za.co.absa.enceladus.examples.CustomRuleSample1.scala Maven / Gradle / Ivy

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/*
 * Copyright 2018 ABSA Group Limited
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package za.co.absa.enceladus.examples

import org.apache.spark.sql.{DataFrame, SparkSession}
import za.co.absa.enceladus.conformance.config.ConformanceConfig
import za.co.absa.enceladus.conformance.interpreter.{DynamicInterpreter, FeatureSwitches}
import za.co.absa.enceladus.dao.MenasDAO
import za.co.absa.enceladus.dao.auth.MenasKerberosCredentials
import za.co.absa.enceladus.dao.rest.RestDaoFactory
import za.co.absa.enceladus.examples.interpreter.rules.custom.UppercaseCustomConformanceRule
import za.co.absa.enceladus.model.Dataset
import za.co.absa.enceladus.utils.time.TimeZoneNormalizer

object CustomRuleSample1 extends CustomRuleSampleFs {

  case class ExampleRow(id: Int, makeUpper: String, leave: String)
  case class OutputRow(id: Int, makeUpper: String, leave: String, doneUpper: String)

  implicit val spark: SparkSession = SparkSession.builder()
    .master("local[*]")
    .appName("CustomRuleSample1")
    .config("spark.sql.codegen.wholeStage", value = false)
    .getOrCreate()
  TimeZoneNormalizer.normalizeAll(spark) //normalize the timezone of JVM and the spark session

  def main(args: Array[String]) {
    // scalastyle:off magic.number
    val menasBaseUrls = List("http://localhost:8080/menas")
    val meansCredentials = MenasKerberosCredentials("[email protected]", "src/main/resources/user.keytab.example")
    implicit val progArgs: ConformanceConfig = ConformanceConfig() // here we may need to specify some parameters (for certain rules)
    implicit val dao: MenasDAO = RestDaoFactory.getInstance(meansCredentials, menasBaseUrls) // you may have to hard-code your own implementation here (if not working with menas)

    val experimentalMR = true
    val isCatalystWorkaroundEnabled = true
    val enableCF: Boolean = false

    val inputData = spark.createDataFrame(
      Seq(
        ExampleRow(1, "Hello world", "What a beautiful place"),
        ExampleRow(4, "One Ring to rule them all", "One Ring to find them"),
        ExampleRow(9, "ALREADY CAPS", "and this is lower-case")
      ))

    val conformanceDef =  Dataset(
      name = "Custom rule sample 1",
      version = 0,
      hdfsPath = "/a/b/c",
      hdfsPublishPath = "/publish/a/b/c",

      schemaName = "Not really used here",

      schemaVersion = 9999,

      conformance = List(
        UppercaseCustomConformanceRule(order = 0,
                                       outputColumn = "doneUpper",
                                       controlCheckpoint = false,
                                       inputColumn = "makeUpper")
      )
    )

    implicit val featureSwitches: FeatureSwitches = FeatureSwitches()
      .setExperimentalMappingRuleEnabled(experimentalMR)
      .setCatalystWorkaroundEnabled(isCatalystWorkaroundEnabled)
      .setControlFrameworkEnabled(enableCF)

    val outputData: DataFrame = DynamicInterpreter().interpret(conformanceDef, inputData)

    outputData.show(false)
    //scalastyle:on magicnumber
  }
}