umehrot2 commented on a change in pull request #1702: URL: https://github.com/apache/hudi/pull/1702#discussion_r447578702
########## File path: hudi-spark/src/main/scala/org/apache/hudi/HudiBootstrapRDD.scala ########## @@ -0,0 +1,131 @@ +/* + * 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 org.apache.spark.{Partition, TaskContext} +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.SparkSession +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.execution.datasources.PartitionedFile +import org.apache.spark.sql.types.StructType +import org.apache.spark.sql.vectorized.ColumnarBatch + +class HudiBootstrapRDD(@transient spark: SparkSession, + dataReadFunction: PartitionedFile => Iterator[Any], + skeletonReadFunction: PartitionedFile => Iterator[Any], + regularReadFunction: PartitionedFile => Iterator[Any], + dataSchema: StructType, + skeletonSchema: StructType, + requiredColumns: Array[String], + tableState: HudiBootstrapTableState) + extends RDD[InternalRow](spark.sparkContext, Nil) { + + override def compute(split: Partition, context: TaskContext): Iterator[InternalRow] = { + val bootstrapPartition = split.asInstanceOf[HudiBootstrapPartition] + + if (log.isDebugEnabled) { + if (bootstrapPartition.split.skeletonFile.isDefined) { + logDebug("Got Split => Index: " + bootstrapPartition.index + ", Data File: " + + bootstrapPartition.split.dataFile.filePath + ", Skeleton File: " + + bootstrapPartition.split.skeletonFile.get.filePath) + } else { + logDebug("Got Split => Index: " + bootstrapPartition.index + ", Data File: " + + bootstrapPartition.split.dataFile.filePath) + } + } + + var partitionedFileIterator: Iterator[InternalRow] = null + + if (bootstrapPartition.split.skeletonFile.isDefined) { + // It is a bootstrap split. Check both skeleton and data files. + if (dataSchema.isEmpty) { + // No data column to fetch, hence fetch only from skeleton file + partitionedFileIterator = read(bootstrapPartition.split.skeletonFile.get, skeletonReadFunction) + } else if (skeletonSchema.isEmpty) { + // No metadata column to fetch, hence fetch only from data file + partitionedFileIterator = read(bootstrapPartition.split.dataFile, dataReadFunction) + } else { + // Fetch from both data and skeleton file, and merge + val dataFileIterator = read(bootstrapPartition.split.dataFile, dataReadFunction) + val skeletonFileIterator = read(bootstrapPartition.split.skeletonFile.get, skeletonReadFunction) + partitionedFileIterator = merge(skeletonFileIterator, dataFileIterator) + } + } else { + partitionedFileIterator = read(bootstrapPartition.split.dataFile, regularReadFunction) + } + partitionedFileIterator + } + + def merge(skeletonFileIterator: Iterator[InternalRow], dataFileIterator: Iterator[InternalRow]) + : Iterator[InternalRow] = { + new Iterator[InternalRow] { + override def hasNext: Boolean = dataFileIterator.hasNext && skeletonFileIterator.hasNext + override def next(): InternalRow = { + mergeInternalRow(skeletonFileIterator.next(), dataFileIterator.next()) + } + } + } + + def mergeInternalRow(skeletonRow: InternalRow, dataRow: InternalRow): InternalRow = { + val skeletonArr = skeletonRow.copy().toSeq(skeletonSchema) + val dataArr = dataRow.copy().toSeq(dataSchema) + // We need to return it in the order requested + val mergedArr = requiredColumns.map(col => { + if (skeletonSchema.fieldNames.contains(col)) { + val idx = skeletonSchema.fieldIndex(col) + skeletonArr(idx) + } else { + val idx = dataSchema.fieldIndex(col) + dataArr(idx) + } + }) + + logDebug("Merged data and skeleton values => " + mergedArr.mkString(",")) + val mergedRow = InternalRow.fromSeq(mergedArr) + mergedRow + } + + def read(partitionedFile: PartitionedFile, readFileFunction: PartitionedFile => Iterator[Any]) + : Iterator[InternalRow] = { + val fileIterator = readFileFunction(partitionedFile) + + import scala.collection.JavaConverters._ + + val rows = fileIterator.flatMap(_ match { + case r: InternalRow => Seq(r) + case b: ColumnarBatch => b.rowIterator().asScala Review comment: As per my understanding `column pruning` is independent of `vectorized reader`. `vectorized reader` will basically read a batch of rows into a `columnar batch` and that is what will happen here as well. However, the only difference is that we are not passing it as a columnar batch all the way down as a batch. However, even if I use `regular parquet reader` at some point it must be converting the `columnar batch` to `rows` I guess. Right now I am not fully sure whether I am able to `100%` use all the benefits of `vectorized reading` with this method, but atleast it reads the data as a batch. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected]
