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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.sql.Timestamp;
import org.apache.hadoop.hive.common.type.HiveIntervalDayTime;
import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression;
import org.apache.hadoop.hive.ql.exec.vector.expressions.NullUtil;
import org.apache.hadoop.hive.ql.exec.vector.*;
import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch;
import org.apache.hadoop.hive.ql.exec.vector.VectorExpressionDescriptor;
import org.apache.hadoop.hive.ql.util.DateTimeMath;
import org.apache.hadoop.hive.ql.metadata.HiveException;
/**
* Generated from template TimestampColumnArithmeticTimestampColumnBase.txt, which covers binary arithmetic
* expressions between columns.
*/
public class TimestampColAddIntervalDayTimeColumn extends VectorExpression {
private static final long serialVersionUID = 1L;
private final int colNum1;
private final int colNum2;
private transient final DateTimeMath dtm = new DateTimeMath();
public TimestampColAddIntervalDayTimeColumn(int colNum1, int colNum2, int outputColumnNum) {
super(outputColumnNum);
this.colNum1 = colNum1;
this.colNum2 = colNum2;
}
public TimestampColAddIntervalDayTimeColumn() {
super();
// Dummy final assignments.
colNum1 = -1;
colNum2 = -1;
}
@Override
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);
}
// Input #1 is type timestamp.
TimestampColumnVector inputColVector1 = (TimestampColumnVector) batch.cols[colNum1];
// Input #2 is type interval_day_time.
IntervalDayTimeColumnVector inputColVector2 = (IntervalDayTimeColumnVector) batch.cols[colNum2];
// Output is type timestamp.
TimestampColumnVector outputColVector = (TimestampColumnVector) batch.cols[outputColumnNum];
int[] sel = batch.selected;
/*
* Propagate null values for a two-input operator and set isRepeating and noNulls appropriately.
*/
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.
*/
if (inputColVector1.isRepeating && inputColVector2.isRepeating) {
dtm.add(
inputColVector1.asScratchTimestamp(0), inputColVector2.asScratchIntervalDayTime(0), outputColVector.getScratchTimestamp());
outputColVector.setFromScratchTimestamp(0);
} else if (inputColVector1.isRepeating) {
Timestamp value1 = inputColVector1.asScratchTimestamp(0);
if (batch.selectedInUse) {
for(int j = 0; j != n; j++) {
int i = sel[j];
dtm.add(
value1, inputColVector2.asScratchIntervalDayTime(i), outputColVector.getScratchTimestamp());
outputColVector.setFromScratchTimestamp(i);
}
} else {
for(int i = 0; i != n; i++) {
dtm.add(
value1, inputColVector2.asScratchIntervalDayTime(i), outputColVector.getScratchTimestamp());
outputColVector.setFromScratchTimestamp(i);
}
}
} else if (inputColVector2.isRepeating) {
HiveIntervalDayTime value2 = inputColVector2.asScratchIntervalDayTime(0);
if (batch.selectedInUse) {
for(int j = 0; j != n; j++) {
int i = sel[j];
dtm.add(
inputColVector1.asScratchTimestamp(i), value2, outputColVector.getScratchTimestamp());
outputColVector.setFromScratchTimestamp(i);
}
} else {
for(int i = 0; i != n; i++) {
dtm.add(
inputColVector1.asScratchTimestamp(i), value2, outputColVector.getScratchTimestamp());
outputColVector.setFromScratchTimestamp(i);
}
}
} else {
if (batch.selectedInUse) {
for(int j = 0; j != n; j++) {
int i = sel[j];
dtm.add(
inputColVector1.asScratchTimestamp(i), inputColVector2.asScratchIntervalDayTime(i), outputColVector.getScratchTimestamp());
outputColVector.setFromScratchTimestamp(i);
}
} else {
for(int i = 0; i != n; i++) {
dtm.add(
inputColVector1.asScratchTimestamp(i), inputColVector2.asScratchIntervalDayTime(i), outputColVector.getScratchTimestamp());
outputColVector.setFromScratchTimestamp(i);
}
}
}
/* 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.
*/
NullUtil.setNullDataEntriesTimestamp(outputColVector, batch.selectedInUse, sel, n);
}
@Override
public String vectorExpressionParameters() {
return getColumnParamString(0, colNum1) + ", " + getColumnParamString(1, colNum2);
}
@Override
public VectorExpressionDescriptor.Descriptor getDescriptor() {
return (new VectorExpressionDescriptor.Builder())
.setMode(
VectorExpressionDescriptor.Mode.PROJECTION)
.setNumArguments(2)
.setArgumentTypes(
VectorExpressionDescriptor.ArgumentType.getType("timestamp"),
VectorExpressionDescriptor.ArgumentType.getType("interval_day_time"))
.setInputExpressionTypes(
VectorExpressionDescriptor.InputExpressionType.COLUMN,
VectorExpressionDescriptor.InputExpressionType.COLUMN).build();
}
}