org.apache.hadoop.hive.ql.udf.generic.GenericUDAFEvaluator Maven / Gradle / Ivy
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* distributed with this work for additional information
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* to you under the Apache License, Version 2.0 (the
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* 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,
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* 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 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, array> and so on (arbitrary levels of nesting).
*/
@UDFType(deterministic = true)
public abstract class GenericUDAFEvaluator implements Closeable {
public static @interface AggregationType {
boolean estimable() default false;
}
public static boolean isEstimable(AggregationBuffer buffer) {
if (buffer instanceof AbstractAggregationBuffer) {
Class 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,
/**
* PARTIAL2: from partial aggregation data to partial aggregation data:
* merge() and terminatePartial() will be called.
*/
PARTIAL2,
/**
* FINAL: from partial aggregation to full aggregation: merge() and
* terminate() will be called.
*/
FINAL,
/**
* COMPLETE: from original data directly to full aggregation: iterate() and
* terminate() will be called.
*/
COMPLETE
};
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
* The mode of aggregation.
* @param parameters
* The ObjectInspector for the parameters: In PARTIAL1 and COMPLETE
* mode, the parameters are original data; In PARTIAL2 and FINAL
* mode, the parameters are just partial aggregations (in that case,
* 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.
*
* 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;
}
}