[GitHub] spark pull request #15467: [SPARK-17912][SQL] Refactor code generation to ge...

[sameeragarwal <gi...@git.apache.org>]

Github user sameeragarwal commented on a diff in the pull request:

    https://github.com/apache/spark/pull/15467#discussion_r96527596
  
    --- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/ColumnarBatchScan.scala ---
    @@ -0,0 +1,151 @@
    +/*
    + * 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
    +
    +import org.apache.spark.sql.catalyst.expressions.UnsafeRow
    +import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
    +import org.apache.spark.sql.execution.columnar.InMemoryTableScanExec
    +import org.apache.spark.sql.execution.metric.SQLMetrics
    +import org.apache.spark.sql.execution.vectorized.{ColumnarBatch, ColumnVector}
    +import org.apache.spark.sql.types.DataType
    +
    +
    +/**
    + * Helper trait for abstracting scan functionality using
    + * [[org.apache.spark.sql.execution.vectorized.ColumnarBatch]]es.
    + */
    +private[sql] trait ColumnarBatchScan extends CodegenSupport {
    +
    +  val columnIndexes: Array[Int] = null
    +
    +  val inMemoryTableScan: InMemoryTableScanExec = null
    +
    +  override lazy val metrics = Map(
    +    "numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of output rows"),
    +    "scanTime" -> SQLMetrics.createTimingMetric(sparkContext, "scan time"))
    +
    +  lazy val enableScanStatistics: Boolean =
    +    sqlContext.getConf("spark.sql.inMemoryTableScanStatistics.enable", "false").toBoolean
    +
    +  /**
    +   * Generate [[ColumnVector]] expressions for our parent to consume as rows.
    +   * This is called once per [[ColumnarBatch]].
    +   */
    +  private def genCodeColumnVector(
    +      ctx: CodegenContext,
    +      columnVar: String,
    +      ordinal: String,
    +      dataType: DataType,
    +      nullable: Boolean): ExprCode = {
    +    val javaType = ctx.javaType(dataType)
    +    val value = ctx.getValue(columnVar, dataType, ordinal)
    +    val isNullVar = if (nullable) { ctx.freshName("isNull") } else { "false" }
    +    val valueVar = ctx.freshName("value")
    +    val str = s"columnVector[$columnVar, $ordinal, ${dataType.simpleString}]"
    +    val code = s"${ctx.registerComment(str)}\n" + (if (nullable) {
    +      s"""
    +        boolean $isNullVar = $columnVar.isNullAt($ordinal);
    +        $javaType $valueVar = $isNullVar ? ${ctx.defaultValue(dataType)} : ($value);
    +      """
    +    } else {
    +      s"$javaType $valueVar = $value;"
    +    }).trim
    +    ExprCode(code, isNullVar, valueVar)
    +  }
    +
    +  /**
    +   * Produce code to process the input iterator as [[ColumnarBatch]]es.
    +   * This produces an [[UnsafeRow]] for each row in each batch.
    +   */
    +  // TODO: return ColumnarBatch.Rows instead
    +  override protected def doProduce(ctx: CodegenContext): String = {
    +    val input = ctx.freshName("input")
    +    // PhysicalRDD always just has one input
    +    ctx.addMutableState("scala.collection.Iterator", input, s"$input = inputs[0];")
    +
    +    // metrics
    +    val numOutputRows = metricTerm(ctx, "numOutputRows")
    +    val scanTimeMetric = metricTerm(ctx, "scanTime")
    +    val scanTimeTotalNs = ctx.freshName("scanTime")
    +    ctx.addMutableState("long", scanTimeTotalNs, s"$scanTimeTotalNs = 0;")
    +    val incReadBatches = if (!enableScanStatistics) "" else {
    +      val readPartitions = ctx.addReferenceObj("readPartitions", inMemoryTableScan.readPartitions)
    +      val readBatches = ctx.addReferenceObj("readBatches", inMemoryTableScan.readBatches)
    +      ctx.addMutableState("int", "initializeInMemoryTableScanStatistics",
    +        s"""
    +           |$readPartitions.setValue(0);
    +           |$readBatches.setValue(0);
    +           |if ($input.hasNext()) { $readPartitions.add(1); }
    +       """.stripMargin)
    +      s"$readBatches.add(1);"
    +    }
    +
    +    val columnarBatchClz = "org.apache.spark.sql.execution.vectorized.ColumnarBatch"
    +    val batch = ctx.freshName("batch")
    +    ctx.addMutableState(columnarBatchClz, batch, s"$batch = null;")
    +
    +    val columnVectorClz = "org.apache.spark.sql.execution.vectorized.ColumnVector"
    +    val idx = ctx.freshName("batchIdx")
    +    ctx.addMutableState("int", idx, s"$idx = 0;")
    +    val colVars = output.indices.map(i => ctx.freshName("colInstance" + i))
    +    val columnAssigns = colVars.zipWithIndex.map { case (name, i) =>
    +      ctx.addMutableState(columnVectorClz, name, s"$name = null;")
    +      val index = if (columnIndexes == null) i else columnIndexes(i)
    --- End diff --
    
    same comment: maybe we can not introduce `columnIndexes` for now.


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