Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance.
Project price only 1 $
You can buy this project and download/modify it how often you want.
org.apache.hadoop.hive.ql.exec.vector.expressions.aggregates.gen.VectorUDAFAvgDecimal64ToDecimal Maven / Gradle / Ivy
/*
* 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.aggregates.gen;
import java.util.ArrayList;
import java.util.List;
import org.apache.hadoop.hive.ql.exec.Description;
import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression;
import org.apache.hadoop.hive.ql.exec.vector.expressions.aggregates.VectorAggregateExpression;
import org.apache.hadoop.hive.ql.exec.vector.VectorAggregationBufferRow;
import org.apache.hadoop.hive.ql.exec.vector.VectorAggregationDesc;
import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch;
import org.apache.hadoop.hive.ql.exec.vector.ColumnVector;
import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector;
import org.apache.hadoop.hive.ql.exec.vector.DecimalColumnVector;
import org.apache.hadoop.hive.ql.exec.vector.Decimal64ColumnVector;
import org.apache.hadoop.hive.ql.exec.vector.StructColumnVector;
import org.apache.hadoop.hive.ql.metadata.HiveException;
import org.apache.hadoop.hive.ql.plan.AggregationDesc;
import org.apache.hadoop.hive.ql.udf.generic.GenericUDAFEvaluator;
import org.apache.hadoop.hive.ql.udf.generic.GenericUDAFEvaluator.Mode;
import org.apache.hadoop.hive.ql.util.JavaDataModel;
import org.apache.hadoop.hive.serde2.typeinfo.StructTypeInfo;
import org.apache.hadoop.hive.serde2.typeinfo.DecimalTypeInfo;
import org.apache.hadoop.hive.serde2.io.HiveDecimalWritable;
import com.google.common.base.Preconditions;
/**
* Generated from template VectorUDAFAvg.txt.
*/
@Description(name = "avg",
value = "_FUNC_(expr) - Returns the average value of expr (vectorized, type: decimal64)")
public class VectorUDAFAvgDecimal64ToDecimal extends VectorAggregateExpression {
private static final long serialVersionUID = 1L;
private int inputScale;
private DecimalTypeInfo outputDecimalTypeInfo;
private transient final HiveDecimalWritable temp = new HiveDecimalWritable();
/** class for storing the current aggregate value. */
static class Aggregation implements AggregationBuffer {
private static final long serialVersionUID = 1L;
// The max for 18 - 1 digits.
private static final long nearDecimal64Max =
HiveDecimalWritable.getDecimal64AbsMax(HiveDecimalWritable.DECIMAL64_DECIMAL_DIGITS - 1);
private final int inputScale;
private final HiveDecimalWritable temp;
//----------------------------------------------------------------------------------------------
private long sum;
private final HiveDecimalWritable regularDecimalSum = new HiveDecimalWritable(0);
transient private long count;
/**
* Value is explicitly (re)initialized in reset()
*/
private boolean usingRegularDecimal = false;
public Aggregation(int inputScale, HiveDecimalWritable temp) {
this.inputScale = inputScale;
this.temp = temp;
}
public void avgValue(long value) {
if (Math.abs(sum) > nearDecimal64Max) {
if (!usingRegularDecimal) {
usingRegularDecimal = true;
regularDecimalSum.deserialize64(sum, inputScale);
} else {
temp.deserialize64(sum, inputScale);
regularDecimalSum.mutateAdd(temp);
}
sum = value;
} else {
sum += value;
}
count++;
}
@Override
public int getVariableSize() {
throw new UnsupportedOperationException();
}
@Override
public void reset () {
usingRegularDecimal = false;
sum = 0;
regularDecimalSum.setFromLong(0);
count = 0L;
}
}
// This constructor is used to momentarily create the object so match can be called.
public VectorUDAFAvgDecimal64ToDecimal() {
super();
}
public VectorUDAFAvgDecimal64ToDecimal(VectorAggregationDesc vecAggrDesc) {
super(vecAggrDesc);
Preconditions.checkState(this.mode == GenericUDAFEvaluator.Mode.PARTIAL1);
init();
}
private void init() {
inputScale = ((DecimalTypeInfo) inputTypeInfo).getScale();
StructTypeInfo structTypeInfo = (StructTypeInfo) outputTypeInfo;
outputDecimalTypeInfo = (DecimalTypeInfo) structTypeInfo.getAllStructFieldTypeInfos().get(AVERAGE_SUM_FIELD_INDEX);
}
private Aggregation getCurrentAggregationBuffer(
VectorAggregationBufferRow[] aggregationBufferSets,
int bufferIndex,
int row) {
VectorAggregationBufferRow mySet = aggregationBufferSets[row];
Aggregation myagg = (Aggregation) mySet.getAggregationBuffer(bufferIndex);
return myagg;
}
@Override
public void aggregateInputSelection(
VectorAggregationBufferRow[] aggregationBufferSets,
int bufferIndex,
VectorizedRowBatch batch) throws HiveException {
int batchSize = batch.size;
if (batchSize == 0) {
return;
}
inputExpression.evaluate(batch);
Decimal64ColumnVector inputVector =
(Decimal64ColumnVector) batch.cols[
this.inputExpression.getOutputColumnNum()];
long[] vector = inputVector.vector;
if (inputVector.noNulls) {
if (inputVector.isRepeating) {
iterateNoNullsRepeatingWithAggregationSelection(
aggregationBufferSets, bufferIndex,
vector[0], batchSize);
} else {
if (batch.selectedInUse) {
iterateNoNullsSelectionWithAggregationSelection(
aggregationBufferSets, bufferIndex,
vector, batch.selected, batchSize);
} else {
iterateNoNullsWithAggregationSelection(
aggregationBufferSets, bufferIndex,
vector, batchSize);
}
}
} else {
if (inputVector.isRepeating) {
iterateHasNullsRepeatingWithAggregationSelection(
aggregationBufferSets, bufferIndex,
vector[0], batchSize, inputVector.isNull);
} else {
if (batch.selectedInUse) {
iterateHasNullsSelectionWithAggregationSelection(
aggregationBufferSets, bufferIndex,
vector, batchSize, batch.selected, inputVector.isNull);
} else {
iterateHasNullsWithAggregationSelection(
aggregationBufferSets, bufferIndex,
vector, batchSize, inputVector.isNull);
}
}
}
}
private void iterateNoNullsRepeatingWithAggregationSelection(
VectorAggregationBufferRow[] aggregationBufferSets,
int bufferIndex,
long value,
int batchSize) {
for (int i=0; i < batchSize; ++i) {
Aggregation myagg = getCurrentAggregationBuffer(
aggregationBufferSets,
bufferIndex,
i);
myagg.avgValue(value);
}
}
private void iterateNoNullsSelectionWithAggregationSelection(
VectorAggregationBufferRow[] aggregationBufferSets,
int bufferIndex,
long[] values,
int[] selection,
int batchSize) {
for (int i=0; i < batchSize; ++i) {
Aggregation myagg = getCurrentAggregationBuffer(
aggregationBufferSets,
bufferIndex,
i);
myagg.avgValue(values[selection[i]]);
}
}
private void iterateNoNullsWithAggregationSelection(
VectorAggregationBufferRow[] aggregationBufferSets,
int bufferIndex,
long[] values,
int batchSize) {
for (int i=0; i < batchSize; ++i) {
Aggregation myagg = getCurrentAggregationBuffer(
aggregationBufferSets,
bufferIndex,
i);
myagg.avgValue(values[i]);
}
}
private void iterateHasNullsRepeatingWithAggregationSelection(
VectorAggregationBufferRow[] aggregationBufferSets,
int bufferIndex,
long value,
int batchSize,
boolean[] isNull) {
if (isNull[0]) {
return;
}
for (int i=0; i < batchSize; ++i) {
Aggregation myagg = getCurrentAggregationBuffer(
aggregationBufferSets,
bufferIndex,
i);
myagg.avgValue(value);
}
}
private void iterateHasNullsSelectionWithAggregationSelection(
VectorAggregationBufferRow[] aggregationBufferSets,
int bufferIndex,
long[] values,
int batchSize,
int[] selection,
boolean[] isNull) {
for (int j=0; j < batchSize; ++j) {
int i = selection[j];
if (!isNull[i]) {
Aggregation myagg = getCurrentAggregationBuffer(
aggregationBufferSets,
bufferIndex,
j);
myagg.avgValue(values[i]);
}
}
}
private void iterateHasNullsWithAggregationSelection(
VectorAggregationBufferRow[] aggregationBufferSets,
int bufferIndex,
long[] values,
int batchSize,
boolean[] isNull) {
for (int i=0; i < batchSize; ++i) {
if (!isNull[i]) {
Aggregation myagg = getCurrentAggregationBuffer(
aggregationBufferSets,
bufferIndex,
i);
myagg.avgValue(values[i]);
}
}
}
@Override
public void aggregateInput(AggregationBuffer agg, VectorizedRowBatch batch)
throws HiveException {
inputExpression.evaluate(batch);
Decimal64ColumnVector inputVector =
(Decimal64ColumnVector) batch.cols[
this.inputExpression.getOutputColumnNum()];
int batchSize = batch.size;
if (batchSize == 0) {
return;
}
Aggregation myagg = (Aggregation)agg;
long[] vector = inputVector.vector;
if (inputVector.isRepeating) {
if (inputVector.noNulls || !inputVector.isNull[0]) {
final long value = vector[0];
for (int i = 0; i < batchSize; i++) {
myagg.avgValue(value);
}
}
return;
}
if (!batch.selectedInUse && inputVector.noNulls) {
iterateNoSelectionNoNulls(myagg, vector, batchSize);
} else if (!batch.selectedInUse) {
iterateNoSelectionHasNulls(myagg, vector, batchSize, inputVector.isNull);
} else if (inputVector.noNulls){
iterateSelectionNoNulls(myagg, vector, batchSize, batch.selected);
} else {
iterateSelectionHasNulls(myagg, vector, batchSize, inputVector.isNull, batch.selected);
}
}
private void iterateSelectionHasNulls(
Aggregation myagg,
long[] vector,
int batchSize,
boolean[] isNull,
int[] selected) {
for (int j=0; j< batchSize; ++j) {
int i = selected[j];
if (!isNull[i]) {
myagg.avgValue(vector[i]);
}
}
}
private void iterateSelectionNoNulls(
Aggregation myagg,
long[] vector,
int batchSize,
int[] selected) {
for (int i=0; i< batchSize; ++i) {
myagg.avgValue(vector[selected[i]]);
}
}
private void iterateNoSelectionHasNulls(
Aggregation myagg,
long[] vector,
int batchSize,
boolean[] isNull) {
for(int i=0;i 0) {
if (!myagg.usingRegularDecimal) {
myagg.regularDecimalSum.deserialize64(myagg.sum, inputScale);
} else {
myagg.temp.deserialize64(myagg.sum, inputScale);
myagg.regularDecimalSum.mutateAdd(myagg.temp);
}
// For AVG, we only mark NULL on actual overflow.
if (!myagg.regularDecimalSum.isSet()) {
outputColVector.noNulls = false;
outputColVector.isNull[batchIndex] = true;
return;
}
}
outputColVector.isNull[batchIndex] = false;
ColumnVector[] fields = outputColVector.fields;
fields[AVERAGE_COUNT_FIELD_INDEX].isNull[batchIndex] = false;
((LongColumnVector) fields[AVERAGE_COUNT_FIELD_INDEX]).vector[batchIndex] = myagg.count;
fields[AVERAGE_SUM_FIELD_INDEX].isNull[batchIndex] = false;
((DecimalColumnVector) fields[AVERAGE_SUM_FIELD_INDEX]).set(
batchIndex, myagg.regularDecimalSum);
// NULL out useless source field.
ColumnVector sourceColVector = (ColumnVector) fields[AVERAGE_SOURCE_FIELD_INDEX];
sourceColVector.isRepeating = true;
sourceColVector.noNulls = false;
sourceColVector.isNull[0] = true;
}
}
