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[HUDI-4116] Unify clustering/compaction related procedures' output type (#5620)

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* Unify clustering/compaction related procedures' output type

* Address review comments
This commit is contained in:
huberylee
2022-05-19 09:48:03 +08:00
committed by GitHub
parent 551aa959c5
commit 6573469e73
11 changed files with 247 additions and 113 deletions
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15
hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/hudi/HoodieCLIUtils.scala
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@@ -19,14 +19,14 @@
package org.apache.hudi
import org.apache.hudi.avro.model.HoodieClusteringGroup
import org.apache.hudi.client.SparkRDDWriteClient
import org.apache.hudi.common.table.{HoodieTableMetaClient, TableSchemaResolver}
import org.apache.spark.api.java.JavaSparkContext
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.hudi.HoodieSqlCommonUtils.withSparkConf
import scala.collection.JavaConverters.mapAsJavaMapConverter
import scala.collection.immutable.Map
import scala.collection.JavaConverters.{collectionAsScalaIterableConverter, mapAsJavaMapConverter}
object HoodieCLIUtils {
@@ -46,4 +46,15 @@ object HoodieCLIUtils {
DataSourceUtils.createHoodieClient(jsc, schemaStr, basePath,
metaClient.getTableConfig.getTableName, finalParameters.asJava)
}
def extractPartitions(clusteringGroups: Seq[HoodieClusteringGroup]): String = {
var partitionPaths: Seq[String] = Seq.empty
clusteringGroups.foreach(g =>
g.getSlices.asScala.foreach(slice =>
partitionPaths = partitionPaths :+ slice.getPartitionPath
)
)
partitionPaths.sorted.mkString(",")
}
}

11
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/spark/sql/hudi/command/CompactionHoodiePathCommand.scala
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@@ -19,11 +19,9 @@ package org.apache.spark.sql.hudi.command
import org.apache.hudi.common.model.HoodieTableType
import org.apache.hudi.common.table.HoodieTableMetaClient
import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference}
import org.apache.spark.sql.catalyst.expressions.Attribute
import org.apache.spark.sql.catalyst.plans.logical.CompactionOperation.{CompactionOperation, RUN, SCHEDULE}
import org.apache.spark.sql.hudi.command.procedures.{HoodieProcedureUtils, RunCompactionProcedure}
import org.apache.spark.sql.types.StringType
import org.apache.spark.sql.{Row, SparkSession}
import org.apache.spark.unsafe.types.UTF8String
@@ -50,10 +48,5 @@ case class CompactionHoodiePathCommand(path: String,
RunCompactionProcedure.builder.get().build.call(procedureArgs)
}
override val output: Seq[Attribute] = {
operation match {
case RUN => Seq.empty
case SCHEDULE => Seq(AttributeReference("instant", StringType, nullable = false)())
}
}
override val output: Seq[Attribute] = RunCompactionProcedure.builder.get().build.outputType.toAttributes
}

13
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/spark/sql/hudi/command/CompactionHoodieTableCommand.scala
View File

@@ -18,10 +18,10 @@
package org.apache.spark.sql.hudi.command
import org.apache.spark.sql.catalyst.catalog.CatalogTable
import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference}
import org.apache.spark.sql.catalyst.plans.logical.CompactionOperation.{CompactionOperation, RUN, SCHEDULE}
import org.apache.spark.sql.catalyst.expressions.Attribute
import org.apache.spark.sql.catalyst.plans.logical.CompactionOperation.CompactionOperation
import org.apache.spark.sql.hudi.HoodieSqlCommonUtils.getTableLocation
import org.apache.spark.sql.types.StringType
import org.apache.spark.sql.hudi.command.procedures.RunCompactionProcedure
import org.apache.spark.sql.{Row, SparkSession}
@Deprecated
@@ -35,10 +35,5 @@ case class CompactionHoodieTableCommand(table: CatalogTable,
CompactionHoodiePathCommand(basePath, operation, instantTimestamp).run(sparkSession)
}
override val output: Seq[Attribute] = {
operation match {
case RUN => Seq.empty
case SCHEDULE => Seq(AttributeReference("instant", StringType, nullable = false)())
}
}
override val output: Seq[Attribute] = RunCompactionProcedure.builder.get().build.outputType.toAttributes
}

12
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/spark/sql/hudi/command/CompactionShowHoodiePathCommand.scala
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@@ -19,10 +19,8 @@ package org.apache.spark.sql.hudi.command
import org.apache.hudi.common.model.HoodieTableType
import org.apache.hudi.common.table.HoodieTableMetaClient
import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference}
import org.apache.spark.sql.catalyst.expressions.Attribute
import org.apache.spark.sql.hudi.command.procedures.{HoodieProcedureUtils, ShowCompactionProcedure}
import org.apache.spark.sql.types.{IntegerType, StringType}
import org.apache.spark.sql.{Row, SparkSession}
import org.apache.spark.unsafe.types.UTF8String
@@ -42,11 +40,5 @@ case class CompactionShowHoodiePathCommand(path: String, limit: Int)
ShowCompactionProcedure.builder.get().build.call(procedureArgs)
}
override val output: Seq[Attribute] = {
Seq(
AttributeReference("instant", StringType, nullable = false)(),
AttributeReference("action", StringType, nullable = false)(),
AttributeReference("size", IntegerType, nullable = false)()
)
}
override val output: Seq[Attribute] = ShowCompactionProcedure.builder.get().build.outputType.toAttributes
}

12
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/spark/sql/hudi/command/CompactionShowHoodieTableCommand.scala
View File

@@ -18,9 +18,9 @@
package org.apache.spark.sql.hudi.command
import org.apache.spark.sql.catalyst.catalog.CatalogTable
import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference}
import org.apache.spark.sql.catalyst.expressions.Attribute
import org.apache.spark.sql.hudi.HoodieSqlCommonUtils.getTableLocation
import org.apache.spark.sql.types.{IntegerType, StringType}
import org.apache.spark.sql.hudi.command.procedures.ShowCompactionProcedure
import org.apache.spark.sql.{Row, SparkSession}
@Deprecated
@@ -32,11 +32,5 @@ case class CompactionShowHoodieTableCommand(table: CatalogTable, limit: Int)
CompactionShowHoodiePathCommand(basePath, limit).run(sparkSession)
}
override val output: Seq[Attribute] = {
Seq(
AttributeReference("timestamp", StringType, nullable = false)(),
AttributeReference("action", StringType, nullable = false)(),
AttributeReference("size", IntegerType, nullable = false)()
)
}
override val output: Seq[Attribute] = ShowCompactionProcedure.builder.get().build.outputType.toAttributes
}

34
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/spark/sql/hudi/command/procedures/RunClusteringProcedure.scala
View File

@@ -18,7 +18,7 @@
package org.apache.spark.sql.hudi.command.procedures
import org.apache.hudi.DataSourceReadOptions.{QUERY_TYPE, QUERY_TYPE_SNAPSHOT_OPT_VAL}
import org.apache.hudi.common.table.timeline.HoodieActiveTimeline
import org.apache.hudi.common.table.timeline.{HoodieActiveTimeline, HoodieTimeline}
import org.apache.hudi.common.table.{HoodieTableMetaClient, TableSchemaResolver}
import org.apache.hudi.common.util.ValidationUtils.checkArgument
import org.apache.hudi.common.util.{ClusteringUtils, Option => HOption}
@@ -32,6 +32,7 @@ import org.apache.spark.sql.execution.datasources.FileStatusCache
import org.apache.spark.sql.types._
import java.util.function.Supplier
import scala.collection.JavaConverters._
class RunClusteringProcedure extends BaseProcedure
@@ -50,13 +51,15 @@ class RunClusteringProcedure extends BaseProcedure
ProcedureParameter.optional(0, "table", DataTypes.StringType, None),
ProcedureParameter.optional(1, "path", DataTypes.StringType, None),
ProcedureParameter.optional(2, "predicate", DataTypes.StringType, None),
ProcedureParameter.optional(3, "order", DataTypes.StringType, None)
ProcedureParameter.optional(3, "order", DataTypes.StringType, None),
ProcedureParameter.optional(4, "show_involved_partition", DataTypes.BooleanType, false)
)
private val OUTPUT_TYPE = new StructType(Array[StructField](
StructField("timestamp", DataTypes.StringType, nullable = true, Metadata.empty),
StructField("partition", DataTypes.StringType, nullable = true, Metadata.empty),
StructField("groups", DataTypes.IntegerType, nullable = true, Metadata.empty)
StructField("input_group_size", DataTypes.IntegerType, nullable = true, Metadata.empty),
StructField("state", DataTypes.StringType, nullable = true, Metadata.empty),
StructField("involved_partitions", DataTypes.StringType, nullable = true, Metadata.empty)
))
def parameters: Array[ProcedureParameter] = PARAMETERS
@@ -70,6 +73,7 @@ class RunClusteringProcedure extends BaseProcedure
val tablePath = getArgValueOrDefault(args, PARAMETERS(1))
val predicate = getArgValueOrDefault(args, PARAMETERS(2))
val orderColumns = getArgValueOrDefault(args, PARAMETERS(3))
val showInvolvedPartitions = getArgValueOrDefault(args, PARAMETERS(4)).get.asInstanceOf[Boolean]
val basePath: String = getBasePath(tableName, tablePath)
val metaClient = HoodieTableMetaClient.builder.setConf(jsc.hadoopConfiguration()).setBasePath(basePath).build
@@ -114,7 +118,27 @@ class RunClusteringProcedure extends BaseProcedure
pendingClustering.foreach(client.cluster(_, true))
logInfo(s"Finish clustering all the instants: ${pendingClustering.mkString(",")}," +
s" time cost: ${System.currentTimeMillis() - startTs}ms.")
Seq.empty[Row]
val clusteringInstants = metaClient.reloadActiveTimeline().getInstants.iterator().asScala
.filter(p => p.getAction == HoodieTimeline.REPLACE_COMMIT_ACTION && pendingClustering.contains(p.getTimestamp))
.toSeq
.sortBy(f => f.getTimestamp)
.reverse
val clusteringPlans = clusteringInstants.map(instant =>
ClusteringUtils.getClusteringPlan(metaClient, instant)
)
if (showInvolvedPartitions) {
clusteringPlans.map { p =>
Row(p.get().getLeft.getTimestamp, p.get().getRight.getInputGroups.size(),
p.get().getLeft.getState.name(), HoodieCLIUtils.extractPartitions(p.get().getRight.getInputGroups.asScala))
}
} else {
clusteringPlans.map { p =>
Row(p.get().getLeft.getTimestamp, p.get().getRight.getInputGroups.size(), p.get().getLeft.getState.name(), "*")
}
}
}
override def build: Procedure = new RunClusteringProcedure()

29
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/spark/sql/hudi/command/procedures/RunCompactionProcedure.scala
View File

@@ -20,10 +20,9 @@ package org.apache.spark.sql.hudi.command.procedures
import org.apache.hudi.common.model.HoodieCommitMetadata
import org.apache.hudi.common.table.HoodieTableMetaClient
import org.apache.hudi.common.table.timeline.{HoodieActiveTimeline, HoodieTimeline}
import org.apache.hudi.common.util.{HoodieTimer, Option => HOption}
import org.apache.hudi.common.util.{CompactionUtils, HoodieTimer, Option => HOption}
import org.apache.hudi.exception.HoodieException
import org.apache.hudi.{HoodieCLIUtils, SparkAdapterSupport}
import org.apache.spark.internal.Logging
import org.apache.spark.sql.Row
import org.apache.spark.sql.types._
@@ -47,7 +46,9 @@ class RunCompactionProcedure extends BaseProcedure with ProcedureBuilder with Sp
)
private val OUTPUT_TYPE = new StructType(Array[StructField](
StructField("instant", DataTypes.StringType, nullable = true, Metadata.empty)
StructField("timestamp", DataTypes.StringType, nullable = true, Metadata.empty),
StructField("operation_size", DataTypes.IntegerType, nullable = true, Metadata.empty),
StructField("state", DataTypes.StringType, nullable = true, Metadata.empty)
))
def parameters: Array[ProcedureParameter] = PARAMETERS
@@ -66,13 +67,12 @@ class RunCompactionProcedure extends BaseProcedure with ProcedureBuilder with Sp
val metaClient = HoodieTableMetaClient.builder.setConf(jsc.hadoopConfiguration()).setBasePath(basePath).build
val client = HoodieCLIUtils.createHoodieClientFromPath(sparkSession, basePath, Map.empty)
var willCompactionInstants: Seq[String] = Seq.empty
operation match {
case "schedule" =>
val instantTime = instantTimestamp.map(_.toString).getOrElse(HoodieActiveTimeline.createNewInstantTime)
if (client.scheduleCompactionAtInstant(instantTime, HOption.empty[java.util.Map[String, String]])) {
Seq(Row(instantTime))
} else {
Seq.empty[Row]
willCompactionInstants = Seq(instantTime)
}
case "run" =>
// Do compaction
@@ -81,7 +81,7 @@ class RunCompactionProcedure extends BaseProcedure with ProcedureBuilder with Sp
.filter(p => p.getAction == HoodieTimeline.COMPACTION_ACTION)
.map(_.getTimestamp)
.toSeq.sortBy(f => f)
val willCompactionInstants = if (instantTimestamp.isEmpty) {
willCompactionInstants = if (instantTimestamp.isEmpty) {
if (pendingCompactionInstants.nonEmpty) {
pendingCompactionInstants
} else { // If there are no pending compaction, schedule to generate one.
@@ -102,9 +102,9 @@ class RunCompactionProcedure extends BaseProcedure with ProcedureBuilder with Sp
s"$basePath, Available pending compaction instants are: ${pendingCompactionInstants.mkString(",")} ")
}
}
if (willCompactionInstants.isEmpty) {
logInfo(s"No need to compaction on $basePath")
Seq.empty[Row]
} else {
logInfo(s"Run compaction at instants: [${willCompactionInstants.mkString(",")}] on $basePath")
val timer = new HoodieTimer
@@ -116,10 +116,21 @@ class RunCompactionProcedure extends BaseProcedure with ProcedureBuilder with Sp
}
logInfo(s"Finish Run compaction at instants: [${willCompactionInstants.mkString(",")}]," +
s" spend: ${timer.endTimer()}ms")
Seq.empty[Row]
}
case _ => throw new UnsupportedOperationException(s"Unsupported compaction operation: $operation")
}
val compactionInstants = metaClient.reloadActiveTimeline().getInstants.iterator().asScala
.filter(instant => willCompactionInstants.contains(instant.getTimestamp))
.toSeq
.sortBy(p => p.getTimestamp)
.reverse
compactionInstants.map(instant =>
(instant, CompactionUtils.getCompactionPlan(metaClient, instant.getTimestamp))
).map { case (instant, plan) =>
Row(instant.getTimestamp, plan.getOperations.size(), instant.getState.name())
}
}
private def handleResponse(metadata: HoodieCommitMetadata): Unit = {

37
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/spark/sql/hudi/command/procedures/ShowClusteringProcedure.scala
View File

@@ -17,26 +17,31 @@
package org.apache.spark.sql.hudi.command.procedures
import org.apache.hudi.SparkAdapterSupport
import org.apache.hudi.{HoodieCLIUtils, SparkAdapterSupport}
import org.apache.hudi.common.table.HoodieTableMetaClient
import org.apache.hudi.common.table.timeline.HoodieTimeline
import org.apache.hudi.common.util.ClusteringUtils
import org.apache.spark.internal.Logging
import org.apache.spark.sql.Row
import org.apache.spark.sql.types._
import java.util.function.Supplier
import scala.collection.JavaConverters._
class ShowClusteringProcedure extends BaseProcedure with ProcedureBuilder with SparkAdapterSupport with Logging {
private val PARAMETERS = Array[ProcedureParameter](
ProcedureParameter.optional(0, "table", DataTypes.StringType, None),
ProcedureParameter.optional(1, "path", DataTypes.StringType, None),
ProcedureParameter.optional(2, "limit", DataTypes.IntegerType, 20)
ProcedureParameter.optional(2, "limit", DataTypes.IntegerType, 20),
ProcedureParameter.optional(3, "show_involved_partition", DataTypes.BooleanType, false)
)
private val OUTPUT_TYPE = new StructType(Array[StructField](
StructField("timestamp", DataTypes.StringType, nullable = true, Metadata.empty),
StructField("groups", DataTypes.IntegerType, nullable = true, Metadata.empty)
StructField("input_group_size", DataTypes.IntegerType, nullable = true, Metadata.empty),
StructField("state", DataTypes.StringType, nullable = true, Metadata.empty),
StructField("involved_partitions", DataTypes.StringType, nullable = true, Metadata.empty)
))
def parameters: Array[ProcedureParameter] = PARAMETERS
@@ -49,12 +54,32 @@ class ShowClusteringProcedure extends BaseProcedure with ProcedureBuilder with S
val tableName = getArgValueOrDefault(args, PARAMETERS(0))
val tablePath = getArgValueOrDefault(args, PARAMETERS(1))
val limit = getArgValueOrDefault(args, PARAMETERS(2)).get.asInstanceOf[Int]
val showInvolvedPartitions = getArgValueOrDefault(args, PARAMETERS(3)).get.asInstanceOf[Boolean]
val basePath: String = getBasePath(tableName, tablePath)
val metaClient = HoodieTableMetaClient.builder.setConf(jsc.hadoopConfiguration()).setBasePath(basePath).build
ClusteringUtils.getAllPendingClusteringPlans(metaClient).iterator().asScala.map { p =>
Row(p.getLeft.getTimestamp, p.getRight.getInputGroups.size())
}.toSeq.take(limit)
val clusteringInstants = metaClient.getActiveTimeline.getInstants.iterator().asScala
.filter(p => p.getAction == HoodieTimeline.REPLACE_COMMIT_ACTION)
.toSeq
.sortBy(f => f.getTimestamp)
.reverse
.take(limit)
val clusteringPlans = clusteringInstants.map(instant =>
ClusteringUtils.getClusteringPlan(metaClient, instant)
)
if (showInvolvedPartitions) {
clusteringPlans.map { p =>
Row(p.get().getLeft.getTimestamp, p.get().getRight.getInputGroups.size(),
p.get().getLeft.getState.name(), HoodieCLIUtils.extractPartitions(p.get().getRight.getInputGroups.asScala))
}
} else {
clusteringPlans.map { p =>
Row(p.get().getLeft.getTimestamp, p.get().getRight.getInputGroups.size(),
p.get().getLeft.getState.name(), "*")
}
}
}
override def build: Procedure = new ShowClusteringProcedure()

16
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/spark/sql/hudi/command/procedures/ShowCompactionProcedure.scala
View File

@@ -44,8 +44,8 @@ class ShowCompactionProcedure extends BaseProcedure with ProcedureBuilder with S
private val OUTPUT_TYPE = new StructType(Array[StructField](
StructField("timestamp", DataTypes.StringType, nullable = true, Metadata.empty),
StructField("action", DataTypes.StringType, nullable = true, Metadata.empty),
StructField("size", DataTypes.IntegerType, nullable = true, Metadata.empty)
StructField("operation_size", DataTypes.IntegerType, nullable = true, Metadata.empty),
StructField("state", DataTypes.StringType, nullable = true, Metadata.empty)
))
def parameters: Array[ProcedureParameter] = PARAMETERS
@@ -64,17 +64,17 @@ class ShowCompactionProcedure extends BaseProcedure with ProcedureBuilder with S
assert(metaClient.getTableType == HoodieTableType.MERGE_ON_READ,
s"Cannot show compaction on a Non Merge On Read table.")
val timeLine = metaClient.getActiveTimeline
val compactionInstants = timeLine.getInstants.iterator().asScala
val compactionInstants = metaClient.getActiveTimeline.getInstants.iterator().asScala
.filter(p => p.getAction == HoodieTimeline.COMPACTION_ACTION)
.toSeq
.sortBy(f => f.getTimestamp)
.reverse
.take(limit)
val compactionPlans = compactionInstants.map(instant =>
(instant, CompactionUtils.getCompactionPlan(metaClient, instant.getTimestamp)))
compactionPlans.map { case (instant, plan) =>
Row(instant.getTimestamp, instant.getAction, plan.getOperations.size())
compactionInstants.map(instant =>
(instant, CompactionUtils.getCompactionPlan(metaClient, instant.getTimestamp))
).map { case (instant, plan) =>
Row(instant.getTimestamp, plan.getOperations.size(), instant.getState.name())
}
}

103
hudi-spark-datasource/hudi-spark/src/test/scala/org/apache/spark/sql/hudi/procedure/TestClusteringProcedure.scala
View File

@@ -20,10 +20,9 @@
package org.apache.spark.sql.hudi.procedure
import org.apache.hadoop.fs.Path
import org.apache.hudi.common.table.timeline.{HoodieActiveTimeline, HoodieTimeline}
import org.apache.hudi.common.table.timeline.{HoodieActiveTimeline, HoodieInstant, HoodieTimeline}
import org.apache.hudi.common.util.{Option => HOption}
import org.apache.hudi.{HoodieCLIUtils, HoodieDataSourceHelpers}
import org.apache.spark.sql.hudi.HoodieSparkSqlTestBase
import scala.collection.JavaConverters.asScalaIteratorConverter
@@ -64,28 +63,22 @@ class TestClusteringProcedure extends HoodieSparkSqlTestBase {
val secondScheduleInstant = HoodieActiveTimeline.createNewInstantTime
client.scheduleClusteringAtInstant(secondScheduleInstant, HOption.empty())
checkAnswer(s"call show_clustering('$tableName')")(
Seq(firstScheduleInstant, 3),
Seq(secondScheduleInstant, 1)
Seq(secondScheduleInstant, 1, HoodieInstant.State.REQUESTED.name(), "*"),
Seq(firstScheduleInstant, 3, HoodieInstant.State.REQUESTED.name(), "*")
)
// Do clustering for all clustering plan generated above, and no new clustering
// instant will be generated because of there is no commit after the second
// clustering plan generated
spark.sql(s"call run_clustering(table => '$tableName', order => 'ts')")
checkAnswer(s"call run_clustering(table => '$tableName', order => 'ts', show_involved_partition => true)")(
Seq(secondScheduleInstant, 1, HoodieInstant.State.COMPLETED.name(), "ts=1003"),
Seq(firstScheduleInstant, 3, HoodieInstant.State.COMPLETED.name(), "ts=1000,ts=1001,ts=1002")
)
// No new commits
val fs = new Path(basePath).getFileSystem(spark.sessionState.newHadoopConf())
assertResult(false)(HoodieDataSourceHelpers.hasNewCommits(fs, basePath, secondScheduleInstant))
checkAnswer(s"select id, name, price, ts from $tableName order by id")(
Seq(1, "a1", 10.0, 1000),
Seq(2, "a2", 10.0, 1001),
Seq(3, "a3", 10.0, 1002),
Seq(4, "a4", 10.0, 1003)
)
// After clustering there should be no pending clustering.
checkAnswer(s"call show_clustering(table => '$tableName')")()
// Check the number of finished clustering instants
val finishedClustering = HoodieDataSourceHelpers.allCompletedCommitsCompactions(fs, basePath)
.getInstants
@@ -94,10 +87,23 @@ class TestClusteringProcedure extends HoodieSparkSqlTestBase {
.toSeq
assertResult(2)(finishedClustering.size)
checkAnswer(s"select id, name, price, ts from $tableName order by id")(
Seq(1, "a1", 10.0, 1000),
Seq(2, "a2", 10.0, 1001),
Seq(3, "a3", 10.0, 1002),
Seq(4, "a4", 10.0, 1003)
)
// After clustering there should be no pending clustering and all clustering instants should be completed
checkAnswer(s"call show_clustering(table => '$tableName')")(
Seq(secondScheduleInstant, 1, HoodieInstant.State.COMPLETED.name(), "*"),
Seq(firstScheduleInstant, 3, HoodieInstant.State.COMPLETED.name(), "*")
)
// Do clustering without manual schedule(which will do the schedule if no pending clustering exists)
spark.sql(s"insert into $tableName values(5, 'a5', 10, 1004)")
spark.sql(s"insert into $tableName values(6, 'a6', 10, 1005)")
spark.sql(s"call run_clustering(table => '$tableName', order => 'ts')")
spark.sql(s"call run_clustering(table => '$tableName', order => 'ts', show_involved_partition => true)").show()
val thirdClusteringInstant = HoodieDataSourceHelpers.allCompletedCommitsCompactions(fs, basePath)
.findInstantsAfter(secondScheduleInstant)
@@ -142,7 +148,7 @@ class TestClusteringProcedure extends HoodieSparkSqlTestBase {
| location '$basePath'
""".stripMargin)
spark.sql(s"call run_clustering(path => '$basePath')")
spark.sql(s"call run_clustering(path => '$basePath')").show()
checkAnswer(s"call show_clustering(path => '$basePath')")()
spark.sql(s"insert into $tableName values(1, 'a1', 10, 1000)")
@@ -152,18 +158,22 @@ class TestClusteringProcedure extends HoodieSparkSqlTestBase {
// Generate the first clustering plan
val firstScheduleInstant = HoodieActiveTimeline.createNewInstantTime
client.scheduleClusteringAtInstant(firstScheduleInstant, HOption.empty())
checkAnswer(s"call show_clustering(path => '$basePath')")(
Seq(firstScheduleInstant, 3)
checkAnswer(s"call show_clustering(path => '$basePath', show_involved_partition => true)")(
Seq(firstScheduleInstant, 3, HoodieInstant.State.REQUESTED.name(), "ts=1000,ts=1001,ts=1002")
)
// Do clustering for all the clustering plan
spark.sql(s"call run_clustering(path => '$basePath', order => 'ts')")
checkAnswer(s"call run_clustering(path => '$basePath', order => 'ts')")(
Seq(firstScheduleInstant, 3, HoodieInstant.State.COMPLETED.name(), "*")
)
checkAnswer(s"select id, name, price, ts from $tableName order by id")(
Seq(1, "a1", 10.0, 1000),
Seq(2, "a2", 10.0, 1001),
Seq(3, "a3", 10.0, 1002)
)
val fs = new Path(basePath).getFileSystem(spark.sessionState.newHadoopConf())
HoodieDataSourceHelpers.hasNewCommits(fs, basePath, firstScheduleInstant)
assertResult(false)(HoodieDataSourceHelpers.hasNewCommits(fs, basePath, firstScheduleInstant))
// Check the number of finished clustering instants
var finishedClustering = HoodieDataSourceHelpers.allCompletedCommitsCompactions(fs, basePath)
@@ -176,7 +186,12 @@ class TestClusteringProcedure extends HoodieSparkSqlTestBase {
// Do clustering without manual schedule(which will do the schedule if no pending clustering exists)
spark.sql(s"insert into $tableName values(4, 'a4', 10, 1003)")
spark.sql(s"insert into $tableName values(5, 'a5', 10, 1004)")
spark.sql(s"call run_clustering(table => '$tableName', predicate => 'ts >= 1003L')")
val resultA = spark.sql(s"call run_clustering(table => '$tableName', predicate => 'ts >= 1003L', show_involved_partition => true)")
.collect()
.map(row => Seq(row.getString(0), row.getInt(1), row.getString(2), row.getString(3)))
assertResult(1)(resultA.length)
assertResult("ts=1003,ts=1004")(resultA(0)(3))
checkAnswer(s"select id, name, price, ts from $tableName order by id")(
Seq(1, "a1", 10.0, 1000),
Seq(2, "a2", 10.0, 1001),
@@ -220,6 +235,8 @@ class TestClusteringProcedure extends HoodieSparkSqlTestBase {
val fs = new Path(basePath).getFileSystem(spark.sessionState.newHadoopConf())
// Test partition pruning with single predicate
var resultA: Array[Seq[Any]] = Array.empty
{
spark.sql(s"insert into $tableName values(1, 'a1', 10, 1000)")
spark.sql(s"insert into $tableName values(2, 'a2', 10, 1001)")
@@ -230,7 +247,11 @@ class TestClusteringProcedure extends HoodieSparkSqlTestBase {
)("Only partition predicates are allowed")
// Do clustering table with partition predicate
spark.sql(s"call run_clustering(table => '$tableName', predicate => 'ts <= 1001L', order => 'ts')")
resultA = spark.sql(s"call run_clustering(table => '$tableName', predicate => 'ts <= 1001L', order => 'ts', show_involved_partition => true)")
.collect()
.map(row => Seq(row.getString(0), row.getInt(1), row.getString(2), row.getString(3)))
assertResult(1)(resultA.length)
assertResult("ts=1000,ts=1001")(resultA(0)(3))
// There is 1 completed clustering instant
val clusteringInstants = HoodieDataSourceHelpers.allCompletedCommitsCompactions(fs, basePath)
@@ -245,9 +266,12 @@ class TestClusteringProcedure extends HoodieSparkSqlTestBase {
val clusteringPlan = HoodieDataSourceHelpers.getClusteringPlan(fs, basePath, clusteringInstant.getTimestamp)
assertResult(true)(clusteringPlan.isPresent)
assertResult(2)(clusteringPlan.get().getInputGroups.size())
assertResult(resultA(0)(1))(clusteringPlan.get().getInputGroups.size())
// No pending clustering instant
checkAnswer(s"call show_clustering(table => '$tableName')")()
// All clustering instants are completed
checkAnswer(s"call show_clustering(table => '$tableName', show_involved_partition => true)")(
Seq(resultA(0).head, resultA(0)(1), HoodieInstant.State.COMPLETED.name(), "ts=1000,ts=1001")
)
checkAnswer(s"select id, name, price, ts from $tableName order by id")(
Seq(1, "a1", 10.0, 1000),
@@ -257,6 +281,8 @@ class TestClusteringProcedure extends HoodieSparkSqlTestBase {
}
// Test partition pruning with {@code And} predicates
var resultB: Array[Seq[Any]] = Array.empty
{
spark.sql(s"insert into $tableName values(4, 'a4', 10, 1003)")
spark.sql(s"insert into $tableName values(5, 'a5', 10, 1004)")
@@ -267,7 +293,11 @@ class TestClusteringProcedure extends HoodieSparkSqlTestBase {
)("Only partition predicates are allowed")
// Do clustering table with partition predicate
spark.sql(s"call run_clustering(table => '$tableName', predicate => 'ts > 1001L and ts <= 1005L', order => 'ts')")
resultB = spark.sql(s"call run_clustering(table => '$tableName', predicate => 'ts > 1001L and ts <= 1005L', order => 'ts', show_involved_partition => true)")
.collect()
.map(row => Seq(row.getString(0), row.getInt(1), row.getString(2), row.getString(3)))
assertResult(1)(resultB.length)
assertResult("ts=1002,ts=1003,ts=1004,ts=1005")(resultB(0)(3))
// There are 2 completed clustering instants
val clusteringInstants = HoodieDataSourceHelpers.allCompletedCommitsCompactions(fs, basePath)
@@ -283,8 +313,11 @@ class TestClusteringProcedure extends HoodieSparkSqlTestBase {
assertResult(true)(clusteringPlan.isPresent)
assertResult(4)(clusteringPlan.get().getInputGroups.size())
// No pending clustering instant
checkAnswer(s"call show_clustering(table => '$tableName')")()
// All clustering instants are completed
checkAnswer(s"call show_clustering(table => '$tableName', show_involved_partition => true)")(
Seq(resultA(0).head, resultA(0)(1), HoodieInstant.State.COMPLETED.name(), "ts=1000,ts=1001"),
Seq(resultB(0).head, resultB(0)(1), HoodieInstant.State.COMPLETED.name(), "ts=1002,ts=1003,ts=1004,ts=1005")
)
checkAnswer(s"select id, name, price, ts from $tableName order by id")(
Seq(1, "a1", 10.0, 1000),
@@ -297,6 +330,8 @@ class TestClusteringProcedure extends HoodieSparkSqlTestBase {
}
// Test partition pruning with {@code And}-{@code Or} predicates
var resultC: Array[Seq[Any]] = Array.empty
{
spark.sql(s"insert into $tableName values(7, 'a7', 10, 1006)")
spark.sql(s"insert into $tableName values(8, 'a8', 10, 1007)")
@@ -308,7 +343,11 @@ class TestClusteringProcedure extends HoodieSparkSqlTestBase {
)("Only partition predicates are allowed")
// Do clustering table with partition predicate
spark.sql(s"call run_clustering(table => '$tableName', predicate => '(ts >= 1006L and ts < 1008L) or ts >= 1009L', order => 'ts')")
resultC = spark.sql(s"call run_clustering(table => '$tableName', predicate => '(ts >= 1006L and ts < 1008L) or ts >= 1009L', order => 'ts', show_involved_partition => true)")
.collect()
.map(row => Seq(row.getString(0), row.getInt(1), row.getString(2), row.getString(3)))
assertResult(1)(resultC.length)
assertResult("ts=1006,ts=1007,ts=1009")(resultC(0)(3))
// There are 3 completed clustering instants
val clusteringInstants = HoodieDataSourceHelpers.allCompletedCommitsCompactions(fs, basePath)
@@ -324,8 +363,12 @@ class TestClusteringProcedure extends HoodieSparkSqlTestBase {
assertResult(true)(clusteringPlan.isPresent)
assertResult(3)(clusteringPlan.get().getInputGroups.size())
// No pending clustering instant
checkAnswer(s"call show_clustering(table => '$tableName')")()
// All clustering instants are completed
checkAnswer(s"call show_clustering(table => '$tableName', show_involved_partition => true)")(
Seq(resultA(0).head, resultA(0)(1), HoodieInstant.State.COMPLETED.name(), "ts=1000,ts=1001"),
Seq(resultB(0).head, resultB(0)(1), HoodieInstant.State.COMPLETED.name(), "ts=1002,ts=1003,ts=1004,ts=1005"),
Seq(resultC(0).head, resultC(0)(1), HoodieInstant.State.COMPLETED.name(), "ts=1006,ts=1007,ts=1009")
)
checkAnswer(s"select id, name, price, ts from $tableName order by id")(
Seq(1, "a1", 10.0, 1000),

78
hudi-spark-datasource/hudi-spark/src/test/scala/org/apache/spark/sql/hudi/procedure/TestCompactionProcedure.scala
View File

@@ -19,6 +19,7 @@
package org.apache.spark.sql.hudi.procedure
import org.apache.hudi.common.table.timeline.HoodieInstant
import org.apache.spark.sql.hudi.HoodieSparkSqlTestBase
class TestCompactionProcedure extends HoodieSparkSqlTestBase {
@@ -48,22 +49,52 @@ class TestCompactionProcedure extends HoodieSparkSqlTestBase {
spark.sql(s"insert into $tableName values(4, 'a4', 10, 1000)")
spark.sql(s"update $tableName set price = 11 where id = 1")
spark.sql(s"call run_compaction(op => 'schedule', table => '$tableName')")
// Schedule the first compaction
val resultA = spark.sql(s"call run_compaction(op => 'schedule', table => '$tableName')")
.collect()
.map(row => Seq(row.getString(0), row.getInt(1), row.getString(2)))
spark.sql(s"update $tableName set price = 12 where id = 2")
spark.sql(s"call run_compaction('schedule', '$tableName')")
val compactionRows = spark.sql(s"call show_compaction(table => '$tableName', limit => 10)").collect()
val timestamps = compactionRows.map(_.getString(0))
// Schedule the second compaction
val resultB = spark.sql(s"call run_compaction('schedule', '$tableName')")
.collect()
.map(row => Seq(row.getString(0), row.getInt(1), row.getString(2)))
assertResult(1)(resultA.length)
assertResult(1)(resultB.length)
val showCompactionSql: String = s"call show_compaction(table => '$tableName', limit => 10)"
checkAnswer(showCompactionSql)(
resultA(0),
resultB(0)
)
val compactionRows = spark.sql(showCompactionSql).collect()
val timestamps = compactionRows.map(_.getString(0)).sorted
assertResult(2)(timestamps.length)
spark.sql(s"call run_compaction(op => 'run', table => '$tableName', timestamp => ${timestamps(1)})")
// Execute the second scheduled compaction instant actually
checkAnswer(s"call run_compaction(op => 'run', table => '$tableName', timestamp => ${timestamps(1)})")(
Seq(resultB(0).head, resultB(0)(1), HoodieInstant.State.COMPLETED.name())
)
checkAnswer(s"select id, name, price, ts from $tableName order by id")(
Seq(1, "a1", 11.0, 1000),
Seq(2, "a2", 12.0, 1000),
Seq(3, "a3", 10.0, 1000),
Seq(4, "a4", 10.0, 1000)
)
assertResult(1)(spark.sql(s"call show_compaction('$tableName')").collect().length)
spark.sql(s"call run_compaction(op => 'run', table => '$tableName', timestamp => ${timestamps(0)})")
// A compaction action eventually becomes commit when completed, so show_compaction
// can only see the first scheduled compaction instant
val resultC = spark.sql(s"call show_compaction('$tableName')")
.collect()
.map(row => Seq(row.getString(0), row.getInt(1), row.getString(2)))
assertResult(1)(resultC.length)
assertResult(resultA)(resultC)
checkAnswer(s"call run_compaction(op => 'run', table => '$tableName', timestamp => ${timestamps(0)})")(
Seq(resultA(0).head, resultA(0)(1), HoodieInstant.State.COMPLETED.name())
)
checkAnswer(s"select id, name, price, ts from $tableName order by id")(
Seq(1, "a1", 11.0, 1000),
Seq(2, "a2", 12.0, 1000),
@@ -98,25 +129,40 @@ class TestCompactionProcedure extends HoodieSparkSqlTestBase {
spark.sql(s"insert into $tableName values(3, 'a3', 10, 1000)")
spark.sql(s"update $tableName set price = 11 where id = 1")
spark.sql(s"call run_compaction(op => 'run', path => '${tmp.getCanonicalPath}')")
checkAnswer(s"call run_compaction(op => 'run', path => '${tmp.getCanonicalPath}')")()
checkAnswer(s"select id, name, price, ts from $tableName order by id")(
Seq(1, "a1", 11.0, 1000),
Seq(2, "a2", 10.0, 1000),
Seq(3, "a3", 10.0, 1000)
)
assertResult(0)(spark.sql(s"call show_compaction(path => '${tmp.getCanonicalPath}')").collect().length)
// schedule compaction first
spark.sql(s"update $tableName set price = 12 where id = 1")
spark.sql(s"call run_compaction(op=> 'schedule', path => '${tmp.getCanonicalPath}')")
// schedule compaction second
// Schedule the first compaction
val resultA = spark.sql(s"call run_compaction(op=> 'schedule', path => '${tmp.getCanonicalPath}')")
.collect()
.map(row => Seq(row.getString(0), row.getInt(1), row.getString(2)))
spark.sql(s"update $tableName set price = 12 where id = 2")
spark.sql(s"call run_compaction(op => 'schedule', path => '${tmp.getCanonicalPath}')")
// show compaction
assertResult(2)(spark.sql(s"call show_compaction(path => '${tmp.getCanonicalPath}')").collect().length)
// run compaction for all the scheduled compaction
spark.sql(s"call run_compaction(op => 'run', path => '${tmp.getCanonicalPath}')")
// Schedule the second compaction
val resultB = spark.sql(s"call run_compaction(op => 'schedule', path => '${tmp.getCanonicalPath}')")
.collect()
.map(row => Seq(row.getString(0), row.getInt(1), row.getString(2)))
assertResult(1)(resultA.length)
assertResult(1)(resultB.length)
checkAnswer(s"call show_compaction(path => '${tmp.getCanonicalPath}')")(
resultA(0),
resultB(0)
)
// Run compaction for all the scheduled compaction
checkAnswer(s"call run_compaction(op => 'run', path => '${tmp.getCanonicalPath}')")(
Seq(resultA(0).head, resultA(0)(1), HoodieInstant.State.COMPLETED.name()),
Seq(resultB(0).head, resultB(0)(1), HoodieInstant.State.COMPLETED.name())
)
checkAnswer(s"select id, name, price, ts from $tableName order by id")(
Seq(1, "a1", 12.0, 1000),