hudi-spark/src/test/scala/org/apache/hudi/functional/TestStructuredStreaming.scala

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You 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 org.apache.hudi.functional

import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.hudi.common.table.HoodieTableMetaClient
import org.apache.hudi.common.testutils.RawTripTestPayload.recordsToStrings
import org.apache.hudi.config.HoodieWriteConfig
import org.apache.hudi.exception.TableNotFoundException
import org.apache.hudi.testutils.HoodieClientTestBase
import org.apache.hudi.{DataSourceReadOptions, DataSourceWriteOptions, HoodieDataSourceHelpers}
import org.apache.log4j.LogManager
import org.apache.spark.sql._
import org.apache.spark.sql.streaming.{OutputMode, ProcessingTime}
import org.junit.jupiter.api.Assertions.{assertEquals, assertTrue}
import org.junit.jupiter.api.{AfterEach, BeforeEach, Test}

import scala.collection.JavaConversions._
import scala.concurrent.ExecutionContext.Implicits.global
import scala.concurrent.duration.Duration
import scala.concurrent.{Await, Future}

/**
 * Basic tests on the spark datasource for structured streaming sink
 */
class TestStructuredStreaming extends HoodieClientTestBase {
  private val log = LogManager.getLogger(getClass)
  var spark: SparkSession = null
  val commonOpts = Map(
    "hoodie.insert.shuffle.parallelism" -> "4",
    "hoodie.upsert.shuffle.parallelism" -> "4",
    DataSourceWriteOptions.RECORDKEY_FIELD_OPT_KEY -> "_row_key",
    DataSourceWriteOptions.PARTITIONPATH_FIELD_OPT_KEY -> "partition",
    DataSourceWriteOptions.PRECOMBINE_FIELD_OPT_KEY -> "timestamp",
    HoodieWriteConfig.TABLE_NAME -> "hoodie_test"
  )

  @BeforeEach override def setUp() {
    initPath()
    initSparkContexts()
    spark = sqlContext.sparkSession
    initTestDataGenerator()
    initFileSystem()
  }

  @AfterEach override def tearDown() = {
    cleanupSparkContexts()
    cleanupTestDataGenerator()
    cleanupFileSystem()
  }

  @Test
  def testStructuredStreaming(): Unit = {
    fs.delete(new Path(basePath), true)
    val sourcePath = basePath + "/source"
    val destPath = basePath + "/dest"
    fs.mkdirs(new Path(sourcePath))

    // First chunk of data
    val records1 = recordsToStrings(dataGen.generateInserts("000", 100)).toList
    val inputDF1 = spark.read.json(spark.sparkContext.parallelize(records1, 2))

    // Second chunk of data
    val records2 = recordsToStrings(dataGen.generateUpdates("001", 100)).toList
    val inputDF2 = spark.read.json(spark.sparkContext.parallelize(records2, 2))
    val uniqueKeyCnt = inputDF2.select("_row_key").distinct().count()

    // define the source of streaming
    val streamingInput =
      spark.readStream
        .schema(inputDF1.schema)
        .json(sourcePath)

    val f1 = Future {
      println("streaming starting")
      //'writeStream' can be called only on streaming Dataset/DataFrame
      streamingInput
        .writeStream
        .format("org.apache.hudi")
        .options(commonOpts)
        .trigger(new ProcessingTime(100))
        .option("checkpointLocation", basePath + "/checkpoint")
        .outputMode(OutputMode.Append)
        .start(destPath)
        .awaitTermination(10000)
      println("streaming ends")
    }

    val f2 = Future {
      inputDF1.coalesce(1).write.mode(SaveMode.Append).json(sourcePath)
      // wait for spark streaming to process one microbatch
      val currNumCommits = waitTillAtleastNCommits(fs, destPath, 1, 120, 5)
      assertTrue(HoodieDataSourceHelpers.hasNewCommits(fs, destPath, "000"))
      val commitInstantTime1 = HoodieDataSourceHelpers.latestCommit(fs, destPath)
      // Read RO View
      val hoodieROViewDF1 = spark.read.format("org.apache.hudi")
        .load(destPath + "/*/*/*/*")
      assert(hoodieROViewDF1.count() == 100)

      inputDF2.coalesce(1).write.mode(SaveMode.Append).json(sourcePath)
      // wait for spark streaming to process one microbatch
      waitTillAtleastNCommits(fs, destPath, currNumCommits + 1, 120, 5)
      val commitInstantTime2 = HoodieDataSourceHelpers.latestCommit(fs, destPath)
      assertEquals(2, HoodieDataSourceHelpers.listCommitsSince(fs, destPath, "000").size())
      // Read RO View
      val hoodieROViewDF2 = spark.read.format("org.apache.hudi")
        .load(destPath + "/*/*/*/*")
      assertEquals(100, hoodieROViewDF2.count()) // still 100, since we only updated


      // Read Incremental View
      // we have 2 commits, try pulling the first commit (which is not the latest)
      val firstCommit = HoodieDataSourceHelpers.listCommitsSince(fs, destPath, "000").get(0)
      val hoodieIncViewDF1 = spark.read.format("org.apache.hudi")
        .option(DataSourceReadOptions.QUERY_TYPE_OPT_KEY, DataSourceReadOptions.QUERY_TYPE_INCREMENTAL_OPT_VAL)
        .option(DataSourceReadOptions.BEGIN_INSTANTTIME_OPT_KEY, "000")
        .option(DataSourceReadOptions.END_INSTANTTIME_OPT_KEY, firstCommit)
        .load(destPath)
      assertEquals(100, hoodieIncViewDF1.count())
      // 100 initial inserts must be pulled
      var countsPerCommit = hoodieIncViewDF1.groupBy("_hoodie_commit_time").count().collect()
      assertEquals(1, countsPerCommit.length)
      assertEquals(firstCommit, countsPerCommit(0).get(0))

      // pull the latest commit
      val hoodieIncViewDF2 = spark.read.format("org.apache.hudi")
        .option(DataSourceReadOptions.QUERY_TYPE_OPT_KEY, DataSourceReadOptions.QUERY_TYPE_INCREMENTAL_OPT_VAL)
        .option(DataSourceReadOptions.BEGIN_INSTANTTIME_OPT_KEY, commitInstantTime1)
        .load(destPath)

      assertEquals(uniqueKeyCnt, hoodieIncViewDF2.count()) // 100 records must be pulled
      countsPerCommit = hoodieIncViewDF2.groupBy("_hoodie_commit_time").count().collect()
      assertEquals(1, countsPerCommit.length)
      assertEquals(commitInstantTime2, countsPerCommit(0).get(0))
    }
    Await.result(Future.sequence(Seq(f1, f2)), Duration.Inf)
  }

  @throws[InterruptedException]
  private def waitTillAtleastNCommits(fs: FileSystem, tablePath: String,
                                      numCommits: Int, timeoutSecs: Int, sleepSecsAfterEachRun: Int) = {
    val beginTime = System.currentTimeMillis
    var currTime = beginTime
    val timeoutMsecs = timeoutSecs * 1000
    var numInstants = 0
    var success = false
    while ({!success && (currTime - beginTime) < timeoutMsecs}) try {
      val timeline = HoodieDataSourceHelpers.allCompletedCommitsCompactions(fs, tablePath)
      log.info("Timeline :" + timeline.getInstants.toArray)
      if (timeline.countInstants >= numCommits) {
        numInstants = timeline.countInstants
        success = true
      }
      val metaClient = new HoodieTableMetaClient(fs.getConf, tablePath, true)
    } catch {
      case te: TableNotFoundException =>
        log.info("Got table not found exception. Retrying")
    } finally {
      Thread.sleep(sleepSecsAfterEachRun * 1000)
      currTime = System.currentTimeMillis
    }
    if (!success) throw new IllegalStateException("Timed-out waiting for " + numCommits + " commits to appear in " + tablePath)
    numInstants
  }
}