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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;
import java.util.Arrays;
import org.apache.hadoop.hive.ql.exec.vector.BytesColumnVector;
import org.apache.hadoop.hive.ql.exec.vector.VectorExpressionDescriptor;
import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch;
import org.apache.hadoop.io.Text;
/**
* Expression for vectorized evaluation of unary UDFs on strings.
* An object of {@link IUDFUnaryString} is applied to every element of
* the vector.
*/
public class StringUnaryUDF extends VectorExpression {
public interface IUDFUnaryString {
Text evaluate(Text s);
}
private static final long serialVersionUID = 1L;
private int colNum;
private int outputColumn;
private IUDFUnaryString func;
private transient final Text s;
StringUnaryUDF(int colNum, int outputColumn, IUDFUnaryString func) {
this();
this.colNum = colNum;
this.outputColumn = outputColumn;
this.func = func;
}
public StringUnaryUDF() {
super();
s = new Text();
}
@Override
public void evaluate(VectorizedRowBatch batch) {
if (childExpressions != null) {
super.evaluateChildren(batch);
}
BytesColumnVector inputColVector = (BytesColumnVector) batch.cols[colNum];
int[] sel = batch.selected;
int n = batch.size;
byte[][] vector = inputColVector.vector;
int [] start = inputColVector.start;
int [] length = inputColVector.length;
BytesColumnVector outV = (BytesColumnVector) batch.cols[outputColumn];
outV.initBuffer();
Text t;
if (n == 0) {
//Nothing to do
return;
}
// Design Note: In the future, if this function can be implemented
// directly to translate input to output without creating new
// objects, performance can probably be improved significantly.
// It's implemented in the simplest way now, just calling the
// existing built-in function.
if (inputColVector.noNulls) {
outV.noNulls = true;
if (inputColVector.isRepeating) {
outV.isRepeating = true;
s.set(vector[0], start[0], length[0]);
t = func.evaluate(s);
setString(outV, 0, t);
} else if (batch.selectedInUse) {
for(int j = 0; j != n; j++) {
int i = sel[j];
/* Fill output isNull with false for selected elements since there is a chance we'll
* convert to noNulls == false in setString();
*/
outV.isNull[i] = false;
s.set(vector[i], start[i], length[i]);
t = func.evaluate(s);
setString(outV, i, t);
}
outV.isRepeating = false;
} else {
// Set all elements to not null. The setString call can override this.
Arrays.fill(outV.isNull, 0, n, false);
for(int i = 0; i != n; i++) {
s.set(vector[i], start[i], length[i]);
t = func.evaluate(s);
setString(outV, i, t);
}
outV.isRepeating = false;
}
} else {
// Handle case with nulls. Don't do function if the value is null, to save time,
// because calling the function can be expensive.
outV.noNulls = false;
if (inputColVector.isRepeating) {
outV.isRepeating = true;
outV.isNull[0] = inputColVector.isNull[0]; // setString can override this
if (!inputColVector.isNull[0]) {
s.set(vector[0], start[0], length[0]);
t = func.evaluate(s);
setString(outV, 0, t);
}
} else if (batch.selectedInUse) {
for(int j = 0; j != n; j++) {
int i = sel[j];
outV.isNull[i] = inputColVector.isNull[i]; // setString can override this
if (!inputColVector.isNull[i]) {
s.set(vector[i], start[i], length[i]);
t = func.evaluate(s);
setString(outV, i, t);
}
}
outV.isRepeating = false;
} else {
// setString can override this null propagation
System.arraycopy(inputColVector.isNull, 0, outV.isNull, 0, n);
for(int i = 0; i != n; i++) {
if (!inputColVector.isNull[i]) {
s.set(vector[i], start[i], length[i]);
t = func.evaluate(s);
setString(outV, i, t);
}
}
outV.isRepeating = false;
}
}
}
/* Set the output string entry i to the contents of Text object t.
* If t is a null object reference, record that the value is a SQL NULL.
*/
private static void setString(BytesColumnVector outV, int i, Text t) {
if (t == null) {
outV.noNulls = false;
outV.isNull[i] = true;
return;
}
outV.setVal(i, t.getBytes(), 0, t.getLength());
}
@Override
public int getOutputColumn() {
return outputColumn;
}
@Override
public String getOutputType() {
return "String";
}
public int getColNum() {
return colNum;
}
public void setColNum(int colNum) {
this.colNum = colNum;
}
public IUDFUnaryString getFunc() {
return func;
}
public void setFunc(IUDFUnaryString func) {
this.func = func;
}
public void setOutputColumn(int outputColumn) {
this.outputColumn = outputColumn;
}
@Override
public VectorExpressionDescriptor.Descriptor getDescriptor() {
VectorExpressionDescriptor.Builder b = new VectorExpressionDescriptor.Builder();
b.setMode(VectorExpressionDescriptor.Mode.PROJECTION)
.setNumArguments(1)
.setArgumentTypes(
VectorExpressionDescriptor.ArgumentType.STRING)
.setInputExpressionTypes(
VectorExpressionDescriptor.InputExpressionType.COLUMN);
return b.build();
}
}
