org.apache.hadoop.hive.ql.udf.generic.GenericUDAFVariance Maven / Gradle / Ivy
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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.ArrayList;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
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.util.JavaDataModel;
import org.apache.hadoop.hive.serde2.io.DoubleWritable;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorFactory;
import org.apache.hadoop.hive.serde2.objectinspector.PrimitiveObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.StructField;
import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.primitive.DoubleObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.primitive.LongObjectInspector;
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.PrimitiveTypeInfo;
import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.util.StringUtils;
/**
* Compute the variance. This class is extended by: GenericUDAFVarianceSample
* GenericUDAFStd GenericUDAFStdSample
*
*/
@Description(name = "variance,var_pop",
value = "_FUNC_(x) - Returns the variance of a set of numbers")
public class GenericUDAFVariance extends AbstractGenericUDAFResolver {
static final Log LOG = LogFactory.getLog(GenericUDAFVariance.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:
case FLOAT:
case DOUBLE:
case STRING:
case TIMESTAMP:
case DECIMAL:
return new GenericUDAFVarianceEvaluator();
case BOOLEAN:
case DATE:
default:
throw new UDFArgumentTypeException(0,
"Only numeric or string type arguments are accepted but "
+ parameters[0].getTypeName() + " is passed.");
}
}
/**
* Evaluate the variance using the algorithm described by Chan, Golub, and LeVeque in
* "Algorithms for computing the sample variance: analysis and recommendations"
* The American Statistician, 37 (1983) pp. 242--247.
*
* variance = variance1 + variance2 + n/(m*(m+n)) * pow(((m/n)*t1 - t2),2)
*
* where: - variance is sum[x-avg^2] (this is actually n times the variance)
* and is updated at every step. - n is the count of elements in chunk1 - m is
* the count of elements in chunk2 - t1 = sum of elements in chunk1, t2 =
* sum of elements in chunk2.
*
* This algorithm was proven to be numerically stable by J.L. Barlow in
* "Error analysis of a pairwise summation algorithm to compute sample variance"
* Numer. Math, 58 (1991) pp. 583--590
*
*/
public static class GenericUDAFVarianceEvaluator extends GenericUDAFEvaluator {
// For PARTIAL1 and COMPLETE
private transient PrimitiveObjectInspector inputOI;
// For PARTIAL2 and FINAL
private transient StructObjectInspector soi;
private transient StructField countField;
private transient StructField sumField;
private transient StructField varianceField;
private LongObjectInspector countFieldOI;
private DoubleObjectInspector sumFieldOI;
// For PARTIAL1 and PARTIAL2
private Object[] partialResult;
// For FINAL and COMPLETE
private DoubleWritable result;
@Override
public ObjectInspector init(Mode m, ObjectInspector[] parameters) throws HiveException {
assert (parameters.length == 1);
super.init(m, parameters);
// init input
if (mode == Mode.PARTIAL1 || mode == Mode.COMPLETE) {
inputOI = (PrimitiveObjectInspector) parameters[0];
} else {
soi = (StructObjectInspector) parameters[0];
countField = soi.getStructFieldRef("count");
sumField = soi.getStructFieldRef("sum");
varianceField = soi.getStructFieldRef("variance");
countFieldOI = (LongObjectInspector) countField
.getFieldObjectInspector();
sumFieldOI = (DoubleObjectInspector) sumField.getFieldObjectInspector();
}
// init output
if (mode == Mode.PARTIAL1 || mode == Mode.PARTIAL2) {
// The output of a partial aggregation is a struct containing
// a long count and doubles sum and variance.
ArrayList foi = new ArrayList();
foi.add(PrimitiveObjectInspectorFactory.writableLongObjectInspector);
foi.add(PrimitiveObjectInspectorFactory.writableDoubleObjectInspector);
foi.add(PrimitiveObjectInspectorFactory.writableDoubleObjectInspector);
ArrayList fname = new ArrayList();
fname.add("count");
fname.add("sum");
fname.add("variance");
partialResult = new Object[3];
partialResult[0] = new LongWritable(0);
partialResult[1] = new DoubleWritable(0);
partialResult[2] = new DoubleWritable(0);
return ObjectInspectorFactory.getStandardStructObjectInspector(fname,
foi);
} else {
setResult(new DoubleWritable(0));
return PrimitiveObjectInspectorFactory.writableDoubleObjectInspector;
}
}
@AggregationType(estimable = true)
static class StdAgg extends AbstractAggregationBuffer {
long count; // number of elements
double sum; // sum of elements
double variance; // sum[x-avg^2] (this is actually n times the variance)
@Override
public int estimate() { return JavaDataModel.PRIMITIVES2 * 3; }
};
@Override
public AggregationBuffer getNewAggregationBuffer() throws HiveException {
StdAgg result = new StdAgg();
reset(result);
return result;
}
@Override
public void reset(AggregationBuffer agg) throws HiveException {
StdAgg myagg = (StdAgg) agg;
myagg.count = 0;
myagg.sum = 0;
myagg.variance = 0;
}
private boolean warned = false;
@Override
public void iterate(AggregationBuffer agg, Object[] parameters)
throws HiveException {
assert (parameters.length == 1);
Object p = parameters[0];
if (p != null) {
StdAgg myagg = (StdAgg) agg;
try {
double v = PrimitiveObjectInspectorUtils.getDouble(p, inputOI);
myagg.count++;
myagg.sum += v;
if(myagg.count > 1) {
double t = myagg.count*v - myagg.sum;
myagg.variance += (t*t) / ((double)myagg.count*(myagg.count-1));
}
} catch (NumberFormatException e) {
if (!warned) {
warned = true;
LOG.warn(getClass().getSimpleName() + " "
+ StringUtils.stringifyException(e));
LOG.warn(getClass().getSimpleName()
+ " ignoring similar exceptions.");
}
}
}
}
@Override
public Object terminatePartial(AggregationBuffer agg) throws HiveException {
StdAgg myagg = (StdAgg) agg;
((LongWritable) partialResult[0]).set(myagg.count);
((DoubleWritable) partialResult[1]).set(myagg.sum);
((DoubleWritable) partialResult[2]).set(myagg.variance);
return partialResult;
}
@Override
public void merge(AggregationBuffer agg, Object partial) throws HiveException {
if (partial != null) {
StdAgg myagg = (StdAgg) agg;
Object partialCount = soi.getStructFieldData(partial, countField);
Object partialSum = soi.getStructFieldData(partial, sumField);
Object partialVariance = soi.getStructFieldData(partial, varianceField);
long n = myagg.count;
long m = countFieldOI.get(partialCount);
if (n == 0) {
// Just copy the information since there is nothing so far
myagg.variance = sumFieldOI.get(partialVariance);
myagg.count = countFieldOI.get(partialCount);
myagg.sum = sumFieldOI.get(partialSum);
}
if (m != 0 && n != 0) {
// Merge the two partials
double a = myagg.sum;
double b = sumFieldOI.get(partialSum);
myagg.count += m;
myagg.sum += b;
double t = (m/(double)n)*a - b;
myagg.variance += sumFieldOI.get(partialVariance) + ((n/(double)m)/((double)n+m)) * t * t;
}
}
}
@Override
public Object terminate(AggregationBuffer agg) throws HiveException {
StdAgg myagg = (StdAgg) agg;
if (myagg.count == 0) { // SQL standard - return null for zero elements
return null;
} else {
if (myagg.count > 1) {
getResult().set(myagg.variance / (myagg.count));
} else { // for one element the variance is always 0
getResult().set(0);
}
return getResult();
}
}
public void setResult(DoubleWritable result) {
this.result = result;
}
public DoubleWritable getResult() {
return result;
}
}
}