-
Notifications
You must be signed in to change notification settings - Fork 4.7k
/
GenericUDAFPercentileApprox.java
414 lines (374 loc) · 17.5 KB
/
GenericUDAFPercentileApprox.java
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
/*
* 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 java.util.List;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
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.ConstantObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.ListObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorFactory;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils;
import org.apache.hadoop.hive.serde2.objectinspector.PrimitiveObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.PrimitiveObjectInspector.PrimitiveCategory;
import org.apache.hadoop.hive.serde2.objectinspector.StandardListObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.primitive.DoubleObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorFactory;
import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorUtils;
/**
* Computes an approximate percentile (quantile) from an approximate histogram, for very
* large numbers of rows where the regular percentile() UDAF might run out of memory.
*
* The input is a single double value or an array of double values representing the quantiles
* requested. The output, corresponding to the input, is either an single double value or an
* array of doubles that are the quantile values.
*/
@Description(name = "percentile_approx",
value = "_FUNC_(expr, pc, [nb]) - For very large data, computes an approximate percentile " +
"value from a histogram, using the optional argument [nb] as the number of histogram" +
" bins to use. A higher value of nb results in a more accurate approximation, at " +
"the cost of higher memory usage.",
extended = "'expr' can be any numeric column, including doubles and floats, and 'pc' is " +
"either a single double/float with a requested percentile, or an array of double/" +
"float with multiple percentiles. If 'nb' is not specified, the default " +
"approximation is done with 10,000 histogram bins, which means that if there are " +
"10,000 or fewer unique values in 'expr', you can expect an exact result. The " +
"percentile() function always computes an exact percentile and can run out of " +
"memory if there are too many unique values in a column, which necessitates " +
"this function.\n" +
"Example (three percentiles requested using a finer histogram approximation):\n" +
"> SELECT percentile_approx(val, array(0.5, 0.95, 0.98), 100000) FROM somedata;\n" +
"[0.05,1.64,2.26]\n")
public class GenericUDAFPercentileApprox extends AbstractGenericUDAFResolver {
static final Logger LOG = LoggerFactory.getLogger(GenericUDAFPercentileApprox.class.getName());
private static void verifyFractionType(ObjectInspector oi) throws UDFArgumentTypeException {
PrimitiveCategory pc = ((PrimitiveObjectInspector)oi).getPrimitiveCategory();
switch(pc) {
case FLOAT:
case DOUBLE:
case DECIMAL:
break;
default:
throw new UDFArgumentTypeException(1, "Only a floating point or decimal, or "
+ "floating point or decimal array argument is accepted as parameter 2, but "
+ pc + " was passed instead.");
}
}
@Override
public GenericUDAFEvaluator getEvaluator(GenericUDAFParameterInfo info) throws SemanticException {
ObjectInspector[] parameters = info.getParameterObjectInspectors();
if (parameters.length != 2 && parameters.length != 3) {
throw new UDFArgumentTypeException(parameters.length - 1,
"Please specify either two or three arguments.");
}
// Validate the first parameter, which is the expression to compute over. This should be a
// numeric primitive type.
if (parameters[0].getCategory() != ObjectInspector.Category.PRIMITIVE) {
throw new UDFArgumentTypeException(0,
"Only primitive type arguments are accepted but "
+ parameters[0].getTypeName() + " was passed as parameter 1.");
}
switch (((PrimitiveObjectInspector) parameters[0]).getPrimitiveCategory()) {
case BYTE:
case SHORT:
case INT:
case LONG:
case FLOAT:
case DOUBLE:
case TIMESTAMP:
case DECIMAL:
break;
default:
throw new UDFArgumentTypeException(0,
"Only numeric type arguments are accepted but "
+ parameters[0].getTypeName() + " was passed as parameter 1.");
}
// Validate the second parameter, which is either a solitary double or an array of doubles.
boolean wantManyQuantiles = false;
switch(parameters[1].getCategory()) {
case PRIMITIVE:
// Only a single double was passed as parameter 2, a single quantile is being requested
verifyFractionType(parameters[1]);
break;
case LIST:
// An array was passed as parameter 2, make sure it's an array of primitives
if(((ListObjectInspector) parameters[1]).getListElementObjectInspector().getCategory() !=
ObjectInspector.Category.PRIMITIVE) {
throw new UDFArgumentTypeException(1,
"A floating point or decimal array argument may be passed as parameter 2, but "
+ parameters[1].getTypeName() + " was passed instead.");
}
// Now make sure it's an array of doubles or floats. We don't allow integer types here
// because percentile (really, quantile) values should generally be strictly between 0 and 1.
verifyFractionType(((ListObjectInspector) parameters[1]).getListElementObjectInspector());
wantManyQuantiles = true;
break;
default:
throw new UDFArgumentTypeException(1,
"Only a floating point or decimal, or floating point or decimal array argument is accepted"
+ " as parameter 2, but " + parameters[1].getTypeName() + " was passed instead.");
}
// Also make sure it is a constant.
if (!ObjectInspectorUtils.isConstantObjectInspector(parameters[1])) {
throw new UDFArgumentTypeException(1,
"The second argument must be a constant, but " + parameters[1].getTypeName() +
" was passed instead.");
}
// If a third parameter has been specified, it should be an integer that specifies the number
// of histogram bins to use in the percentile approximation.
if(parameters.length == 3) {
if(parameters[2].getCategory() != ObjectInspector.Category.PRIMITIVE) {
throw new UDFArgumentTypeException(2, "Only a primitive argument is accepted as "
+ "parameter 3, but " + parameters[2].getTypeName() + " was passed instead.");
}
switch(((PrimitiveObjectInspector) parameters[2]).getPrimitiveCategory()) {
case BYTE:
case SHORT:
case INT:
case LONG:
case TIMESTAMP:
break;
default:
throw new UDFArgumentTypeException(2, "Only an integer argument is accepted as "
+ "parameter 3, but " + parameters[2].getTypeName() + " was passed instead.");
}
// Also make sure it is a constant.
if (!ObjectInspectorUtils.isConstantObjectInspector(parameters[2])) {
throw new UDFArgumentTypeException(2,
"The third argument must be a constant, but " + parameters[2].getTypeName() +
" was passed instead.");
}
}
// Return an evaluator depending on the return type
if(wantManyQuantiles) {
return new GenericUDAFMultiplePercentileApproxEvaluator();
} else {
return new GenericUDAFSinglePercentileApproxEvaluator();
}
}
public static class GenericUDAFSinglePercentileApproxEvaluator extends
GenericUDAFPercentileApproxEvaluator {
@Override
public ObjectInspector init(Mode m, ObjectInspector[] parameters) throws HiveException {
super.init(m, parameters);
// init input object inspectors
if (m == Mode.PARTIAL1 || m == Mode.COMPLETE) {
inputOI = (PrimitiveObjectInspector) parameters[0];
quantiles = getQuantileArray((ConstantObjectInspector)parameters[1]);
if(parameters.length > 2) {
nbins = PrimitiveObjectInspectorUtils.getInt(
((ConstantObjectInspector) parameters[2]).getWritableConstantValue(),
(PrimitiveObjectInspector)parameters[2]);
}
} else {
loi = (StandardListObjectInspector) parameters[0];
}
// Init output object inspectors.
//
// The return type for a partial aggregation is still a list of doubles, as in
// GenericUDAFHistogramNumeric, but we add on the percentile values requested to the
// end, and handle serializing/deserializing before we pass things on to the parent
// method.
// The return type for FINAL and COMPLETE is a full aggregation result, which is a
// single double value
if (m == Mode.PARTIAL1 || m == Mode.PARTIAL2) {
return ObjectInspectorFactory.getStandardListObjectInspector(
PrimitiveObjectInspectorFactory.writableDoubleObjectInspector);
} else {
return PrimitiveObjectInspectorFactory.writableDoubleObjectInspector;
}
}
@Override
public Object terminate(AggregationBuffer agg) throws HiveException {
PercentileAggBuf myagg = (PercentileAggBuf) agg;
if (myagg.histogram.getUsedBins() < 1) { // SQL standard - return null for zero elements
return null;
} else {
assert(myagg.quantiles != null);
return new DoubleWritable(myagg.histogram.quantile(myagg.quantiles[0]));
}
}
}
public static class GenericUDAFMultiplePercentileApproxEvaluator extends
GenericUDAFPercentileApproxEvaluator {
@Override
public ObjectInspector init(Mode m, ObjectInspector[] parameters) throws HiveException {
super.init(m, parameters);
// init input object inspectors
if (m == Mode.PARTIAL1 || m == Mode.COMPLETE) {
inputOI = (PrimitiveObjectInspector) parameters[0];
quantiles = getQuantileArray((ConstantObjectInspector)parameters[1]);
if(parameters.length > 2) {
nbins = PrimitiveObjectInspectorUtils.getInt(
((ConstantObjectInspector) parameters[2]).getWritableConstantValue(),
(PrimitiveObjectInspector)parameters[2]);
}
} else {
loi = (StandardListObjectInspector) parameters[0];
}
// Init output object inspectors.
//
// The return type for a partial aggregation is still a list of doubles, as in
// GenericUDAFHistogramNumeric, but we add on the percentile values requested to the
// end, and handle serializing/deserializing before we pass things on to the parent
// method.
// The return type for FINAL and COMPLETE is a full aggregation result, which is also
// a list of doubles
return ObjectInspectorFactory.getStandardListObjectInspector(
PrimitiveObjectInspectorFactory.writableDoubleObjectInspector);
}
@Override
public Object terminate(AggregationBuffer agg) throws HiveException {
PercentileAggBuf myagg = (PercentileAggBuf) agg;
if (myagg.histogram.getUsedBins() < 1) { // SQL standard - return null for zero elements
return null;
} else {
ArrayList<DoubleWritable> result = new ArrayList<DoubleWritable>();
assert(myagg.quantiles != null);
for(int i = 0; i < myagg.quantiles.length; i++) {
result.add(new DoubleWritable(myagg.histogram.quantile(myagg.quantiles[i])));
}
return result;
}
}
}
/**
* Construct a histogram using the algorithm described by Ben-Haim and Tom-Tov, and then
* use it to compute an approximate percentile value.
*/
public abstract static class GenericUDAFPercentileApproxEvaluator extends GenericUDAFEvaluator {
// For PARTIAL1 and COMPLETE: ObjectInspectors for original data
protected PrimitiveObjectInspector inputOI;
protected double quantiles[];
protected Integer nbins = 10000;
// For PARTIAL2 and FINAL: ObjectInspectors for partial aggregations (list of doubles)
protected transient StandardListObjectInspector loi;
@Override
public void merge(AggregationBuffer agg, Object partial) throws HiveException {
if(partial == null) {
return;
}
PercentileAggBuf myagg = (PercentileAggBuf) agg;
List partialHistogram = (List) loi.getList(partial);
DoubleObjectInspector doi = (DoubleObjectInspector)loi.getListElementObjectInspector();
// remove requested quantiles from the head of the list
int nquantiles = (int) doi.get(partialHistogram.get(0));
if(nquantiles > 0) {
myagg.quantiles = new double[nquantiles];
for(int i = 1; i <= nquantiles; i++) {
myagg.quantiles[i-1] = doi.get(partialHistogram.get(i));
}
partialHistogram.subList(0, nquantiles+1).clear();
} else {
partialHistogram.subList(0, 1).clear();
}
// merge histograms
myagg.histogram.merge(partialHistogram, doi);
}
@Override
public Object terminatePartial(AggregationBuffer agg) throws HiveException {
PercentileAggBuf myagg = (PercentileAggBuf) agg;
ArrayList<DoubleWritable> result = new ArrayList<DoubleWritable>();
if(myagg.quantiles != null) {
result.add(new DoubleWritable(myagg.quantiles.length));
for(int i = 0; i < myagg.quantiles.length; i++) {
result.add(new DoubleWritable(myagg.quantiles[i]));
}
} else {
result.add(new DoubleWritable(0));
}
result.addAll(myagg.histogram.serialize());
return result;
}
@Override
public void iterate(AggregationBuffer agg, Object[] parameters) throws HiveException {
assert (parameters.length == 2 || parameters.length == 3);
if(parameters[0] == null || parameters[1] == null) {
return;
}
PercentileAggBuf myagg = (PercentileAggBuf) agg;
// Get and process the current datum
double v = PrimitiveObjectInspectorUtils.getDouble(parameters[0], inputOI);
myagg.histogram.add(v);
}
// Aggregation buffer methods. We wrap GenericUDAFHistogramNumeric's aggregation buffer
// inside our own, so that we can also store requested quantile values between calls
@AggregationType(estimable = true)
static class PercentileAggBuf extends AbstractAggregationBuffer {
NumericHistogram histogram; // histogram used for quantile approximation
double[] quantiles; // the quantiles requested
@Override
public int estimate() {
JavaDataModel model = JavaDataModel.get();
return histogram.lengthFor(model) +
model.array() + JavaDataModel.PRIMITIVES2 * quantiles.length;
}
};
@Override
public AggregationBuffer getNewAggregationBuffer() throws HiveException {
PercentileAggBuf result = new PercentileAggBuf();
result.histogram = new NumericHistogram();
reset(result);
return result;
}
protected double[] getQuantileArray(ConstantObjectInspector quantileOI)
throws HiveException {
double[] result = null;
Object quantileObj = quantileOI.getWritableConstantValue();
if (quantileOI instanceof ListObjectInspector) {
ObjectInspector elemOI =
((ListObjectInspector)quantileOI).getListElementObjectInspector();
result = new double[((List<?>)quantileObj).size()];
assert(result.length >= 1);
for (int ii = 0; ii < result.length; ++ii) {
result[ii] = PrimitiveObjectInspectorUtils.getDouble(
((List<?>)quantileObj).get(ii),
(PrimitiveObjectInspector)elemOI);
}
} else {
result = new double[1];
result[0] = PrimitiveObjectInspectorUtils.getDouble(
quantileObj,
(PrimitiveObjectInspector)quantileOI);
}
for(int ii = 0; ii < result.length; ++ii) {
if (result[ii] <= 0 || result[ii] >= 1) {
throw new HiveException(
getClass().getSimpleName() + " requires percentile values to " +
"lie strictly between 0 and 1, but you supplied " + result[ii]);
}
}
return result;
}
@Override
public void reset(AggregationBuffer agg) throws HiveException {
PercentileAggBuf result = (PercentileAggBuf) agg;
result.histogram.reset();
result.quantiles = null;
result.histogram.allocate(nbins);
result.quantiles = quantiles;
}
}
}