[HUDI-3204] Fixing partition-values being derived from partition-path instead of source columns (#5364)
- Scaffolded `Spark24HoodieParquetFileFormat` extending `ParquetFileFormat` and overriding the behavior of adding partition columns to every row - Amended `SparkAdapter`s `createHoodieParquetFileFormat` API to be able to configure whether to append partition values or not - Fallback to append partition values in cases when the source columns are not persisted in data-file - Fixing HoodieBaseRelation incorrectly handling mandatory columns
This commit is contained in:
Alexey Kudinkin
@@ -30,7 +30,7 @@ import org.apache.spark.sql.catalyst.plans.JoinType
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import org.apache.spark.sql.catalyst.plans.logical.{InsertIntoTable, Join, LogicalPlan}
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import org.apache.spark.sql.catalyst.rules.Rule
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import org.apache.spark.sql.catalyst.{AliasIdentifier, TableIdentifier}
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import org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat
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import org.apache.spark.sql.execution.datasources.parquet.{ParquetFileFormat, Spark24HoodieParquetFileFormat}
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import org.apache.spark.sql.execution.datasources.{FilePartition, PartitionedFile, Spark2ParsePartitionUtil, SparkParsePartitionUtil}
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import org.apache.spark.sql.hudi.SparkAdapter
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import org.apache.spark.sql.hudi.parser.HoodieSpark2ExtendedSqlParser
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@@ -165,7 +165,7 @@ class Spark2Adapter extends SparkAdapter {
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}
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}
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override def createHoodieParquetFileFormat(): Option[ParquetFileFormat] = {
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Some(new ParquetFileFormat)
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override def createHoodieParquetFileFormat(appendPartitionValues: Boolean): Option[ParquetFileFormat] = {
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Some(new Spark24HoodieParquetFileFormat(appendPartitionValues))
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}
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}
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@@ -0,0 +1,229 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.spark.sql.execution.datasources.parquet
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import org.apache.hadoop.conf.Configuration
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import org.apache.hadoop.fs.Path
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import org.apache.hadoop.mapreduce.lib.input.FileSplit
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import org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
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import org.apache.hadoop.mapreduce.{JobID, TaskAttemptID, TaskID, TaskType}
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import org.apache.parquet.filter2.compat.FilterCompat
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import org.apache.parquet.filter2.predicate.FilterApi
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import org.apache.parquet.format.converter.ParquetMetadataConverter.SKIP_ROW_GROUPS
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import org.apache.parquet.hadoop.{ParquetFileReader, ParquetInputFormat, ParquetRecordReader}
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import org.apache.spark.TaskContext
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import org.apache.spark.sql.SparkSession
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import org.apache.spark.sql.avro.AvroDeserializer
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import org.apache.spark.sql.catalyst.InternalRow
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import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection
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import org.apache.spark.sql.catalyst.expressions.{JoinedRow, UnsafeRow}
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import org.apache.spark.sql.catalyst.util.DateTimeUtils
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import org.apache.spark.sql.execution.datasources.{PartitionedFile, RecordReaderIterator}
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import org.apache.spark.sql.internal.SQLConf
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import org.apache.spark.sql.sources.Filter
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import org.apache.spark.sql.types.{AtomicType, StructType}
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import org.apache.spark.util.SerializableConfiguration
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import java.net.URI
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/**
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* This class is an extension of [[ParquetFileFormat]] overriding Spark-specific behavior
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* that's not possible to customize in any other way
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*
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* NOTE: This is a version of [[AvroDeserializer]] impl from Spark 2.4.4 w/ w/ the following changes applied to it:
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* <ol>
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* <li>Avoiding appending partition values to the rows read from the data file</li>
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* </ol>
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*/
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class Spark24HoodieParquetFileFormat(private val shouldAppendPartitionValues: Boolean) extends ParquetFileFormat {
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override def buildReaderWithPartitionValues(sparkSession: SparkSession,
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dataSchema: StructType,
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partitionSchema: StructType,
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requiredSchema: StructType,
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filters: Seq[Filter],
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options: Map[String, String],
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hadoopConf: Configuration): PartitionedFile => Iterator[InternalRow] = {
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hadoopConf.set(ParquetInputFormat.READ_SUPPORT_CLASS, classOf[ParquetReadSupport].getName)
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hadoopConf.set(
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ParquetReadSupport.SPARK_ROW_REQUESTED_SCHEMA,
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requiredSchema.json)
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hadoopConf.set(
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ParquetWriteSupport.SPARK_ROW_SCHEMA,
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requiredSchema.json)
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hadoopConf.set(
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SQLConf.SESSION_LOCAL_TIMEZONE.key,
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sparkSession.sessionState.conf.sessionLocalTimeZone)
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hadoopConf.setBoolean(
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SQLConf.CASE_SENSITIVE.key,
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sparkSession.sessionState.conf.caseSensitiveAnalysis)
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ParquetWriteSupport.setSchema(requiredSchema, hadoopConf)
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// Sets flags for `ParquetToSparkSchemaConverter`
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hadoopConf.setBoolean(
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SQLConf.PARQUET_BINARY_AS_STRING.key,
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sparkSession.sessionState.conf.isParquetBinaryAsString)
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hadoopConf.setBoolean(
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SQLConf.PARQUET_INT96_AS_TIMESTAMP.key,
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sparkSession.sessionState.conf.isParquetINT96AsTimestamp)
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val broadcastedHadoopConf =
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sparkSession.sparkContext.broadcast(new SerializableConfiguration(hadoopConf))
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// TODO: if you move this into the closure it reverts to the default values.
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// If true, enable using the custom RecordReader for parquet. This only works for
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// a subset of the types (no complex types).
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val resultSchema = StructType(partitionSchema.fields ++ requiredSchema.fields)
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val sqlConf = sparkSession.sessionState.conf
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val enableOffHeapColumnVector = sqlConf.offHeapColumnVectorEnabled
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val enableVectorizedReader: Boolean =
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sqlConf.parquetVectorizedReaderEnabled &&
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resultSchema.forall(_.dataType.isInstanceOf[AtomicType])
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val enableRecordFilter: Boolean = sqlConf.parquetRecordFilterEnabled
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val timestampConversion: Boolean = sqlConf.isParquetINT96TimestampConversion
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val capacity = sqlConf.parquetVectorizedReaderBatchSize
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val enableParquetFilterPushDown: Boolean = sqlConf.parquetFilterPushDown
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// Whole stage codegen (PhysicalRDD) is able to deal with batches directly
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val returningBatch = supportBatch(sparkSession, resultSchema)
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val pushDownDate = sqlConf.parquetFilterPushDownDate
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val pushDownTimestamp = sqlConf.parquetFilterPushDownTimestamp
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val pushDownDecimal = sqlConf.parquetFilterPushDownDecimal
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val pushDownStringStartWith = sqlConf.parquetFilterPushDownStringStartWith
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val pushDownInFilterThreshold = sqlConf.parquetFilterPushDownInFilterThreshold
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val isCaseSensitive = sqlConf.caseSensitiveAnalysis
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(file: PartitionedFile) => {
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assert(!shouldAppendPartitionValues || file.partitionValues.numFields == partitionSchema.size)
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val fileSplit =
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new FileSplit(new Path(new URI(file.filePath)), file.start, file.length, Array.empty)
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val filePath = fileSplit.getPath
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val split =
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new org.apache.parquet.hadoop.ParquetInputSplit(
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filePath,
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fileSplit.getStart,
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fileSplit.getStart + fileSplit.getLength,
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fileSplit.getLength,
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fileSplit.getLocations,
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null)
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val sharedConf = broadcastedHadoopConf.value.value
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lazy val footerFileMetaData =
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ParquetFileReader.readFooter(sharedConf, filePath, SKIP_ROW_GROUPS).getFileMetaData
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// Try to push down filters when filter push-down is enabled.
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val pushed = if (enableParquetFilterPushDown) {
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val parquetSchema = footerFileMetaData.getSchema
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val parquetFilters = new ParquetFilters(pushDownDate, pushDownTimestamp, pushDownDecimal,
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pushDownStringStartWith, pushDownInFilterThreshold, isCaseSensitive)
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filters
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// Collects all converted Parquet filter predicates. Notice that not all predicates can be
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// converted (`ParquetFilters.createFilter` returns an `Option`). That's why a `flatMap`
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// is used here.
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.flatMap(parquetFilters.createFilter(parquetSchema, _))
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.reduceOption(FilterApi.and)
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} else {
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None
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}
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// PARQUET_INT96_TIMESTAMP_CONVERSION says to apply timezone conversions to int96 timestamps'
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// *only* if the file was created by something other than "parquet-mr", so check the actual
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// writer here for this file. We have to do this per-file, as each file in the table may
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// have different writers.
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// Define isCreatedByParquetMr as function to avoid unnecessary parquet footer reads.
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def isCreatedByParquetMr: Boolean =
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footerFileMetaData.getCreatedBy().startsWith("parquet-mr")
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val convertTz =
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if (timestampConversion && !isCreatedByParquetMr) {
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Some(DateTimeUtils.getTimeZone(sharedConf.get(SQLConf.SESSION_LOCAL_TIMEZONE.key)))
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} else {
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None
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}
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val attemptId = new TaskAttemptID(new TaskID(new JobID(), TaskType.MAP, 0), 0)
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val hadoopAttemptContext =
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new TaskAttemptContextImpl(broadcastedHadoopConf.value.value, attemptId)
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// Try to push down filters when filter push-down is enabled.
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// Notice: This push-down is RowGroups level, not individual records.
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if (pushed.isDefined) {
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ParquetInputFormat.setFilterPredicate(hadoopAttemptContext.getConfiguration, pushed.get)
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}
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val taskContext = Option(TaskContext.get())
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if (enableVectorizedReader) {
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val vectorizedReader = new VectorizedParquetRecordReader(
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convertTz.orNull, enableOffHeapColumnVector && taskContext.isDefined, capacity)
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val iter = new RecordReaderIterator(vectorizedReader)
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// SPARK-23457 Register a task completion lister before `initialization`.
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taskContext.foreach(_.addTaskCompletionListener[Unit](_ => iter.close()))
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vectorizedReader.initialize(split, hadoopAttemptContext)
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logDebug(s"Appending $partitionSchema ${file.partitionValues}")
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// NOTE: We're making appending of the partitioned values to the rows read from the
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// data file configurable
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if (shouldAppendPartitionValues) {
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vectorizedReader.initBatch(partitionSchema, file.partitionValues)
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} else {
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vectorizedReader.initBatch(StructType(Nil), InternalRow.empty)
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}
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if (returningBatch) {
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vectorizedReader.enableReturningBatches()
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}
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// UnsafeRowParquetRecordReader appends the columns internally to avoid another copy.
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iter.asInstanceOf[Iterator[InternalRow]]
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} else {
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logDebug(s"Falling back to parquet-mr")
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// ParquetRecordReader returns UnsafeRow
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val reader = if (pushed.isDefined && enableRecordFilter) {
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val parquetFilter = FilterCompat.get(pushed.get, null)
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new ParquetRecordReader[UnsafeRow](new ParquetReadSupport(convertTz), parquetFilter)
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} else {
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new ParquetRecordReader[UnsafeRow](new ParquetReadSupport(convertTz))
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}
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val iter = new RecordReaderIterator(reader)
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// SPARK-23457 Register a task completion lister before `initialization`.
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taskContext.foreach(_.addTaskCompletionListener[Unit](_ => iter.close()))
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reader.initialize(split, hadoopAttemptContext)
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val fullSchema = requiredSchema.toAttributes ++ partitionSchema.toAttributes
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val joinedRow = new JoinedRow()
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val appendPartitionColumns = GenerateUnsafeProjection.generate(fullSchema, fullSchema)
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// This is a horrible erasure hack... if we type the iterator above, then it actually check
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// the type in next() and we get a class cast exception. If we make that function return
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// Object, then we can defer the cast until later!
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//
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// NOTE: We're making appending of the partitioned values to the rows read from the
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// data file configurable
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if (!shouldAppendPartitionValues || partitionSchema.length == 0) {
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// There is no partition columns
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iter.asInstanceOf[Iterator[InternalRow]]
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} else {
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iter.asInstanceOf[Iterator[InternalRow]]
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.map(d => appendPartitionColumns(joinedRow(d, file.partitionValues)))
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}
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}
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}
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}
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}
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