umehrot2 commented on a change in pull request #2651:
URL: https://github.com/apache/hudi/pull/2651#discussion_r593531717
##########
File path:
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/hudi/DefaultSource.scala
##########
@@ -79,39 +82,58 @@ class DefaultSource extends RelationProvider
val allPaths = path.map(p => Seq(p)).getOrElse(Seq()) ++ readPaths
val fs = FSUtils.getFs(allPaths.head,
sqlContext.sparkContext.hadoopConfiguration)
- val globPaths = HoodieSparkUtils.checkAndGlobPathIfNecessary(allPaths, fs)
-
- val tablePath = DataSourceUtils.getTablePath(fs, globPaths.toArray)
+ // Use the HoodieFileIndex only if the 'path' has specified with no "*"
contains.
+ // And READ_PATHS_OPT_KEY has not specified.
+ // Or else we use the original way to read hoodie table.
Review comment:
Its fine for now, but ideally we should support the glob paths with
`HoodieFileIndex` to be able to partition prune and later be able to integrate
it with metadata based listing and column range indexes. Can you open a JIRA
for this ?
##########
File path:
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/hudi/HoodieFileIndex.scala
##########
@@ -0,0 +1,273 @@
+/*
+ * 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
+
+import java.util.Properties
+
+import scala.collection.JavaConverters._
+import org.apache.hadoop.fs.{FileStatus, Path}
+import org.apache.hudi.client.common.HoodieSparkEngineContext
+import org.apache.hudi.common.config.{HoodieMetadataConfig,
SerializableConfiguration}
+import org.apache.hudi.common.fs.FSUtils
+import org.apache.hudi.common.model.HoodieBaseFile
+import org.apache.hudi.common.table.{HoodieTableMetaClient,
TableSchemaResolver}
+import org.apache.hudi.common.table.view.HoodieTableFileSystemView
+import org.apache.hudi.config.HoodieWriteConfig
+import org.apache.spark.api.java.JavaSparkContext
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.catalyst.{InternalRow, expressions}
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.avro.SchemaConverters
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference,
BoundReference, Expression, InterpretedPredicate}
+import org.apache.spark.sql.catalyst.util.{CaseInsensitiveMap, DateTimeUtils}
+import org.apache.spark.sql.execution.datasources.{FileIndex,
PartitionDirectory, PartitionUtils}
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.types.StructType
+
+/**
+ * A File Index which support partition prune for hoodie snapshot and
read-optimized
+ * query.
+ * Main steps to get the file list for query:
+ * 1、Load all files and partition values from the table path.
+ * 2、Do the partition prune by the partition filter condition.
+ *
+ * Note:
+ * Only when the URL_ENCODE_PARTITIONING_OPT_KEY is enable, we can store the
partition columns
+ * to the hoodie.properties in HoodieSqlWriter when write table. So that the
query can benefit
+ * from the partition prune.
+ */
Review comment:
This is too strong a constraint to have. Most customers would not be
using `URL_ENCODE_PARTITIONING_OPT_KEY` and it does not make sense for
partition pruning to not work for them and this feature would loose most of its
value.
I still don't quite understand why we can't support partition pruning by
default. As far as storing the partition columns names in `hoodie.properties`
is concerned, we can do that irrespective and does not have to be constrained
by `URL_ENCODE_PARTITIONING_OPT_KEY`. As I understand, the problem occurs
during the partition pruning where the values provided by spark in the
predicates would not match the value passed by Hudi in cases such as where
there is one partition column with value like `2020/10/10`. Is this
understanding correct ?
But just to support this above case, does not mean we do not do partition
pruning for normal cases where the same will be represented by 3 partition
columns: `yyyy=2020`,`mm=10`,`dd=10`. Our goal should be to support partition
pruning by default, and for certain scenarios like `2020/10/10` the need
encoding, it is upto the customer to pass `URL_ENCODE_PARTITIONING_OPT_KEY` to
be able to support partition pruning for such scenarios.
##########
File path:
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/hudi/HoodieFileIndex.scala
##########
@@ -0,0 +1,273 @@
+/*
+ * 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
+
+import java.util.Properties
+
+import scala.collection.JavaConverters._
+import org.apache.hadoop.fs.{FileStatus, Path}
+import org.apache.hudi.client.common.HoodieSparkEngineContext
+import org.apache.hudi.common.config.{HoodieMetadataConfig,
SerializableConfiguration}
+import org.apache.hudi.common.fs.FSUtils
+import org.apache.hudi.common.model.HoodieBaseFile
+import org.apache.hudi.common.table.{HoodieTableMetaClient,
TableSchemaResolver}
+import org.apache.hudi.common.table.view.HoodieTableFileSystemView
+import org.apache.hudi.config.HoodieWriteConfig
+import org.apache.spark.api.java.JavaSparkContext
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.catalyst.{InternalRow, expressions}
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.avro.SchemaConverters
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference,
BoundReference, Expression, InterpretedPredicate}
+import org.apache.spark.sql.catalyst.util.{CaseInsensitiveMap, DateTimeUtils}
+import org.apache.spark.sql.execution.datasources.{FileIndex,
PartitionDirectory, PartitionUtils}
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.types.StructType
+
+/**
+ * A File Index which support partition prune for hoodie snapshot and
read-optimized
+ * query.
+ * Main steps to get the file list for query:
+ * 1、Load all files and partition values from the table path.
+ * 2、Do the partition prune by the partition filter condition.
+ *
+ * Note:
+ * Only when the URL_ENCODE_PARTITIONING_OPT_KEY is enable, we can store the
partition columns
+ * to the hoodie.properties in HoodieSqlWriter when write table. So that the
query can benefit
+ * from the partition prune.
+ */
+case class HoodieFileIndex(
+ spark: SparkSession,
+ basePath: String,
+ schemaSpec: Option[StructType],
+ options: Map[String, String])
+ extends FileIndex with Logging {
+
+ @transient private val hadoopConf = spark.sessionState.newHadoopConf()
+ private lazy val metaClient = HoodieTableMetaClient
+ .builder().setConf(hadoopConf).setBasePath(basePath).build()
+
+ @transient private val queryPath = new Path(options.getOrElse("path",
"'path' option required"))
+ /**
+ * Get the schema of the table.
+ */
+ lazy val schema: StructType = schemaSpec.getOrElse({
+ val schemaUtil = new TableSchemaResolver(metaClient)
+ SchemaConverters.toSqlType(schemaUtil.getTableAvroSchema)
+ .dataType.asInstanceOf[StructType]
+ })
+
+ /**
+ * Get the partition schema.
+ */
+ private lazy val _partitionSchema: StructType = {
+ val tableConfig = metaClient.getTableConfig
+ val partitionColumns = tableConfig.getPartitionColumns
+ val nameFieldMap = schema.fields.map(filed => filed.name -> filed).toMap
+ // If the URL_ENCODE_PARTITIONING_OPT_KEY has enable, the partition schema
will stored in
+ // hoodie.properties, So we can benefit from the partition prune.
+ if (partitionColumns.isPresent) {
+ val partitionFields = partitionColumns.get().map(column =>
+ nameFieldMap.getOrElse(column, throw new
IllegalArgumentException(s"Cannot find column " +
+ s"$column in the schema[${schema.fields.mkString(",")}]")))
+ new StructType(partitionFields)
+ } else { // If the URL_ENCODE_PARTITIONING_OPT_KEY is disable, we trait it
as a
+ // none-partitioned table.
+ new StructType()
+ }
+ }
+
+ @transient @volatile private var fileSystemView: HoodieTableFileSystemView =
_
+ @transient @volatile private var cachedAllInputFiles: Array[HoodieBaseFile]
= _
+ @transient @volatile private var cachedFileSize: Long = 0L
+ @transient @volatile private var cachedAllPartitionPaths: Seq[PartitionPath]
= _
+
+ refresh()
+
+ override def rootPaths: Seq[Path] = queryPath :: Nil
+
+ override def listFiles(partitionFilters: Seq[Expression],
+ dataFilters: Seq[Expression]):
Seq[PartitionDirectory] = {
+ if (partitionSchema.fields.isEmpty) { // None partition table.
+ Seq(PartitionDirectory(InternalRow.empty, allFiles))
+ } else {
+ // Prune the partition path by the partition filters
+ val prunedPartitions = prunePartition(cachedAllPartitionPaths,
partitionFilters)
+ prunedPartitions.map { partition =>
+ val fileStatues =
fileSystemView.getLatestBaseFiles(partition.partitionPath).iterator()
+ .asScala.toSeq
+ .map(_.getFileStatus)
+ PartitionDirectory(partition.values, fileStatues)
+ }
+ }
+ }
+
+ override def inputFiles: Array[String] = {
+ cachedAllInputFiles.map(_.getFileStatus.getPath.toString)
+ }
+
+ override def refresh(): Unit = {
+ val startTime = System.currentTimeMillis()
+ val partitionFiles = loadPartitionPathFiles()
+ val allFiles = partitionFiles.values.reduceOption(_ ++ _)
+ .getOrElse(Array.empty[FileStatus])
+
+ metaClient.reloadActiveTimeline()
+ val activeInstants =
metaClient.getActiveTimeline.getCommitsTimeline.filterCompletedInstants
+ fileSystemView = new HoodieTableFileSystemView(metaClient, activeInstants,
allFiles)
+ cachedAllInputFiles =
fileSystemView.getLatestBaseFiles.iterator().asScala.toArray
+ cachedAllPartitionPaths = partitionFiles.keys.toSeq
+ cachedFileSize = cachedAllInputFiles.map(_.getFileLen).sum
+
+ val flushSpend = System.currentTimeMillis() - startTime
+ logInfo(s"Refresh for table ${metaClient.getTableConfig.getTableName}," +
+ s" spend: $flushSpend ms")
+ }
+
+ override def sizeInBytes: Long = {
+ cachedFileSize
+ }
+
+ override def partitionSchema: StructType = _partitionSchema
+
+ /**
+ * Get the data schema of the table.
+ * @return
+ */
+ def dataSchema: StructType = {
+ val partitionColumns = _partitionSchema.fields.map(_.name).toSet
+ StructType(schema.fields.filterNot(f => partitionColumns.contains(f.name)))
+ }
+
+ def allFiles: Seq[FileStatus] = cachedAllInputFiles.map(_.getFileStatus)
+
+ private def prunePartition(partitionPaths: Seq[PartitionPath],
+ predicates: Seq[Expression]): Seq[PartitionPath]
= {
+
+ val partitionColumnNames = partitionSchema.fields.map(_.name).toSet
+ val partitionPruningPredicates = predicates.filter {
+ _.references.map(_.name).toSet.subsetOf(partitionColumnNames)
+ }
+ if (partitionPruningPredicates.nonEmpty) {
+ val predicate = partitionPruningPredicates.reduce(expressions.And)
+
Review comment:
I am yet to take a deeper look at this code, but just wanted to confirm.
Does this handle all the different operators like `And`, `Or`, `Not` that can
be used with columns ? Is there any constraint here ?
##########
File path:
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/hudi/HoodieFileIndex.scala
##########
@@ -0,0 +1,273 @@
+/*
+ * 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
+
+import java.util.Properties
+
+import scala.collection.JavaConverters._
+import org.apache.hadoop.fs.{FileStatus, Path}
+import org.apache.hudi.client.common.HoodieSparkEngineContext
+import org.apache.hudi.common.config.{HoodieMetadataConfig,
SerializableConfiguration}
+import org.apache.hudi.common.fs.FSUtils
+import org.apache.hudi.common.model.HoodieBaseFile
+import org.apache.hudi.common.table.{HoodieTableMetaClient,
TableSchemaResolver}
+import org.apache.hudi.common.table.view.HoodieTableFileSystemView
+import org.apache.hudi.config.HoodieWriteConfig
+import org.apache.spark.api.java.JavaSparkContext
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.catalyst.{InternalRow, expressions}
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.avro.SchemaConverters
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference,
BoundReference, Expression, InterpretedPredicate}
+import org.apache.spark.sql.catalyst.util.{CaseInsensitiveMap, DateTimeUtils}
+import org.apache.spark.sql.execution.datasources.{FileIndex,
PartitionDirectory, PartitionUtils}
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.types.StructType
+
+/**
+ * A File Index which support partition prune for hoodie snapshot and
read-optimized
+ * query.
+ * Main steps to get the file list for query:
+ * 1、Load all files and partition values from the table path.
+ * 2、Do the partition prune by the partition filter condition.
+ *
+ * Note:
+ * Only when the URL_ENCODE_PARTITIONING_OPT_KEY is enable, we can store the
partition columns
+ * to the hoodie.properties in HoodieSqlWriter when write table. So that the
query can benefit
+ * from the partition prune.
+ */
+case class HoodieFileIndex(
+ spark: SparkSession,
+ basePath: String,
+ schemaSpec: Option[StructType],
+ options: Map[String, String])
+ extends FileIndex with Logging {
+
+ @transient private val hadoopConf = spark.sessionState.newHadoopConf()
+ private lazy val metaClient = HoodieTableMetaClient
+ .builder().setConf(hadoopConf).setBasePath(basePath).build()
+
+ @transient private val queryPath = new Path(options.getOrElse("path",
"'path' option required"))
+ /**
+ * Get the schema of the table.
+ */
+ lazy val schema: StructType = schemaSpec.getOrElse({
+ val schemaUtil = new TableSchemaResolver(metaClient)
+ SchemaConverters.toSqlType(schemaUtil.getTableAvroSchema)
+ .dataType.asInstanceOf[StructType]
+ })
+
+ /**
+ * Get the partition schema.
+ */
+ private lazy val _partitionSchema: StructType = {
+ val tableConfig = metaClient.getTableConfig
+ val partitionColumns = tableConfig.getPartitionColumns
+ val nameFieldMap = schema.fields.map(filed => filed.name -> filed).toMap
+ // If the URL_ENCODE_PARTITIONING_OPT_KEY has enable, the partition schema
will stored in
+ // hoodie.properties, So we can benefit from the partition prune.
+ if (partitionColumns.isPresent) {
+ val partitionFields = partitionColumns.get().map(column =>
+ nameFieldMap.getOrElse(column, throw new
IllegalArgumentException(s"Cannot find column " +
+ s"$column in the schema[${schema.fields.mkString(",")}]")))
+ new StructType(partitionFields)
+ } else { // If the URL_ENCODE_PARTITIONING_OPT_KEY is disable, we trait it
as a
+ // none-partitioned table.
+ new StructType()
+ }
+ }
+
+ @transient @volatile private var fileSystemView: HoodieTableFileSystemView =
_
+ @transient @volatile private var cachedAllInputFiles: Array[HoodieBaseFile]
= _
+ @transient @volatile private var cachedFileSize: Long = 0L
+ @transient @volatile private var cachedAllPartitionPaths: Seq[PartitionPath]
= _
+
+ refresh()
+
+ override def rootPaths: Seq[Path] = queryPath :: Nil
+
+ override def listFiles(partitionFilters: Seq[Expression],
+ dataFilters: Seq[Expression]):
Seq[PartitionDirectory] = {
+ if (partitionSchema.fields.isEmpty) { // None partition table.
+ Seq(PartitionDirectory(InternalRow.empty, allFiles))
+ } else {
+ // Prune the partition path by the partition filters
+ val prunedPartitions = prunePartition(cachedAllPartitionPaths,
partitionFilters)
+ prunedPartitions.map { partition =>
+ val fileStatues =
fileSystemView.getLatestBaseFiles(partition.partitionPath).iterator()
+ .asScala.toSeq
+ .map(_.getFileStatus)
+ PartitionDirectory(partition.values, fileStatues)
+ }
+ }
+ }
+
+ override def inputFiles: Array[String] = {
+ cachedAllInputFiles.map(_.getFileStatus.getPath.toString)
+ }
+
+ override def refresh(): Unit = {
+ val startTime = System.currentTimeMillis()
+ val partitionFiles = loadPartitionPathFiles()
+ val allFiles = partitionFiles.values.reduceOption(_ ++ _)
+ .getOrElse(Array.empty[FileStatus])
+
+ metaClient.reloadActiveTimeline()
+ val activeInstants =
metaClient.getActiveTimeline.getCommitsTimeline.filterCompletedInstants
+ fileSystemView = new HoodieTableFileSystemView(metaClient, activeInstants,
allFiles)
+ cachedAllInputFiles =
fileSystemView.getLatestBaseFiles.iterator().asScala.toArray
+ cachedAllPartitionPaths = partitionFiles.keys.toSeq
+ cachedFileSize = cachedAllInputFiles.map(_.getFileLen).sum
+
+ val flushSpend = System.currentTimeMillis() - startTime
+ logInfo(s"Refresh for table ${metaClient.getTableConfig.getTableName}," +
+ s" spend: $flushSpend ms")
+ }
+
+ override def sizeInBytes: Long = {
+ cachedFileSize
+ }
+
+ override def partitionSchema: StructType = _partitionSchema
+
+ /**
+ * Get the data schema of the table.
+ * @return
+ */
+ def dataSchema: StructType = {
+ val partitionColumns = _partitionSchema.fields.map(_.name).toSet
+ StructType(schema.fields.filterNot(f => partitionColumns.contains(f.name)))
+ }
+
+ def allFiles: Seq[FileStatus] = cachedAllInputFiles.map(_.getFileStatus)
+
+ private def prunePartition(partitionPaths: Seq[PartitionPath],
+ predicates: Seq[Expression]): Seq[PartitionPath]
= {
+
+ val partitionColumnNames = partitionSchema.fields.map(_.name).toSet
+ val partitionPruningPredicates = predicates.filter {
+ _.references.map(_.name).toSet.subsetOf(partitionColumnNames)
+ }
+ if (partitionPruningPredicates.nonEmpty) {
+ val predicate = partitionPruningPredicates.reduce(expressions.And)
+
+ val boundPredicate = InterpretedPredicate.create(predicate.transform {
+ case a: AttributeReference =>
+ val index = partitionSchema.indexWhere(a.name == _.name)
+ BoundReference(index, partitionSchema(index).dataType, nullable =
true)
+ })
+
+ val partitionPruned = partitionPaths.filter {
+ case PartitionPath(values, _) => boundPredicate.eval(values)
+ }
+ logInfo(s"Total partition size is: ${partitionPaths.size}," +
+ s" after partition prune size is: ${partitionPruned.size}")
+ partitionPruned
+ } else {
+ partitionPaths
+ }
+ }
+
+ /**
+ * Load all partition paths and it's files under the query table path.
+ */
+ private def loadPartitionPathFiles(): Map[PartitionPath, Array[FileStatus]]
= {
+ val sparkEngine = new HoodieSparkEngineContext(new
JavaSparkContext(spark.sparkContext))
+ val properties = new Properties()
+ properties.putAll(options.asJava)
+ val metadataConfig =
HoodieMetadataConfig.newBuilder.fromProperties(properties).build()
+
+ val queryPartitionPath = FSUtils.getRelativePartitionPath(new
Path(basePath), queryPath)
+ // Load all the partition path from the basePath, and filter by the query
partition path.
+ val partitionPaths = FSUtils.getAllPartitionPaths(sparkEngine,
metadataConfig, basePath).asScala
+ .filter(_.startsWith(queryPartitionPath))
+
+ val maxListParallelism =
options.get(HoodieWriteConfig.MAX_LISTING_PARALLELISM)
+
.map(_.toInt).getOrElse(HoodieWriteConfig.DEFAULT_MAX_LISTING_PARALLELISM.intValue())
+ val isPartitionedTable = _partitionSchema.fields.isEmpty
+ val serializableConf = new SerializableConfiguration(hadoopConf)
+ val partitionSchema = _partitionSchema
+ val timeZoneId = CaseInsensitiveMap(options)
+ .get(DateTimeUtils.TIMEZONE_OPTION)
+ .getOrElse(SQLConf.get.sessionLocalTimeZone)
+ // List files in all of the partition path.
+ val partition2Files =
+ spark.sparkContext.parallelize(partitionPaths,
Math.min(partitionPaths.size, maxListParallelism))
+ .map { partitionPath =>
+ val partitionRow = if (isPartitionedTable) { // This is a none
partitioned table
+ InternalRow.fromSeq(Seq(""))
Review comment:
Do you mean `isNonPartitionedTable` here ?
##########
File path:
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/hudi/HoodieFileIndex.scala
##########
@@ -0,0 +1,273 @@
+/*
+ * 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
+
+import java.util.Properties
+
+import scala.collection.JavaConverters._
+import org.apache.hadoop.fs.{FileStatus, Path}
+import org.apache.hudi.client.common.HoodieSparkEngineContext
+import org.apache.hudi.common.config.{HoodieMetadataConfig,
SerializableConfiguration}
+import org.apache.hudi.common.fs.FSUtils
+import org.apache.hudi.common.model.HoodieBaseFile
+import org.apache.hudi.common.table.{HoodieTableMetaClient,
TableSchemaResolver}
+import org.apache.hudi.common.table.view.HoodieTableFileSystemView
+import org.apache.hudi.config.HoodieWriteConfig
+import org.apache.spark.api.java.JavaSparkContext
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.catalyst.{InternalRow, expressions}
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.avro.SchemaConverters
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference,
BoundReference, Expression, InterpretedPredicate}
+import org.apache.spark.sql.catalyst.util.{CaseInsensitiveMap, DateTimeUtils}
+import org.apache.spark.sql.execution.datasources.{FileIndex,
PartitionDirectory, PartitionUtils}
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.types.StructType
+
+/**
+ * A File Index which support partition prune for hoodie snapshot and
read-optimized
+ * query.
+ * Main steps to get the file list for query:
+ * 1、Load all files and partition values from the table path.
+ * 2、Do the partition prune by the partition filter condition.
+ *
+ * Note:
+ * Only when the URL_ENCODE_PARTITIONING_OPT_KEY is enable, we can store the
partition columns
+ * to the hoodie.properties in HoodieSqlWriter when write table. So that the
query can benefit
+ * from the partition prune.
+ */
+case class HoodieFileIndex(
+ spark: SparkSession,
+ basePath: String,
+ schemaSpec: Option[StructType],
+ options: Map[String, String])
+ extends FileIndex with Logging {
+
+ @transient private val hadoopConf = spark.sessionState.newHadoopConf()
+ private lazy val metaClient = HoodieTableMetaClient
+ .builder().setConf(hadoopConf).setBasePath(basePath).build()
+
+ @transient private val queryPath = new Path(options.getOrElse("path",
"'path' option required"))
+ /**
+ * Get the schema of the table.
+ */
+ lazy val schema: StructType = schemaSpec.getOrElse({
+ val schemaUtil = new TableSchemaResolver(metaClient)
+ SchemaConverters.toSqlType(schemaUtil.getTableAvroSchema)
+ .dataType.asInstanceOf[StructType]
+ })
+
+ /**
+ * Get the partition schema.
+ */
+ private lazy val _partitionSchema: StructType = {
+ val tableConfig = metaClient.getTableConfig
+ val partitionColumns = tableConfig.getPartitionColumns
+ val nameFieldMap = schema.fields.map(filed => filed.name -> filed).toMap
+ // If the URL_ENCODE_PARTITIONING_OPT_KEY has enable, the partition schema
will stored in
+ // hoodie.properties, So we can benefit from the partition prune.
+ if (partitionColumns.isPresent) {
+ val partitionFields = partitionColumns.get().map(column =>
+ nameFieldMap.getOrElse(column, throw new
IllegalArgumentException(s"Cannot find column " +
+ s"$column in the schema[${schema.fields.mkString(",")}]")))
+ new StructType(partitionFields)
+ } else { // If the URL_ENCODE_PARTITIONING_OPT_KEY is disable, we trait it
as a
+ // none-partitioned table.
+ new StructType()
+ }
+ }
+
+ @transient @volatile private var fileSystemView: HoodieTableFileSystemView =
_
+ @transient @volatile private var cachedAllInputFiles: Array[HoodieBaseFile]
= _
+ @transient @volatile private var cachedFileSize: Long = 0L
+ @transient @volatile private var cachedAllPartitionPaths: Seq[PartitionPath]
= _
+
+ refresh()
+
+ override def rootPaths: Seq[Path] = queryPath :: Nil
+
+ override def listFiles(partitionFilters: Seq[Expression],
+ dataFilters: Seq[Expression]):
Seq[PartitionDirectory] = {
+ if (partitionSchema.fields.isEmpty) { // None partition table.
+ Seq(PartitionDirectory(InternalRow.empty, allFiles))
+ } else {
+ // Prune the partition path by the partition filters
+ val prunedPartitions = prunePartition(cachedAllPartitionPaths,
partitionFilters)
+ prunedPartitions.map { partition =>
+ val fileStatues =
fileSystemView.getLatestBaseFiles(partition.partitionPath).iterator()
+ .asScala.toSeq
+ .map(_.getFileStatus)
+ PartitionDirectory(partition.values, fileStatues)
+ }
+ }
+ }
+
+ override def inputFiles: Array[String] = {
+ cachedAllInputFiles.map(_.getFileStatus.getPath.toString)
+ }
+
+ override def refresh(): Unit = {
+ val startTime = System.currentTimeMillis()
+ val partitionFiles = loadPartitionPathFiles()
+ val allFiles = partitionFiles.values.reduceOption(_ ++ _)
+ .getOrElse(Array.empty[FileStatus])
+
+ metaClient.reloadActiveTimeline()
+ val activeInstants =
metaClient.getActiveTimeline.getCommitsTimeline.filterCompletedInstants
+ fileSystemView = new HoodieTableFileSystemView(metaClient, activeInstants,
allFiles)
+ cachedAllInputFiles =
fileSystemView.getLatestBaseFiles.iterator().asScala.toArray
+ cachedAllPartitionPaths = partitionFiles.keys.toSeq
+ cachedFileSize = cachedAllInputFiles.map(_.getFileLen).sum
+
+ val flushSpend = System.currentTimeMillis() - startTime
+ logInfo(s"Refresh for table ${metaClient.getTableConfig.getTableName}," +
+ s" spend: $flushSpend ms")
+ }
+
+ override def sizeInBytes: Long = {
+ cachedFileSize
+ }
+
+ override def partitionSchema: StructType = _partitionSchema
+
+ /**
+ * Get the data schema of the table.
+ * @return
+ */
+ def dataSchema: StructType = {
+ val partitionColumns = _partitionSchema.fields.map(_.name).toSet
+ StructType(schema.fields.filterNot(f => partitionColumns.contains(f.name)))
+ }
+
+ def allFiles: Seq[FileStatus] = cachedAllInputFiles.map(_.getFileStatus)
+
+ private def prunePartition(partitionPaths: Seq[PartitionPath],
+ predicates: Seq[Expression]): Seq[PartitionPath]
= {
+
+ val partitionColumnNames = partitionSchema.fields.map(_.name).toSet
+ val partitionPruningPredicates = predicates.filter {
+ _.references.map(_.name).toSet.subsetOf(partitionColumnNames)
+ }
+ if (partitionPruningPredicates.nonEmpty) {
+ val predicate = partitionPruningPredicates.reduce(expressions.And)
+
+ val boundPredicate = InterpretedPredicate.create(predicate.transform {
+ case a: AttributeReference =>
+ val index = partitionSchema.indexWhere(a.name == _.name)
+ BoundReference(index, partitionSchema(index).dataType, nullable =
true)
+ })
+
+ val partitionPruned = partitionPaths.filter {
+ case PartitionPath(values, _) => boundPredicate.eval(values)
+ }
+ logInfo(s"Total partition size is: ${partitionPaths.size}," +
+ s" after partition prune size is: ${partitionPruned.size}")
+ partitionPruned
+ } else {
+ partitionPaths
+ }
+ }
+
+ /**
+ * Load all partition paths and it's files under the query table path.
+ */
+ private def loadPartitionPathFiles(): Map[PartitionPath, Array[FileStatus]]
= {
+ val sparkEngine = new HoodieSparkEngineContext(new
JavaSparkContext(spark.sparkContext))
+ val properties = new Properties()
+ properties.putAll(options.asJava)
+ val metadataConfig =
HoodieMetadataConfig.newBuilder.fromProperties(properties).build()
+
+ val queryPartitionPath = FSUtils.getRelativePartitionPath(new
Path(basePath), queryPath)
+ // Load all the partition path from the basePath, and filter by the query
partition path.
+ val partitionPaths = FSUtils.getAllPartitionPaths(sparkEngine,
metadataConfig, basePath).asScala
+ .filter(_.startsWith(queryPartitionPath))
+
+ val maxListParallelism =
options.get(HoodieWriteConfig.MAX_LISTING_PARALLELISM)
+
.map(_.toInt).getOrElse(HoodieWriteConfig.DEFAULT_MAX_LISTING_PARALLELISM.intValue())
+ val isPartitionedTable = _partitionSchema.fields.isEmpty
+ val serializableConf = new SerializableConfiguration(hadoopConf)
+ val partitionSchema = _partitionSchema
+ val timeZoneId = CaseInsensitiveMap(options)
+ .get(DateTimeUtils.TIMEZONE_OPTION)
+ .getOrElse(SQLConf.get.sessionLocalTimeZone)
+ // List files in all of the partition path.
+ val partition2Files =
+ spark.sparkContext.parallelize(partitionPaths,
Math.min(partitionPaths.size, maxListParallelism))
+ .map { partitionPath =>
+ val partitionRow = if (isPartitionedTable) { // This is a none
partitioned table
+ InternalRow.fromSeq(Seq(""))
+ } else {
+ val partitionSeqs = partitionPath.split("/")
+ assert(partitionSeqs.length == partitionSchema.size,
+ s"size of partition values[size is: ${partitionSeqs.size}, path
" +
+ s"is: '$partitionPath'] is not equal to the size of" +
+ s" partition schema field[size is: ${partitionSchema.size}," +
+ s" fields is: ${partitionSchema.json}].")
+ // Append partition name to the partition value.
+ // e.g. convert "/xx/xx/2021/02" to "/xx/xx/year=2021/month=02"
+ val partitionWithName =
+ partitionSeqs.zip(partitionSchema).map {
+ case (partition, field) =>
+ if (partition.indexOf("=") == -1) {
+ s"${field.name}=$partition"
+ } else {
+ partition
+ }
+ }.mkString("/")
+ val pathWithPartitionName = new Path(basePath, partitionWithName)
+ val partitionDataTypes = partitionSchema.fields.map(f => f.name ->
f.dataType).toMap
+ val partitionValues =
PartitionUtils.parsePartition(pathWithPartitionName,
+ typeInference = false, Set(new Path(basePath)),
partitionDataTypes,
+ DateTimeUtils.getTimeZone(timeZoneId))
+
+ // Convert partitionValues to InternalRow
+ partitionValues.map(_.literals.map(_.value))
+ .map(InternalRow.fromSeq)
+ .getOrElse(InternalRow.empty)
Review comment:
To confirm my understanding, we are doing this part to be able to form
`InternalRow` of partition values right, so it can evaluated with the
predicates ? Re-using this logic from Spark is helping us avoid having to
handle data types conversions ?
##########
File path:
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/hudi/HoodieFileIndex.scala
##########
@@ -0,0 +1,273 @@
+/*
+ * 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
+
+import java.util.Properties
+
+import scala.collection.JavaConverters._
+import org.apache.hadoop.fs.{FileStatus, Path}
+import org.apache.hudi.client.common.HoodieSparkEngineContext
+import org.apache.hudi.common.config.{HoodieMetadataConfig,
SerializableConfiguration}
+import org.apache.hudi.common.fs.FSUtils
+import org.apache.hudi.common.model.HoodieBaseFile
+import org.apache.hudi.common.table.{HoodieTableMetaClient,
TableSchemaResolver}
+import org.apache.hudi.common.table.view.HoodieTableFileSystemView
+import org.apache.hudi.config.HoodieWriteConfig
+import org.apache.spark.api.java.JavaSparkContext
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.catalyst.{InternalRow, expressions}
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.avro.SchemaConverters
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference,
BoundReference, Expression, InterpretedPredicate}
+import org.apache.spark.sql.catalyst.util.{CaseInsensitiveMap, DateTimeUtils}
+import org.apache.spark.sql.execution.datasources.{FileIndex,
PartitionDirectory, PartitionUtils}
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.types.StructType
+
+/**
+ * A File Index which support partition prune for hoodie snapshot and
read-optimized
+ * query.
+ * Main steps to get the file list for query:
+ * 1、Load all files and partition values from the table path.
+ * 2、Do the partition prune by the partition filter condition.
+ *
+ * Note:
+ * Only when the URL_ENCODE_PARTITIONING_OPT_KEY is enable, we can store the
partition columns
+ * to the hoodie.properties in HoodieSqlWriter when write table. So that the
query can benefit
+ * from the partition prune.
+ */
+case class HoodieFileIndex(
+ spark: SparkSession,
+ basePath: String,
+ schemaSpec: Option[StructType],
+ options: Map[String, String])
+ extends FileIndex with Logging {
+
+ @transient private val hadoopConf = spark.sessionState.newHadoopConf()
+ private lazy val metaClient = HoodieTableMetaClient
+ .builder().setConf(hadoopConf).setBasePath(basePath).build()
+
+ @transient private val queryPath = new Path(options.getOrElse("path",
"'path' option required"))
+ /**
+ * Get the schema of the table.
+ */
+ lazy val schema: StructType = schemaSpec.getOrElse({
+ val schemaUtil = new TableSchemaResolver(metaClient)
+ SchemaConverters.toSqlType(schemaUtil.getTableAvroSchema)
+ .dataType.asInstanceOf[StructType]
+ })
+
+ /**
+ * Get the partition schema.
+ */
+ private lazy val _partitionSchema: StructType = {
+ val tableConfig = metaClient.getTableConfig
+ val partitionColumns = tableConfig.getPartitionColumns
+ val nameFieldMap = schema.fields.map(filed => filed.name -> filed).toMap
+ // If the URL_ENCODE_PARTITIONING_OPT_KEY has enable, the partition schema
will stored in
+ // hoodie.properties, So we can benefit from the partition prune.
+ if (partitionColumns.isPresent) {
+ val partitionFields = partitionColumns.get().map(column =>
+ nameFieldMap.getOrElse(column, throw new
IllegalArgumentException(s"Cannot find column " +
+ s"$column in the schema[${schema.fields.mkString(",")}]")))
+ new StructType(partitionFields)
+ } else { // If the URL_ENCODE_PARTITIONING_OPT_KEY is disable, we trait it
as a
+ // none-partitioned table.
+ new StructType()
+ }
+ }
+
+ @transient @volatile private var fileSystemView: HoodieTableFileSystemView =
_
+ @transient @volatile private var cachedAllInputFiles: Array[HoodieBaseFile]
= _
+ @transient @volatile private var cachedFileSize: Long = 0L
+ @transient @volatile private var cachedAllPartitionPaths: Seq[PartitionPath]
= _
+
+ refresh()
+
+ override def rootPaths: Seq[Path] = queryPath :: Nil
+
+ override def listFiles(partitionFilters: Seq[Expression],
+ dataFilters: Seq[Expression]):
Seq[PartitionDirectory] = {
+ if (partitionSchema.fields.isEmpty) { // None partition table.
+ Seq(PartitionDirectory(InternalRow.empty, allFiles))
+ } else {
+ // Prune the partition path by the partition filters
+ val prunedPartitions = prunePartition(cachedAllPartitionPaths,
partitionFilters)
+ prunedPartitions.map { partition =>
+ val fileStatues =
fileSystemView.getLatestBaseFiles(partition.partitionPath).iterator()
+ .asScala.toSeq
+ .map(_.getFileStatus)
+ PartitionDirectory(partition.values, fileStatues)
+ }
+ }
+ }
+
+ override def inputFiles: Array[String] = {
+ cachedAllInputFiles.map(_.getFileStatus.getPath.toString)
+ }
+
+ override def refresh(): Unit = {
+ val startTime = System.currentTimeMillis()
+ val partitionFiles = loadPartitionPathFiles()
+ val allFiles = partitionFiles.values.reduceOption(_ ++ _)
+ .getOrElse(Array.empty[FileStatus])
+
+ metaClient.reloadActiveTimeline()
+ val activeInstants =
metaClient.getActiveTimeline.getCommitsTimeline.filterCompletedInstants
+ fileSystemView = new HoodieTableFileSystemView(metaClient, activeInstants,
allFiles)
+ cachedAllInputFiles =
fileSystemView.getLatestBaseFiles.iterator().asScala.toArray
+ cachedAllPartitionPaths = partitionFiles.keys.toSeq
+ cachedFileSize = cachedAllInputFiles.map(_.getFileLen).sum
+
+ val flushSpend = System.currentTimeMillis() - startTime
+ logInfo(s"Refresh for table ${metaClient.getTableConfig.getTableName}," +
+ s" spend: $flushSpend ms")
+ }
+
+ override def sizeInBytes: Long = {
+ cachedFileSize
+ }
+
+ override def partitionSchema: StructType = _partitionSchema
+
+ /**
+ * Get the data schema of the table.
+ * @return
+ */
+ def dataSchema: StructType = {
+ val partitionColumns = _partitionSchema.fields.map(_.name).toSet
+ StructType(schema.fields.filterNot(f => partitionColumns.contains(f.name)))
+ }
+
+ def allFiles: Seq[FileStatus] = cachedAllInputFiles.map(_.getFileStatus)
+
+ private def prunePartition(partitionPaths: Seq[PartitionPath],
+ predicates: Seq[Expression]): Seq[PartitionPath]
= {
+
+ val partitionColumnNames = partitionSchema.fields.map(_.name).toSet
+ val partitionPruningPredicates = predicates.filter {
+ _.references.map(_.name).toSet.subsetOf(partitionColumnNames)
+ }
+ if (partitionPruningPredicates.nonEmpty) {
+ val predicate = partitionPruningPredicates.reduce(expressions.And)
+
+ val boundPredicate = InterpretedPredicate.create(predicate.transform {
+ case a: AttributeReference =>
+ val index = partitionSchema.indexWhere(a.name == _.name)
+ BoundReference(index, partitionSchema(index).dataType, nullable =
true)
+ })
+
+ val partitionPruned = partitionPaths.filter {
+ case PartitionPath(values, _) => boundPredicate.eval(values)
+ }
+ logInfo(s"Total partition size is: ${partitionPaths.size}," +
+ s" after partition prune size is: ${partitionPruned.size}")
+ partitionPruned
+ } else {
+ partitionPaths
+ }
+ }
+
+ /**
+ * Load all partition paths and it's files under the query table path.
+ */
+ private def loadPartitionPathFiles(): Map[PartitionPath, Array[FileStatus]]
= {
+ val sparkEngine = new HoodieSparkEngineContext(new
JavaSparkContext(spark.sparkContext))
+ val properties = new Properties()
+ properties.putAll(options.asJava)
+ val metadataConfig =
HoodieMetadataConfig.newBuilder.fromProperties(properties).build()
+
+ val queryPartitionPath = FSUtils.getRelativePartitionPath(new
Path(basePath), queryPath)
+ // Load all the partition path from the basePath, and filter by the query
partition path.
+ val partitionPaths = FSUtils.getAllPartitionPaths(sparkEngine,
metadataConfig, basePath).asScala
+ .filter(_.startsWith(queryPartitionPath))
+
+ val maxListParallelism =
options.get(HoodieWriteConfig.MAX_LISTING_PARALLELISM)
+
.map(_.toInt).getOrElse(HoodieWriteConfig.DEFAULT_MAX_LISTING_PARALLELISM.intValue())
+ val isPartitionedTable = _partitionSchema.fields.isEmpty
+ val serializableConf = new SerializableConfiguration(hadoopConf)
+ val partitionSchema = _partitionSchema
+ val timeZoneId = CaseInsensitiveMap(options)
+ .get(DateTimeUtils.TIMEZONE_OPTION)
+ .getOrElse(SQLConf.get.sessionLocalTimeZone)
+ // List files in all of the partition path.
+ val partition2Files =
+ spark.sparkContext.parallelize(partitionPaths,
Math.min(partitionPaths.size, maxListParallelism))
+ .map { partitionPath =>
+ val partitionRow = if (isPartitionedTable) { // This is a none
partitioned table
+ InternalRow.fromSeq(Seq(""))
+ } else {
+ val partitionSeqs = partitionPath.split("/")
+ assert(partitionSeqs.length == partitionSchema.size,
+ s"size of partition values[size is: ${partitionSeqs.size}, path
" +
+ s"is: '$partitionPath'] is not equal to the size of" +
+ s" partition schema field[size is: ${partitionSchema.size}," +
+ s" fields is: ${partitionSchema.json}].")
+ // Append partition name to the partition value.
+ // e.g. convert "/xx/xx/2021/02" to "/xx/xx/year=2021/month=02"
Review comment:
We should handle the scenario where `HIVE_STYLE_PARTITIONING_OPT_KEY`
was turned on. In this case, the partition path string will already be the
required format as I understand.
##########
File path:
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/hudi/HoodieFileIndex.scala
##########
@@ -0,0 +1,273 @@
+/*
+ * 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
+
+import java.util.Properties
+
+import scala.collection.JavaConverters._
+import org.apache.hadoop.fs.{FileStatus, Path}
+import org.apache.hudi.client.common.HoodieSparkEngineContext
+import org.apache.hudi.common.config.{HoodieMetadataConfig,
SerializableConfiguration}
+import org.apache.hudi.common.fs.FSUtils
+import org.apache.hudi.common.model.HoodieBaseFile
+import org.apache.hudi.common.table.{HoodieTableMetaClient,
TableSchemaResolver}
+import org.apache.hudi.common.table.view.HoodieTableFileSystemView
+import org.apache.hudi.config.HoodieWriteConfig
+import org.apache.spark.api.java.JavaSparkContext
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.catalyst.{InternalRow, expressions}
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.avro.SchemaConverters
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference,
BoundReference, Expression, InterpretedPredicate}
+import org.apache.spark.sql.catalyst.util.{CaseInsensitiveMap, DateTimeUtils}
+import org.apache.spark.sql.execution.datasources.{FileIndex,
PartitionDirectory, PartitionUtils}
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.types.StructType
+
+/**
+ * A File Index which support partition prune for hoodie snapshot and
read-optimized
+ * query.
+ * Main steps to get the file list for query:
+ * 1、Load all files and partition values from the table path.
+ * 2、Do the partition prune by the partition filter condition.
+ *
+ * Note:
+ * Only when the URL_ENCODE_PARTITIONING_OPT_KEY is enable, we can store the
partition columns
+ * to the hoodie.properties in HoodieSqlWriter when write table. So that the
query can benefit
+ * from the partition prune.
+ */
+case class HoodieFileIndex(
+ spark: SparkSession,
+ basePath: String,
+ schemaSpec: Option[StructType],
+ options: Map[String, String])
+ extends FileIndex with Logging {
+
+ @transient private val hadoopConf = spark.sessionState.newHadoopConf()
+ private lazy val metaClient = HoodieTableMetaClient
+ .builder().setConf(hadoopConf).setBasePath(basePath).build()
+
+ @transient private val queryPath = new Path(options.getOrElse("path",
"'path' option required"))
+ /**
+ * Get the schema of the table.
+ */
+ lazy val schema: StructType = schemaSpec.getOrElse({
+ val schemaUtil = new TableSchemaResolver(metaClient)
+ SchemaConverters.toSqlType(schemaUtil.getTableAvroSchema)
+ .dataType.asInstanceOf[StructType]
+ })
+
+ /**
+ * Get the partition schema.
+ */
+ private lazy val _partitionSchema: StructType = {
+ val tableConfig = metaClient.getTableConfig
+ val partitionColumns = tableConfig.getPartitionColumns
+ val nameFieldMap = schema.fields.map(filed => filed.name -> filed).toMap
+ // If the URL_ENCODE_PARTITIONING_OPT_KEY has enable, the partition schema
will stored in
+ // hoodie.properties, So we can benefit from the partition prune.
+ if (partitionColumns.isPresent) {
+ val partitionFields = partitionColumns.get().map(column =>
+ nameFieldMap.getOrElse(column, throw new
IllegalArgumentException(s"Cannot find column " +
+ s"$column in the schema[${schema.fields.mkString(",")}]")))
+ new StructType(partitionFields)
+ } else { // If the URL_ENCODE_PARTITIONING_OPT_KEY is disable, we trait it
as a
+ // none-partitioned table.
+ new StructType()
+ }
+ }
+
+ @transient @volatile private var fileSystemView: HoodieTableFileSystemView =
_
+ @transient @volatile private var cachedAllInputFiles: Array[HoodieBaseFile]
= _
+ @transient @volatile private var cachedFileSize: Long = 0L
+ @transient @volatile private var cachedAllPartitionPaths: Seq[PartitionPath]
= _
+
+ refresh()
+
+ override def rootPaths: Seq[Path] = queryPath :: Nil
+
+ override def listFiles(partitionFilters: Seq[Expression],
+ dataFilters: Seq[Expression]):
Seq[PartitionDirectory] = {
+ if (partitionSchema.fields.isEmpty) { // None partition table.
+ Seq(PartitionDirectory(InternalRow.empty, allFiles))
+ } else {
+ // Prune the partition path by the partition filters
+ val prunedPartitions = prunePartition(cachedAllPartitionPaths,
partitionFilters)
+ prunedPartitions.map { partition =>
+ val fileStatues =
fileSystemView.getLatestBaseFiles(partition.partitionPath).iterator()
+ .asScala.toSeq
+ .map(_.getFileStatus)
+ PartitionDirectory(partition.values, fileStatues)
+ }
+ }
+ }
+
+ override def inputFiles: Array[String] = {
+ cachedAllInputFiles.map(_.getFileStatus.getPath.toString)
+ }
+
+ override def refresh(): Unit = {
+ val startTime = System.currentTimeMillis()
+ val partitionFiles = loadPartitionPathFiles()
+ val allFiles = partitionFiles.values.reduceOption(_ ++ _)
+ .getOrElse(Array.empty[FileStatus])
+
+ metaClient.reloadActiveTimeline()
+ val activeInstants =
metaClient.getActiveTimeline.getCommitsTimeline.filterCompletedInstants
+ fileSystemView = new HoodieTableFileSystemView(metaClient, activeInstants,
allFiles)
+ cachedAllInputFiles =
fileSystemView.getLatestBaseFiles.iterator().asScala.toArray
+ cachedAllPartitionPaths = partitionFiles.keys.toSeq
+ cachedFileSize = cachedAllInputFiles.map(_.getFileLen).sum
+
+ val flushSpend = System.currentTimeMillis() - startTime
+ logInfo(s"Refresh for table ${metaClient.getTableConfig.getTableName}," +
+ s" spend: $flushSpend ms")
+ }
+
+ override def sizeInBytes: Long = {
+ cachedFileSize
+ }
+
+ override def partitionSchema: StructType = _partitionSchema
+
+ /**
+ * Get the data schema of the table.
+ * @return
+ */
+ def dataSchema: StructType = {
+ val partitionColumns = _partitionSchema.fields.map(_.name).toSet
+ StructType(schema.fields.filterNot(f => partitionColumns.contains(f.name)))
+ }
+
+ def allFiles: Seq[FileStatus] = cachedAllInputFiles.map(_.getFileStatus)
+
+ private def prunePartition(partitionPaths: Seq[PartitionPath],
+ predicates: Seq[Expression]): Seq[PartitionPath]
= {
+
+ val partitionColumnNames = partitionSchema.fields.map(_.name).toSet
+ val partitionPruningPredicates = predicates.filter {
+ _.references.map(_.name).toSet.subsetOf(partitionColumnNames)
+ }
+ if (partitionPruningPredicates.nonEmpty) {
+ val predicate = partitionPruningPredicates.reduce(expressions.And)
+
+ val boundPredicate = InterpretedPredicate.create(predicate.transform {
+ case a: AttributeReference =>
+ val index = partitionSchema.indexWhere(a.name == _.name)
+ BoundReference(index, partitionSchema(index).dataType, nullable =
true)
+ })
+
+ val partitionPruned = partitionPaths.filter {
+ case PartitionPath(values, _) => boundPredicate.eval(values)
+ }
+ logInfo(s"Total partition size is: ${partitionPaths.size}," +
+ s" after partition prune size is: ${partitionPruned.size}")
+ partitionPruned
+ } else {
+ partitionPaths
+ }
+ }
+
+ /**
+ * Load all partition paths and it's files under the query table path.
+ */
+ private def loadPartitionPathFiles(): Map[PartitionPath, Array[FileStatus]]
= {
+ val sparkEngine = new HoodieSparkEngineContext(new
JavaSparkContext(spark.sparkContext))
+ val properties = new Properties()
+ properties.putAll(options.asJava)
+ val metadataConfig =
HoodieMetadataConfig.newBuilder.fromProperties(properties).build()
+
+ val queryPartitionPath = FSUtils.getRelativePartitionPath(new
Path(basePath), queryPath)
+ // Load all the partition path from the basePath, and filter by the query
partition path.
+ val partitionPaths = FSUtils.getAllPartitionPaths(sparkEngine,
metadataConfig, basePath).asScala
+ .filter(_.startsWith(queryPartitionPath))
+
+ val maxListParallelism =
options.get(HoodieWriteConfig.MAX_LISTING_PARALLELISM)
+
.map(_.toInt).getOrElse(HoodieWriteConfig.DEFAULT_MAX_LISTING_PARALLELISM.intValue())
+ val isPartitionedTable = _partitionSchema.fields.isEmpty
+ val serializableConf = new SerializableConfiguration(hadoopConf)
+ val partitionSchema = _partitionSchema
+ val timeZoneId = CaseInsensitiveMap(options)
+ .get(DateTimeUtils.TIMEZONE_OPTION)
+ .getOrElse(SQLConf.get.sessionLocalTimeZone)
+ // List files in all of the partition path.
+ val partition2Files =
+ spark.sparkContext.parallelize(partitionPaths,
Math.min(partitionPaths.size, maxListParallelism))
+ .map { partitionPath =>
+ val partitionRow = if (isPartitionedTable) { // This is a none
partitioned table
+ InternalRow.fromSeq(Seq(""))
+ } else {
+ val partitionSeqs = partitionPath.split("/")
+ assert(partitionSeqs.length == partitionSchema.size,
+ s"size of partition values[size is: ${partitionSeqs.size}, path
" +
+ s"is: '$partitionPath'] is not equal to the size of" +
+ s" partition schema field[size is: ${partitionSchema.size}," +
+ s" fields is: ${partitionSchema.json}].")
+ // Append partition name to the partition value.
+ // e.g. convert "/xx/xx/2021/02" to "/xx/xx/year=2021/month=02"
+ val partitionWithName =
+ partitionSeqs.zip(partitionSchema).map {
+ case (partition, field) =>
+ if (partition.indexOf("=") == -1) {
+ s"${field.name}=$partition"
+ } else {
+ partition
+ }
+ }.mkString("/")
+ val pathWithPartitionName = new Path(basePath, partitionWithName)
+ val partitionDataTypes = partitionSchema.fields.map(f => f.name ->
f.dataType).toMap
+ val partitionValues =
PartitionUtils.parsePartition(pathWithPartitionName,
+ typeInference = false, Set(new Path(basePath)),
partitionDataTypes,
+ DateTimeUtils.getTimeZone(timeZoneId))
+
+ // Convert partitionValues to InternalRow
+ partitionValues.map(_.literals.map(_.value))
+ .map(InternalRow.fromSeq)
+ .getOrElse(InternalRow.empty)
+ }
+ val fullPartitionPath = if (partitionPath.isEmpty) {
+ new Path(basePath) // This is a none partition path
+ } else {
+ new Path(basePath, partitionPath)
+ }
+ val fs = fullPartitionPath.getFileSystem(serializableConf.get())
+ val filesInPartition = FSUtils.getAllDataFilesInPartition(fs,
fullPartitionPath)
Review comment:
We need to directly do here:
```
fileSystemView.getLatestBaseFiles(partitionPath)
```
This will internally be able to either list via file system or use the
metadata if its available.
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