org.apache.hadoop.hive.ql.exec.vector.expressions.LongColDivideLongColumn Maven / Gradle / Ivy
/**
* 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.hadoop.hive.ql.exec.vector.expressions;
import org.apache.hadoop.hive.ql.exec.vector.DoubleColumnVector;
import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector;
import org.apache.hadoop.hive.ql.exec.vector.VectorExpressionDescriptor;
import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch;
/**
* This operation is handled as a special case because Hive
* long/long division returns double. This file is thus not generated
* from a template like the other arithmetic operations are.
*/
public class LongColDivideLongColumn extends VectorExpression {
private static final long serialVersionUID = 1L;
int colNum1;
int colNum2;
int outputColumn;
public LongColDivideLongColumn(int colNum1, int colNum2, int outputColumn) {
this();
this.colNum1 = colNum1;
this.colNum2 = colNum2;
this.outputColumn = outputColumn;
}
public LongColDivideLongColumn() {
super();
}
@Override
public void evaluate(VectorizedRowBatch batch) {
if (childExpressions != null) {
super.evaluateChildren(batch);
}
LongColumnVector inputColVector1 = (LongColumnVector) batch.cols[colNum1];
LongColumnVector inputColVector2 = (LongColumnVector) batch.cols[colNum2];
DoubleColumnVector outputColVector = (DoubleColumnVector) batch.cols[outputColumn];
int[] sel = batch.selected;
int n = batch.size;
long[] vector1 = inputColVector1.vector;
long[] vector2 = inputColVector2.vector;
double[] outputVector = outputColVector.vector;
// return immediately if batch is empty
if (n == 0) {
return;
}
outputColVector.isRepeating = inputColVector1.isRepeating && inputColVector2.isRepeating;
// Handle nulls first
NullUtil.propagateNullsColCol(
inputColVector1, inputColVector2, outputColVector, sel, n, batch.selectedInUse);
/* Disregard nulls for processing. In other words,
* the arithmetic operation is performed even if one or
* more inputs are null. This is to improve speed by avoiding
* conditional checks in the inner loop.
*/
boolean hasDivBy0 = false;
if (inputColVector1.isRepeating && inputColVector2.isRepeating) {
long denom = vector2[0];
outputVector[0] = vector1[0] / (double) denom;
hasDivBy0 = hasDivBy0 || (denom == 0);
} else if (inputColVector1.isRepeating) {
if (batch.selectedInUse) {
for(int j = 0; j != n; j++) {
int i = sel[j];
long denom = vector2[i];
outputVector[i] = vector1[0] / (double) denom;
hasDivBy0 = hasDivBy0 || (denom == 0);
}
} else {
for(int i = 0; i != n; i++) {
long denom = vector2[i];
outputVector[i] = vector1[0] / (double) denom;
hasDivBy0 = hasDivBy0 || (denom == 0);
}
}
} else if (inputColVector2.isRepeating) {
if (vector2[0] == 0) {
outputColVector.noNulls = false;
outputColVector.isRepeating = true;
outputColVector.isNull[0] = true;
} else if (batch.selectedInUse) {
for(int j = 0; j != n; j++) {
int i = sel[j];
outputVector[i] = vector1[i] / (double) vector2[0];
}
} else {
for(int i = 0; i != n; i++) {
outputVector[i] = vector1[i] / (double) vector2[0];
}
}
} else {
if (batch.selectedInUse) {
for(int j = 0; j != n; j++) {
int i = sel[j];
long denom = vector2[i];
outputVector[i] = vector1[i] / (double) denom;
hasDivBy0 = hasDivBy0 || (denom == 0);
}
} else {
for(int i = 0; i != n; i++) {
long denom = vector2[i];
outputVector[i] = vector1[i] / (double) denom;
hasDivBy0 = hasDivBy0 || (denom == 0);
}
}
}
/* For the case when the output can have null values, follow
* the convention that the data values must be 1 for long and
* NaN for double. This is to prevent possible later zero-divide errors
* in complex arithmetic expressions like col2 / (col1 - 1)
* in the case when some col1 entries are null.
*/
if (!hasDivBy0) {
NullUtil.setNullDataEntriesDouble(outputColVector, batch.selectedInUse, sel, n);
} else {
NullUtil.setNullAndDivBy0DataEntriesDouble(
outputColVector, batch.selectedInUse, sel, n, inputColVector2);
}
}
@Override
public int getOutputColumn() {
return outputColumn;
}
@Override
public String getOutputType() {
return "double";
}
public int getColNum1() {
return colNum1;
}
public void setColNum1(int colNum1) {
this.colNum1 = colNum1;
}
public int getColNum2() {
return colNum2;
}
public void setColNum2(int colNum2) {
this.colNum2 = colNum2;
}
public void setOutputColumn(int outputColumn) {
this.outputColumn = outputColumn;
}
@Override
public VectorExpressionDescriptor.Descriptor getDescriptor() {
return (new VectorExpressionDescriptor.Builder())
.setMode(
VectorExpressionDescriptor.Mode.PROJECTION)
.setNumArguments(2)
.setArgumentTypes(
VectorExpressionDescriptor.ArgumentType.INT_FAMILY,
VectorExpressionDescriptor.ArgumentType.INT_FAMILY)
.setInputExpressionTypes(
VectorExpressionDescriptor.InputExpressionType.COLUMN,
VectorExpressionDescriptor.InputExpressionType.COLUMN).build();
}
}