org.apache.hadoop.hive.ql.exec.vector.expressions.gen.TimestampColGreaterDoubleScalar Maven / Gradle / Ivy
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* to you under the Apache License, Version 2.0 (the
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*
* http://www.apache.org/licenses/LICENSE-2.0
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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.expressions.NullUtil;
import org.apache.hadoop.hive.ql.exec.vector.TimestampColumnVector;
import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector;
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.metadata.HiveException;
/**
* Generated from template TimestampColumnCompareScalar.txt, which covers comparison
* expressions between a Timestamp column and a long/double scalar. The boolean output is stored
* in a separate boolean column.
*/
public class TimestampColGreaterDoubleScalar extends VectorExpression {
private static final long serialVersionUID = 1L;
private final double value;
public TimestampColGreaterDoubleScalar(int colNum, double value, int outputColumnNum) {
super(colNum, outputColumnNum);
this.value = value;
}
public TimestampColGreaterDoubleScalar() {
super();
// Dummy final assignments.
value = 0;
}
@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);
}
TimestampColumnVector inputColVector = (TimestampColumnVector) batch.cols[inputColumnNum[0]];
LongColumnVector outputColVector = (LongColumnVector) batch.cols[outputColumnNum];
int[] sel = batch.selected;
boolean[] inputIsNull = inputColVector.isNull;
boolean[] outputIsNull = outputColVector.isNull;
long[] outputVector = outputColVector.vector;
// We do not need to do a column reset since we are carefully changing the output.
outputColVector.isRepeating = false;
if (inputColVector.isRepeating) {
if (inputColVector.noNulls || !inputIsNull[0]) {
outputIsNull[0] = false;
outputVector[0] = inputColVector.getDouble(0) > value ? 1 : 0;
} else {
outputIsNull[0] = true;
outputColVector.noNulls = false;
}
outputColVector.isRepeating = true;
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] =
inputColVector.getDouble(i) > value ? 1 : 0;
}
} else {
for(int j = 0; j != n; j++) {
final int i = sel[j];
outputVector[i] =
inputColVector.getDouble(i) > value ? 1 : 0;
}
}
} 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] =
inputColVector.getDouble(i) > value ? 1 : 0;
}
}
} else {
/*
* Do careful maintenance of the outputColVector.noNulls flag.
*/
if (batch.selectedInUse) {
for(int j=0; j != n; j++) {
int i = sel[j];
if (!inputIsNull[i]) {
outputIsNull[i] = false;
outputVector[i] = inputColVector.getDouble(i) > value ? 1 : 0;
} else {
// Comparison with NULL is NULL.
outputIsNull[i] = true;
outputColVector.noNulls = false;
}
}
} else {
for(int i = 0; i != n; i++) {
if (!inputIsNull[i]) {
outputIsNull[i] = false;
outputVector[i] = inputColVector.getDouble(i) > value ? 1 : 0;
} else {
// Comparison with NULL is NULL.
outputIsNull[i] = true;
outputColVector.noNulls = false;
}
}
}
}
}
@Override
public String vectorExpressionParameters() {
return getColumnParamString(0, inputColumnNum[0]) + ", val " + value;
}
@Override
public VectorExpressionDescriptor.Descriptor getDescriptor() {
return (new VectorExpressionDescriptor.Builder())
.setMode(
VectorExpressionDescriptor.Mode.PROJECTION)
.setNumArguments(2)
.setArgumentTypes(
VectorExpressionDescriptor.ArgumentType.getType("timestamp"),
VectorExpressionDescriptor.ArgumentType.getType("double"))
.setInputExpressionTypes(
VectorExpressionDescriptor.InputExpressionType.COLUMN,
VectorExpressionDescriptor.InputExpressionType.SCALAR).build();
}
}