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/*
* 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.gen;
import java.util.Arrays;
import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression;
import org.apache.hadoop.hive.ql.exec.vector.VectorExpressionDescriptor;
/*
* Because of the templatized nature of the code, either or both
* of these ColumnVector imports may be needed. Listing both of them
* rather than using ....vectorization.*;
*/
import org.apache.hadoop.hive.ql.exec.vector.expressions.OverflowUtils;
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.VectorizedRowBatch;
import org.apache.hadoop.hive.ql.exec.vector.expressions.NullUtil;
import org.apache.hadoop.hive.ql.metadata.HiveException;
/**
* Generated from template ScalarArithmeticColumn.txt.
* Implements a vectorized arithmetic operator with a scalar on the left and a
* column vector on the right. The result is output to an output column vector.
*/
public class LongScalarMultiplyLongColumnChecked extends VectorExpression {
private static final long serialVersionUID = 1L;
private final long value;
private final int colNum;
public LongScalarMultiplyLongColumnChecked(long value, int colNum, int outputColumnNum) {
super(outputColumnNum);
this.colNum = colNum;
this.value = value;
}
public LongScalarMultiplyLongColumnChecked() {
super();
// Dummy final assignments.
value = 0;
colNum = -1;
}
@Override
/**
* Method to evaluate scalar-column operation in vectorized fashion.
*
* @batch a package of rows with each column stored in a vector
*/
public void evaluate(VectorizedRowBatch batch) throws HiveException {
// return immediately if batch is empty
final int n = batch.size;
if (n == 0) {
return;
}
if (childExpressions != null) {
super.evaluateChildren(batch);
}
LongColumnVector inputColVector = (LongColumnVector) batch.cols[colNum];
LongColumnVector outputColVector = (LongColumnVector) batch.cols[outputColumnNum];
int[] sel = batch.selected;
boolean[] inputIsNull = inputColVector.isNull;
boolean[] outputIsNull = outputColVector.isNull;
// We do not need to do a column reset since we are carefully changing the output.
outputColVector.isRepeating = false;
long[] vector = inputColVector.vector;
long[] outputVector = outputColVector.vector;
if (inputColVector.isRepeating) {
if (inputColVector.noNulls || !inputIsNull[0]) {
outputIsNull[0] = false;
outputVector[0] = value * vector[0];
} else {
outputIsNull[0] = true;
outputColVector.noNulls = false;
}
outputColVector.isRepeating = true;
//when operating in checked mode make sure we handle overflows similar to non-vectorized expression
OverflowUtils.accountForOverflowLong(getOutputTypeInfo(), outputColVector,
batch.selectedInUse, sel, n);
NullUtil.setNullOutputEntriesColScalar(outputColVector, batch.selectedInUse, sel, n);
return;
}
if (inputColVector.noNulls) {
if (batch.selectedInUse) {
// CONSIDER: For large n, fill n or all of isNull array and use the tighter ELSE loop.
if (!outputColVector.noNulls) {
for(int j = 0; j != n; j++) {
final int i = sel[j];
outputIsNull[i] = false;
outputVector[i] = value * vector[i];
}
} else {
for(int j = 0; j != n; j++) {
final int i = sel[j];
outputVector[i] = value * vector[i];
}
}
} else {
if (!outputColVector.noNulls) {
// Assume it is almost always a performance win to fill all of isNull so we can
// safely reset noNulls.
Arrays.fill(outputIsNull, false);
outputColVector.noNulls = true;
}
for(int i = 0; i != n; i++) {
outputVector[i] = value * vector[i];
}
}
} else /* there are NULLs in the inputColVector */ {
// Carefully handle NULLs...
/*
* For better performance on LONG/DOUBLE we don't want the conditional
* statements inside the for loop.
*/
outputColVector.noNulls = false;
if (batch.selectedInUse) {
for(int j = 0; j != n; j++) {
int i = sel[j];
outputIsNull[i] = inputIsNull[i];
outputVector[i] = value * vector[i];
}
} else {
System.arraycopy(inputIsNull, 0, outputIsNull, 0, n);
for(int i = 0; i != n; i++) {
outputVector[i] = value * vector[i];
}
}
}
//when operating in checked mode make sure we handle overflows similar to non-vectorized expression
OverflowUtils.accountForOverflowLong(getOutputTypeInfo(), outputColVector,
batch.selectedInUse, sel, n);
NullUtil.setNullOutputEntriesColScalar(outputColVector, batch.selectedInUse, sel, n);
}
@Override
public boolean supportsCheckedExecution() {
return true;
}
@Override
public String vectorExpressionParameters() {
return "val " + value + ", " + getColumnParamString(1, colNum);
}
@Override
public VectorExpressionDescriptor.Descriptor getDescriptor() {
return (new VectorExpressionDescriptor.Builder())
.setMode(
VectorExpressionDescriptor.Mode.PROJECTION)
.setNumArguments(2)
.setArgumentTypes(
VectorExpressionDescriptor.ArgumentType.getType("long"),
VectorExpressionDescriptor.ArgumentType.getType("long"))
.setInputExpressionTypes(
VectorExpressionDescriptor.InputExpressionType.SCALAR,
VectorExpressionDescriptor.InputExpressionType.COLUMN).build();
}
}
