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package org.apache.griffin.measure.context

import org.apache.spark.sql.{DataFrame, Row}
import org.apache.spark.sql.types._
import org.scalatest._

import org.apache.griffin.measure.SparkSuiteBase

class DataFrameCacheTest extends FlatSpec with Matchers with SparkSuiteBase {

  def createDataFrame(arr: Seq[Int]): DataFrame = {
    val schema = StructType(Array(
      StructField("id", LongType),
      StructField("name", StringType),
      StructField("age", IntegerType)
    ))
    val rows = arr.map { i =>
      Row(i.toLong, s"name_$i", i + 15)
    }
    val rowRdd = spark.sparkContext.parallelize(rows)
    spark.createDataFrame(rowRdd, schema)
  }

  "data frame cache" should "be able to cache and uncache data frames" in {
    val dfCache = DataFrameCache()
    val df1 = createDataFrame(1 to 5)
    val df2 = createDataFrame(1 to 10)
    val df3 = createDataFrame(1 to 15)

    // cache
    dfCache.cacheDataFrame("t1", df1)
    dfCache.cacheDataFrame("t2", df2)
    dfCache.cacheDataFrame("t3", df3)
    dfCache.dataFrames.get("t2") should be (Some(df2))

    // uncache
    dfCache.uncacheDataFrame("t2")
    dfCache.dataFrames.get("t2") should be (None)
    dfCache.trashDataFrames.toList should be (df2 :: Nil)

    // uncache all
    dfCache.uncacheAllDataFrames()
    dfCache.dataFrames.toMap should be (Map[String, DataFrame]())
    dfCache.trashDataFrames.toList should be (df2 :: df1 :: df3 :: Nil)

    // clear all trash
    dfCache.clearAllTrashDataFrames()
    dfCache.trashDataFrames.toList should be (Nil)
  }


}