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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.udf.generic;
import java.util.HashSet;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.apache.hadoop.hive.common.type.HiveDecimal;
import org.apache.hadoop.hive.ql.exec.Description;
import org.apache.hadoop.hive.ql.exec.UDFArgumentTypeException;
import org.apache.hadoop.hive.ql.metadata.HiveException;
import org.apache.hadoop.hive.ql.parse.SemanticException;
import org.apache.hadoop.hive.ql.plan.ptf.WindowFrameDef;
import org.apache.hadoop.hive.ql.util.JavaDataModel;
import org.apache.hadoop.hive.serde2.io.DoubleWritable;
import org.apache.hadoop.hive.serde2.io.HiveDecimalWritable;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils;
import org.apache.hadoop.hive.serde2.objectinspector.PrimitiveObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils.ObjectInspectorCopyOption;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils.ObjectInspectorObject;
import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorFactory;
import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorUtils;
import org.apache.hadoop.hive.serde2.typeinfo.DecimalTypeInfo;
import org.apache.hadoop.hive.serde2.typeinfo.HiveDecimalUtils;
import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo;
import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo;
import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoFactory;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.util.StringUtils;
/**
* GenericUDAFSum.
*
*/
@Description(name = "sum", value = "_FUNC_(x) - Returns the sum of a set of numbers")
public class GenericUDAFSum extends AbstractGenericUDAFResolver {
static final Logger LOG = LoggerFactory.getLogger(GenericUDAFSum.class.getName());
@Override
public GenericUDAFEvaluator getEvaluator(TypeInfo[] parameters)
throws SemanticException {
if (parameters.length != 1) {
throw new UDFArgumentTypeException(parameters.length - 1,
"Exactly one argument is expected.");
}
if (parameters[0].getCategory() != ObjectInspector.Category.PRIMITIVE) {
throw new UDFArgumentTypeException(0,
"Only primitive type arguments are accepted but "
+ parameters[0].getTypeName() + " is passed.");
}
switch (((PrimitiveTypeInfo) parameters[0]).getPrimitiveCategory()) {
case BYTE:
case SHORT:
case INT:
case LONG:
return new GenericUDAFSumLong();
case TIMESTAMP:
case FLOAT:
case DOUBLE:
case STRING:
case VARCHAR:
case CHAR:
return new GenericUDAFSumDouble();
case DECIMAL:
return new GenericUDAFSumHiveDecimal();
case BOOLEAN:
case DATE:
default:
throw new UDFArgumentTypeException(0,
"Only numeric or string type arguments are accepted but "
+ parameters[0].getTypeName() + " is passed.");
}
}
@Override
public GenericUDAFEvaluator getEvaluator(GenericUDAFParameterInfo info)
throws SemanticException {
TypeInfo[] parameters = info.getParameters();
GenericUDAFSumEvaluator eval = (GenericUDAFSumEvaluator) getEvaluator(parameters);
eval.setWindowing(info.isWindowing());
eval.setSumDistinct(info.isDistinct());
return eval;
}
public static PrimitiveObjectInspector.PrimitiveCategory getReturnType(TypeInfo type) {
if (type.getCategory() != ObjectInspector.Category.PRIMITIVE) {
return null;
}
switch (((PrimitiveTypeInfo) type).getPrimitiveCategory()) {
case BYTE:
case SHORT:
case INT:
case LONG:
return PrimitiveObjectInspector.PrimitiveCategory.LONG;
case TIMESTAMP:
case FLOAT:
case DOUBLE:
case STRING:
case VARCHAR:
case CHAR:
return PrimitiveObjectInspector.PrimitiveCategory.DOUBLE;
case DECIMAL:
return PrimitiveObjectInspector.PrimitiveCategory.DECIMAL;
}
return null;
}
/**
* The base type for sum operator evaluator
*
*/
public static abstract class GenericUDAFSumEvaluator<ResultType extends Writable> extends GenericUDAFEvaluator {
static abstract class SumAgg<T> extends AbstractAggregationBuffer {
boolean empty;
T sum;
HashSet<ObjectInspectorObject> uniqueObjects; // Unique rows.
}
protected PrimitiveObjectInspector inputOI;
protected PrimitiveObjectInspector outputOI;
protected ResultType result;
protected boolean isWindowing;
protected boolean sumDistinct;
public void setWindowing(boolean isWindowing) {
this.isWindowing = isWindowing;
}
public void setSumDistinct(boolean sumDistinct) {
this.sumDistinct = sumDistinct;
}
protected boolean isWindowingDistinct() {
return isWindowing && sumDistinct;
}
@Override
public Object terminatePartial(AggregationBuffer agg) throws HiveException {
if (isWindowingDistinct()) {
throw new HiveException("Distinct windowing UDAF doesn't support merge and terminatePartial");
} else {
return terminate(agg);
}
}
/**
* Check if the input object is eligible to contribute to the sum. If it's null
* or the same value as the previous one for the case of SUM(DISTINCT). Then
* skip it.
* @param input the input object
* @return True if sumDistinct is false or the non-null input is different from the previous object
*/
protected boolean isEligibleValue(SumAgg agg, Object input) {
if (input == null) {
return false;
}
if (isWindowingDistinct()) {
HashSet<ObjectInspectorObject> uniqueObjs = agg.uniqueObjects;
ObjectInspectorObject obj = input instanceof ObjectInspectorObject ?
(ObjectInspectorObject)input :
new ObjectInspectorObject(
ObjectInspectorUtils.copyToStandardObject(input, inputOI, ObjectInspectorCopyOption.JAVA),
outputOI);
if (!uniqueObjs.contains(obj)) {
uniqueObjs.add(obj);
return true;
}
return false;
}
return true;
}
}
/**
* GenericUDAFSumHiveDecimal.
*
*/
public static class GenericUDAFSumHiveDecimal extends GenericUDAFSumEvaluator<HiveDecimalWritable> {
@Override
public ObjectInspector init(Mode m, ObjectInspector[] parameters) throws HiveException {
assert (parameters.length == 1);
super.init(m, parameters);
result = new HiveDecimalWritable(0);
inputOI = (PrimitiveObjectInspector) parameters[0];
// The output precision is 10 greater than the input which should cover at least
// 10b rows. The scale is the same as the input.
DecimalTypeInfo outputTypeInfo = null;
if (mode == Mode.PARTIAL1 || mode == Mode.COMPLETE) {
int precision = Math.min(HiveDecimal.MAX_PRECISION, inputOI.precision() + 10);
outputTypeInfo = TypeInfoFactory.getDecimalTypeInfo(precision, inputOI.scale());
} else {
outputTypeInfo = (DecimalTypeInfo) inputOI.getTypeInfo();
}
ObjectInspector oi = PrimitiveObjectInspectorFactory.getPrimitiveWritableObjectInspector(outputTypeInfo);
outputOI = (PrimitiveObjectInspector) ObjectInspectorUtils.getStandardObjectInspector(
oi, ObjectInspectorCopyOption.JAVA);
return oi;
}
/** class for storing decimal sum value. */
@AggregationType(estimable = false) // hard to know exactly for decimals
static class SumHiveDecimalWritableAgg extends SumAgg<HiveDecimalWritable> {
}
@Override
public AggregationBuffer getNewAggregationBuffer() throws HiveException {
SumHiveDecimalWritableAgg agg = new SumHiveDecimalWritableAgg();
reset(agg);
return agg;
}
@Override
public void reset(AggregationBuffer agg) throws HiveException {
SumAgg<HiveDecimalWritable> bdAgg = (SumAgg<HiveDecimalWritable>) agg;
bdAgg.empty = true;
bdAgg.sum = new HiveDecimalWritable(0);
bdAgg.uniqueObjects = new HashSet<ObjectInspectorObject>();
}
boolean warned = false;
@Override
public void iterate(AggregationBuffer agg, Object[] parameters) throws HiveException {
assert (parameters.length == 1);
try {
if (isEligibleValue((SumHiveDecimalWritableAgg) agg, parameters[0])) {
((SumHiveDecimalWritableAgg)agg).empty = false;
((SumHiveDecimalWritableAgg)agg).sum.mutateAdd(
PrimitiveObjectInspectorUtils.getHiveDecimal(parameters[0], inputOI));
}
} catch (NumberFormatException e) {
if (!warned) {
warned = true;
LOG.warn(getClass().getSimpleName() + " "
+ StringUtils.stringifyException(e));
LOG
.warn(getClass().getSimpleName()
+ " ignoring similar exceptions.");
}
}
}
@Override
public void merge(AggregationBuffer agg, Object partial) throws HiveException {
if (partial != null) {
SumHiveDecimalWritableAgg myagg = (SumHiveDecimalWritableAgg) agg;
if (myagg.sum == null || !myagg.sum.isSet()) {
return;
}
myagg.empty = false;
if (isWindowingDistinct()) {
throw new HiveException("Distinct windowing UDAF doesn't support merge and terminatePartial");
} else {
myagg.sum.mutateAdd(PrimitiveObjectInspectorUtils.getHiveDecimal(partial, inputOI));
}
}
}
@Override
public Object terminate(AggregationBuffer agg) throws HiveException {
SumHiveDecimalWritableAgg myagg = (SumHiveDecimalWritableAgg) agg;
if (myagg.empty || myagg.sum == null || !myagg.sum.isSet()) {
return null;
}
DecimalTypeInfo decimalTypeInfo = (DecimalTypeInfo)outputOI.getTypeInfo();
myagg.sum.mutateEnforcePrecisionScale(decimalTypeInfo.getPrecision(), decimalTypeInfo.getScale());
if (!myagg.sum.isSet()) {
LOG.warn("The sum of a column with data type HiveDecimal is out of range");
return null;
}
result.set(myagg.sum);
return result;
}
@Override
public GenericUDAFEvaluator getWindowingEvaluator(WindowFrameDef wFrameDef) {
// Don't use streaming for distinct cases
if (sumDistinct) {
return null;
}
return new GenericUDAFStreamingEvaluator.SumAvgEnhancer<HiveDecimalWritable, HiveDecimal>(
this, wFrameDef) {
@Override
protected HiveDecimalWritable getNextResult(
org.apache.hadoop.hive.ql.udf.generic.GenericUDAFStreamingEvaluator.SumAvgEnhancer<HiveDecimalWritable, HiveDecimal>.SumAvgStreamingState ss)
throws HiveException {
SumHiveDecimalWritableAgg myagg = (SumHiveDecimalWritableAgg) ss.wrappedBuf;
HiveDecimal r = myagg.empty ? null : myagg.sum.getHiveDecimal();
HiveDecimal d = ss.retrieveNextIntermediateValue();
if (d != null ) {
r = r == null ? null : r.subtract(d);
}
return r == null ? null : new HiveDecimalWritable(r);
}
@Override
protected HiveDecimal getCurrentIntermediateResult(
org.apache.hadoop.hive.ql.udf.generic.GenericUDAFStreamingEvaluator.SumAvgEnhancer<HiveDecimalWritable, HiveDecimal>.SumAvgStreamingState ss)
throws HiveException {
SumHiveDecimalWritableAgg myagg = (SumHiveDecimalWritableAgg) ss.wrappedBuf;
return myagg.empty ? null : myagg.sum.getHiveDecimal();
}
};
}
}
/**
* GenericUDAFSumDouble.
*
*/
public static class GenericUDAFSumDouble extends GenericUDAFSumEvaluator<DoubleWritable> {
@Override
public ObjectInspector init(Mode m, ObjectInspector[] parameters) throws HiveException {
assert (parameters.length == 1);
super.init(m, parameters);
result = new DoubleWritable(0);
inputOI = (PrimitiveObjectInspector) parameters[0];
outputOI = (PrimitiveObjectInspector)ObjectInspectorUtils.getStandardObjectInspector(inputOI,
ObjectInspectorCopyOption.JAVA);
return PrimitiveObjectInspectorFactory.writableDoubleObjectInspector;
}
/** class for storing double sum value. */
@AggregationType(estimable = true)
static class SumDoubleAgg extends SumAgg<Double> {
@Override
public int estimate() { return JavaDataModel.PRIMITIVES1 + JavaDataModel.PRIMITIVES2; }
}
@Override
public AggregationBuffer getNewAggregationBuffer() throws HiveException {
SumDoubleAgg result = new SumDoubleAgg();
reset(result);
return result;
}
@Override
public void reset(AggregationBuffer agg) throws HiveException {
SumDoubleAgg myagg = (SumDoubleAgg) agg;
myagg.empty = true;
myagg.sum = 0.0;
myagg.uniqueObjects = new HashSet<ObjectInspectorObject>();
}
boolean warned = false;
@Override
public void iterate(AggregationBuffer agg, Object[] parameters) throws HiveException {
assert (parameters.length == 1);
try {
if (isEligibleValue((SumDoubleAgg) agg, parameters[0])) {
((SumDoubleAgg)agg).empty = false;
((SumDoubleAgg)agg).sum += PrimitiveObjectInspectorUtils.getDouble(parameters[0], inputOI);
}
} catch (NumberFormatException e) {
if (!warned) {
warned = true;
LOG.warn(getClass().getSimpleName() + " "
+ StringUtils.stringifyException(e));
LOG
.warn(getClass().getSimpleName()
+ " ignoring similar exceptions.");
}
}
}
@Override
public void merge(AggregationBuffer agg, Object partial) throws HiveException {
if (partial != null) {
SumDoubleAgg myagg = (SumDoubleAgg) agg;
myagg.empty = false;
if (isWindowingDistinct()) {
throw new HiveException("Distinct windowing UDAF doesn't support merge and terminatePartial");
} else {
myagg.sum += PrimitiveObjectInspectorUtils.getDouble(partial, inputOI);
}
}
}
@Override
public Object terminate(AggregationBuffer agg) throws HiveException {
SumDoubleAgg myagg = (SumDoubleAgg) agg;
if (myagg.empty) {
return null;
}
result.set(myagg.sum);
return result;
}
@Override
public GenericUDAFEvaluator getWindowingEvaluator(WindowFrameDef wFrameDef) {
// Don't use streaming for distinct cases
if (sumDistinct) {
return null;
}
return new GenericUDAFStreamingEvaluator.SumAvgEnhancer<DoubleWritable, Double>(this,
wFrameDef) {
@Override
protected DoubleWritable getNextResult(
org.apache.hadoop.hive.ql.udf.generic.GenericUDAFStreamingEvaluator.SumAvgEnhancer<DoubleWritable, Double>.SumAvgStreamingState ss)
throws HiveException {
SumDoubleAgg myagg = (SumDoubleAgg) ss.wrappedBuf;
Double r = myagg.empty ? null : myagg.sum;
Double d = ss.retrieveNextIntermediateValue();
if (d != null) {
r = r == null ? null : r - d;
}
return r == null ? null : new DoubleWritable(r);
}
@Override
protected Double getCurrentIntermediateResult(
org.apache.hadoop.hive.ql.udf.generic.GenericUDAFStreamingEvaluator.SumAvgEnhancer<DoubleWritable, Double>.SumAvgStreamingState ss)
throws HiveException {
SumDoubleAgg myagg = (SumDoubleAgg) ss.wrappedBuf;
return myagg.empty ? null : new Double(myagg.sum);
}
};
}
}
/**
* GenericUDAFSumLong.
*
*/
public static class GenericUDAFSumLong extends GenericUDAFSumEvaluator<LongWritable> {
@Override
public ObjectInspector init(Mode m, ObjectInspector[] parameters) throws HiveException {
assert (parameters.length == 1);
super.init(m, parameters);
result = new LongWritable(0);
inputOI = (PrimitiveObjectInspector) parameters[0];
outputOI = (PrimitiveObjectInspector)ObjectInspectorUtils.getStandardObjectInspector(inputOI,
ObjectInspectorCopyOption.JAVA);
return PrimitiveObjectInspectorFactory.writableLongObjectInspector;
}
/** class for storing double sum value. */
@AggregationType(estimable = true)
static class SumLongAgg extends SumAgg<Long> {
@Override
public int estimate() { return JavaDataModel.PRIMITIVES1 + JavaDataModel.PRIMITIVES2; }
}
@Override
public AggregationBuffer getNewAggregationBuffer() throws HiveException {
SumLongAgg result = new SumLongAgg();
reset(result);
return result;
}
@Override
public void reset(AggregationBuffer agg) throws HiveException {
SumLongAgg myagg = (SumLongAgg) agg;
myagg.empty = true;
myagg.sum = 0L;
myagg.uniqueObjects = new HashSet<ObjectInspectorObject>();
}
private boolean warned = false;
@Override
public void iterate(AggregationBuffer agg, Object[] parameters) throws HiveException {
assert (parameters.length == 1);
try {
if (isEligibleValue((SumLongAgg) agg, parameters[0])) {
((SumLongAgg)agg).empty = false;
((SumLongAgg)agg).sum += PrimitiveObjectInspectorUtils.getLong(parameters[0], inputOI);
}
} catch (NumberFormatException e) {
if (!warned) {
warned = true;
LOG.warn(getClass().getSimpleName() + " "
+ StringUtils.stringifyException(e));
}
}
}
@Override
public void merge(AggregationBuffer agg, Object partial) throws HiveException {
if (partial != null) {
SumLongAgg myagg = (SumLongAgg) agg;
myagg.empty = false;
if (isWindowingDistinct()) {
throw new HiveException("Distinct windowing UDAF doesn't support merge and terminatePartial");
} else {
myagg.sum += PrimitiveObjectInspectorUtils.getLong(partial, inputOI);
}
}
}
@Override
public Object terminate(AggregationBuffer agg) throws HiveException {
SumLongAgg myagg = (SumLongAgg) agg;
if (myagg.empty) {
return null;
}
result.set(myagg.sum);
return result;
}
@Override
public GenericUDAFEvaluator getWindowingEvaluator(WindowFrameDef wFrameDef) {
// Don't use streaming for distinct cases
if (isWindowingDistinct()) {
return null;
}
return new GenericUDAFStreamingEvaluator.SumAvgEnhancer<LongWritable, Long>(this,
wFrameDef) {
@Override
protected LongWritable getNextResult(
org.apache.hadoop.hive.ql.udf.generic.GenericUDAFStreamingEvaluator.SumAvgEnhancer<LongWritable, Long>.SumAvgStreamingState ss)
throws HiveException {
SumLongAgg myagg = (SumLongAgg) ss.wrappedBuf;
Long r = myagg.empty ? null : myagg.sum;
Long d = ss.retrieveNextIntermediateValue();
if (d != null) {
r = r == null ? null : r - d;
}
return r == null ? null : new LongWritable(r);
}
@Override
protected Long getCurrentIntermediateResult(
org.apache.hadoop.hive.ql.udf.generic.GenericUDAFStreamingEvaluator.SumAvgEnhancer<LongWritable, Long>.SumAvgStreamingState ss)
throws HiveException {
SumLongAgg myagg = (SumLongAgg) ss.wrappedBuf;
return myagg.empty ? null : new Long(myagg.sum);
}
};
}
}
}
ddd
GenericUDAF
/**
* 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.udf.generic;
import java.io.Closeable;
import java.io.IOException;
import java.lang.annotation.Retention;
import java.lang.annotation.RetentionPolicy;
import org.apache.hadoop.hive.ql.exec.MapredContext;
import org.apache.hadoop.hive.ql.metadata.HiveException;
import org.apache.hadoop.hive.ql.plan.ptf.WindowFrameDef;
import org.apache.hadoop.hive.ql.udf.UDFType;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;
import org.apache.hive.common.util.AnnotationUtils;
/**
* A Generic User-defined aggregation function (GenericUDAF) for the use with
* Hive.
*
* New GenericUDAF classes need to inherit from this GenericUDAF class.
*
* The GenericUDAF are superior to normal UDAFs in the following ways: 1. It can
* accept arguments of complex types, and return complex types. 2. It can accept
* variable length of arguments. 3. It can accept an infinite number of function
* signature - for example, it's easy to write a GenericUDAF that accepts
* array<int>, array<array<int>> and so on (arbitrary levels of nesting).
*/
@UDFType(deterministic = true)
public abstract class GenericUDAFEvaluator implements Closeable {
@Retention(RetentionPolicy.RUNTIME)
public static @interface AggregationType {
boolean estimable() default false;
}
public static boolean isEstimable(AggregationBuffer buffer) {
if (buffer instanceof AbstractAggregationBuffer) {
Class<? extends AggregationBuffer> clazz = buffer.getClass();
AggregationType annotation = AnnotationUtils.getAnnotation(clazz, AggregationType.class);
return annotation != null && annotation.estimable();
}
return false;
}
/**
* Mode.
*
*/
public static enum Mode {
/**
* PARTIAL1: from original data to partial aggregation data: iterate() and
* terminatePartial() will be called.
*/
PARTIAL1, 相当于map阶段，调用iterate()和terminatePartial()
/**
* PARTIAL2: from partial aggregation data to partial aggregation data:
* merge() and terminatePartial() will be called.
*/
PARTIAL2, 相当于combiner阶段，调用merge()和terminatePartial()
/**
* FINAL: from partial aggregation to full aggregation: merge() and
* terminate() will be called.
*/
FINAL, 相当于reduce阶段调用merge()和terminate()
/**
* COMPLETE: from original data directly to full aggregation: iterate() and
* terminate() will be called.
*/
COMPLETE COMPLETE: 相当于没有reduce阶段map，调用iterate()和terminate()
};
Mode mode;
/**
* The constructor.
*/
public GenericUDAFEvaluator() {
}
/**
* Additionally setup GenericUDAFEvaluator with MapredContext before initializing.
* This is only called in runtime of MapRedTask.
*
* @param mapredContext context
*/
public void configure(MapredContext mapredContext) {
}
/**
* Initialize the evaluator.
*
* @param m mode Init方式 mode在初始四个方法需要的调用或者初始化
* The mode of aggregation.
* @param parameters
* The ObjectInspector for the parameters: In PARTIAL1 and COMPLETE 在partial1 complelte 存储是初始化数据，原理很简单。parital1是map complete 是没有map 的reduce
* mode, the parameters are original data; In PARTIAL2 and FINAL
* mode, the parameters are just partial aggregations (in that case,剩下两个是聚合后的数据。
* the array will always have a single element).
* @return The ObjectInspector for the return value. In PARTIAL1 and PARTIAL2
* mode, the ObjectInspector for the return value of
* terminatePartial() call; In FINAL and COMPLETE mode, the
* ObjectInspector for the return value of terminate() call.
*
* NOTE: We need ObjectInspector[] (in addition to the TypeInfo[] in
* GenericUDAFResolver) for 2 reasons: 1. ObjectInspector contains
* more information than TypeInfo; and GenericUDAFEvaluator.init at
* execution time. 2. We call GenericUDAFResolver.getEvaluator at
* compilation time,
*/
public ObjectInspector init(Mode m, ObjectInspector[] parameters) throws HiveException {
// This function should be overriden in every sub class
// And the sub class should call super.init(m, parameters) to get mode set.
mode = m;
return null;
}
/**
* The interface for a class that is used to store the aggregation result
* during the process of aggregation.
*
* We split this piece of data out because there can be millions of instances
* of this Aggregation in hash-based aggregation process, and it's very
* important to conserve memory.
*
* In the future, we may completely hide this class inside the Evaluator and
* use integer numbers to identify which aggregation we are looking at.
*
* @deprecated use {@link AbstractAggregationBuffer} instead
*/
public static interface AggregationBuffer {
};
public static abstract class AbstractAggregationBuffer implements AggregationBuffer {
/**
* Estimate the size of memory which is occupied by aggregation buffer.
* Currently, hive assumes that primitives types occupies 16 byte and java object has
* 64 byte overhead for each. For map, each entry also has 64 byte overhead.
*/
public int estimate() { return -1; }
}
/**
* Get a new aggregation object.
*/
public abstract AggregationBuffer getNewAggregationBuffer() throws HiveException;
/**
* Reset the aggregation. This is useful if we want to reuse the same
* aggregation.
*/
public abstract void reset(AggregationBuffer agg) throws HiveException;
/**
* Close GenericUDFEvaluator.
* This is only called in runtime of MapRedTask.
*/
public void close() throws IOException {
}
/**
* This function will be called by GroupByOperator when it sees a new input
* row.
*
* @param agg
* The object to store the aggregation result.
* @param parameters
* The row, can be inspected by the OIs passed in init().
*/
public void aggregate(AggregationBuffer agg, Object[] parameters) throws HiveException {
if (mode == Mode.PARTIAL1 || mode == Mode.COMPLETE) {
iterate(agg, parameters);
} else {
assert (parameters.length == 1);
merge(agg, parameters[0]);
}
}
/**
* This function will be called by GroupByOperator when it sees a new input
* row.
*
* @param agg
* The object to store the aggregation result.
*/
public Object evaluate(AggregationBuffer agg) throws HiveException {
if (mode == Mode.PARTIAL1 || mode == Mode.PARTIAL2) {
return terminatePartial(agg);
} else {
return terminate(agg);
}
}
/**
* Iterate through original data.
*
* @param parameters
* The objects of parameters.
*/
public abstract void iterate(AggregationBuffer agg, Object[] parameters) throws HiveException;
/**
* Get partial aggregation result.
*
* @return partial aggregation result.
*/
public abstract Object terminatePartial(AggregationBuffer agg) throws HiveException;
/**
* Merge with partial aggregation result. NOTE: null might be passed in case
* there is no input data.
*
* @param partial
* The partial aggregation result.
*/
public abstract void merge(AggregationBuffer agg, Object partial) throws HiveException;
/**
* Get final aggregation result.
*
* @return final aggregation result.
*/
public abstract Object terminate(AggregationBuffer agg) throws HiveException;
/**
* When evaluating an aggregates over a fixed Window, the naive way to compute
* results is to compute the aggregate for each row. But often there is a way
* to compute results in a more efficient manner. This method enables the
* basic evaluator to provide a function object that does the job in a more
* efficient manner.
* <p>
* This method is called after this Evaluator is initialized. The returned
* Function must be initialized. It is passed the 'window' of aggregation for
* each row.
*
* @param wFrmDef
* the Window definition in play for this evaluation.
* @return null implies that this fn cannot be processed in Streaming mode. So
* each row is evaluated independently.
*/
public GenericUDAFEvaluator getWindowingEvaluator(WindowFrameDef wFrmDef) {
return null;
}
}
http://paddy-w.iteye.com/blog/2081409