org.apache.hadoop.hive.ql.exec.vector.expressions.MathFuncLongToDouble Maven / Gradle / Ivy
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* Licensed to the Apache Software Foundation (ASF) under one
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* 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.VectorizedRowBatch;
/**
* Implement vectorized math function that takes a double (and optionally additional
* constant argument(s)) and returns long.
* May be used for functions like ROUND(d, N), Pow(a, p) etc.
*
* Do NOT use this for simple math functions like sin/cos/exp etc. that just take
* a single argument. For those, modify the template ColumnUnaryFunc.txt
* and expand the template to generate needed classes.
*/
public abstract class MathFuncLongToDouble extends VectorExpression {
private static final long serialVersionUID = 1L;
private int colNum;
private int outputColumn;
// Subclasses must override this with a function that implements the desired logic.
protected abstract double func(long l);
public MathFuncLongToDouble(int colNum, int outputColumn) {
this.colNum = colNum;
this.outputColumn = outputColumn;
}
public MathFuncLongToDouble() {
}
@Override
public void evaluate(VectorizedRowBatch batch) {
if (childExpressions != null) {
this.evaluateChildren(batch);
}
LongColumnVector inputColVector = (LongColumnVector) batch.cols[colNum];
DoubleColumnVector outputColVector = (DoubleColumnVector) batch.cols[outputColumn];
int[] sel = batch.selected;
boolean[] inputIsNull = inputColVector.isNull;
boolean[] outputIsNull = outputColVector.isNull;
outputColVector.noNulls = inputColVector.noNulls;
int n = batch.size;
long[] vector = inputColVector.vector;
double[] outputVector = outputColVector.vector;
// return immediately if batch is empty
if (n == 0) {
return;
}
if (inputColVector.isRepeating) {
outputVector[0] = func(vector[0]);
// Even if there are no nulls, we always copy over entry 0. Simplifies code.
outputIsNull[0] = inputIsNull[0];
outputColVector.isRepeating = true;
} else if (inputColVector.noNulls) {
if (batch.selectedInUse) {
for(int j = 0; j != n; j++) {
int i = sel[j];
outputVector[i] = func(vector[i]);
}
} else {
for(int i = 0; i != n; i++) {
outputVector[i] = func(vector[i]);
}
}
outputColVector.isRepeating = false;
} else /* there are nulls */ {
if (batch.selectedInUse) {
for(int j = 0; j != n; j++) {
int i = sel[j];
outputVector[i] = func(vector[i]);
outputIsNull[i] = inputIsNull[i];
}
} else {
for(int i = 0; i != n; i++) {
outputVector[i] = func(vector[i]);
}
System.arraycopy(inputIsNull, 0, outputIsNull, 0, n);
}
outputColVector.isRepeating = false;
}
cleanup(outputColVector, sel, batch.selectedInUse, n);
}
// override this with a no-op if subclass doesn't need to treat NaN as null
protected void cleanup(DoubleColumnVector outputColVector, int[] sel,
boolean selectedInUse, int n) {
MathExpr.NaNToNull(outputColVector, sel, selectedInUse, n);
}
@Override
public int getOutputColumn() {
return outputColumn;
}
public void setOutputColumn(int outputColumn) {
this.outputColumn = outputColumn;
}
public int getColNum() {
return colNum;
}
public void setColNum(int colNum) {
this.colNum = colNum;
}
@Override
public String getOutputType() {
return "double";
}
}