Github user dongjoon-hyun commented on a diff in the pull request:
https://github.com/apache/spark/pull/19943#discussion_r160017124
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcColumnarBatchReader.scala
---
@@ -0,0 +1,493 @@
+/*
+ * 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.spark.sql.execution.datasources.orc
+
+import org.apache.hadoop.mapreduce.{InputSplit, RecordReader,
TaskAttemptContext}
+import org.apache.hadoop.mapreduce.lib.input.FileSplit
+import org.apache.orc._
+import org.apache.orc.mapred.OrcInputFormat
+import org.apache.orc.storage.ql.exec.vector._
+import org.apache.orc.storage.serde2.io.HiveDecimalWritable
+
+import org.apache.spark.memory.MemoryMode
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.execution.vectorized._
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.vectorized._
+
+
+/**
+ * To support vectorization in WholeStageCodeGen, this reader returns
ColumnarBatch.
+ * After creating, `initialize` and `setRequiredSchema` should be called
sequentially.
+ */
+private[orc] class OrcColumnarBatchReader extends RecordReader[Void,
ColumnarBatch] {
+ import OrcColumnarBatchReader._
+
+ /**
+ * ORC File Reader.
+ */
+ private var reader: Reader = _
+
+ /**
+ * Vectorized Row Batch.
+ */
+ private var batch: VectorizedRowBatch = _
+
+ /**
+ * Requested Column IDs.
+ */
+ private var requestedColIds: Array[Int] = _
+
+ /**
+ * Record reader from row batch.
+ */
+ private var recordReader: org.apache.orc.RecordReader = _
+
+ /**
+ * Required Schema.
+ */
+ private var requiredSchema: StructType = _
+
+ /**
+ * ColumnarBatch for vectorized execution by whole-stage codegen.
+ */
+ private var columnarBatch: ColumnarBatch = _
+
+ /**
+ * Writable column vectors of ColumnarBatch.
+ */
+ private var columnVectors: Seq[WritableColumnVector] = _
+
+ /**
+ * The number of rows read and considered to be returned.
+ */
+ private var rowsReturned: Long = 0L
+
+ /**
+ * Total number of rows.
+ */
+ private var totalRowCount: Long = 0L
+
+ override def getCurrentKey: Void = null
+
+ override def getCurrentValue: ColumnarBatch = columnarBatch
+
+ override def getProgress: Float = rowsReturned.toFloat / totalRowCount
+
+ override def nextKeyValue(): Boolean = nextBatch()
+
+ override def close(): Unit = {
+ if (columnarBatch != null) {
+ columnarBatch.close()
+ columnarBatch = null
+ }
+ if (recordReader != null) {
+ recordReader.close()
+ recordReader = null
+ }
+ }
+
+ /**
+ * Initialize ORC file reader and batch record reader.
+ * Please note that `setRequiredSchema` is needed to be called after
this.
+ */
+ override def initialize(inputSplit: InputSplit, taskAttemptContext:
TaskAttemptContext): Unit = {
+ val fileSplit = inputSplit.asInstanceOf[FileSplit]
+ val conf = taskAttemptContext.getConfiguration
+ reader = OrcFile.createReader(
+ fileSplit.getPath,
+ OrcFile.readerOptions(conf)
+ .maxLength(OrcConf.MAX_FILE_LENGTH.getLong(conf))
+ .filesystem(fileSplit.getPath.getFileSystem(conf)))
+
+ val options = OrcInputFormat.buildOptions(conf, reader,
fileSplit.getStart, fileSplit.getLength)
+ recordReader = reader.rows(options)
+ totalRowCount = reader.getNumberOfRows
+ }
+
+ /**
+ * Set required schema and partition information.
+ * With this information, this creates ColumnarBatch with the full
schema.
+ */
+ def setRequiredSchema(
+ orcSchema: TypeDescription,
+ requestedColIds: Array[Int],
+ resultSchema: StructType,
+ requiredSchema: StructType,
+ partitionValues: InternalRow): Unit = {
+ batch = orcSchema.createRowBatch(DEFAULT_SIZE)
+ assert(!batch.selectedInUse, "No filters are allowed")
--- End diff --
No, ORC fills the batch after applying the push-downed filters.
`selectedInUse` is used by some operators of Hive in order to generate
derived row-batches.
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