getAs[String](colName))
      case _ => df.map(_.getAs[String](0))
    }
    sqlContext.read.json(rdd) // slow process
  }

  def accuracy(sqlContext: SQLContext,
               inputDfName: String,
               contextId: ContextId,
               details: Map[String, Any]): DataFrame = {
    import AccuracyOprKeys._

    val miss = details.getStringOrKey(_miss)
    val total = details.getStringOrKey(_total)
    val matched = details.getStringOrKey(_matched)
    val matchedFraction = details.getStringOrKey(_matchedFraction)

    val updateTime = new Date().getTime

    def getLong(r: Row, k: String): Option[Long] = {
      try {
        Some(r.getAs[Long](k))
      } catch {
        case e: Throwable => None
      }
    }

    val df = sqlContext.table(s"`${inputDfName}`")

    val results = df.rdd.flatMap { row =>
      try {
        val tmst = getLong(row, ConstantColumns.tmst).getOrElse(contextId.timestamp)
        val missCount = getLong(row, miss).getOrElse(0L)
        val totalCount = getLong(row, total).getOrElse(0L)
        val ar = AccuracyMetric(missCount, totalCount)
        if (ar.isLegal) Some((tmst, ar)) else None
      } catch {
        case e: Throwable => None
      }
    }.collect

    // cache and update results
    val updatedResults = CacheResults.update(results.map{ pair =>
      val (t, r) = pair
      CacheResult(t, updateTime, r)
    })

    // generate metrics
    val schema = StructType(Array(
      StructField(ConstantColumns.tmst, LongType),
      StructField(miss, LongType),
      StructField(total, LongType),
      StructField(matched, LongType),
      StructField(matchedFraction, DoubleType),
      StructField(ConstantColumns.record, BooleanType),
      StructField(ConstantColumns.empty, BooleanType)
    ))
    val rows = updatedResults.map { r =>
      val ar = r.result.asInstanceOf[AccuracyMetric]
      Row(r.timeStamp, ar.miss, ar.total, ar.getMatch, ar.matchFraction, !ar.initial, ar.eventual)
    }.toArray
    val rowRdd = sqlContext.sparkContext.parallelize(rows)
    val retDf = sqlContext.createDataFrame(rowRdd, schema)

    retDf
  }

  def clear(sqlContext: SQLContext, inputDfName: String, details: Map[String, Any]): DataFrame = {
    val df = sqlContext.table(s"`${inputDfName}`")
    val emptyRdd = sqlContext.sparkContext.emptyRDD[Row]
    sqlContext.createDataFrame(emptyRdd, df.schema)
  }

}