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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 java.util.List;
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.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.PrimitiveObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.StandardListObjectInspector;
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.ListTypeInfo;
import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo;
import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo;
import org.apache.hadoop.io.Text;
/**
* Estimates the top-k contextual n-grams in arbitrary sequential data using a heuristic.
*/
@Description(name = "context_ngrams",
value = "_FUNC_(expr, array, k, pf) estimates the top-k most " +
"frequent n-grams that fit into the specified context. The second parameter specifies " +
"a string of words that specify the positions of the n-gram elements, with a null value " +
"standing in for a 'blank' that must be filled by an n-gram element.",
extended = "The primary expression must be an array of strings, or an array of arrays of " +
"strings, such as the return type of the sentences() UDF. The second parameter specifies " +
"the context -- for example, array(\"i\", \"love\", null) -- which would estimate the top " +
"'k' words that follow the phrase \"i love\" in the primary expression. The optional " +
"fourth parameter 'pf' controls the memory used by the heuristic. Larger values will " +
"yield better accuracy, but use more memory. Example usage:\n" +
" SELECT context_ngrams(sentences(lower(review)), array(\"i\", \"love\", null, null), 10)" +
" FROM movies\n" +
"would attempt to determine the 10 most common two-word phrases that follow \"i love\" " +
"in a database of free-form natural language movie reviews.")
public class GenericUDAFContextNGrams implements GenericUDAFResolver {
static final Log LOG = LogFactory.getLog(GenericUDAFContextNGrams.class.getName());
@Override
public GenericUDAFEvaluator getEvaluator(TypeInfo[] parameters) throws SemanticException {
if (parameters.length != 3 && parameters.length != 4) {
throw new UDFArgumentTypeException(parameters.length-1,
"Please specify either three or four arguments.");
}
// Validate the first parameter, which is the expression to compute over. This should be an
// array of strings type, or an array of arrays of strings.
PrimitiveTypeInfo pti;
if (parameters[0].getCategory() != ObjectInspector.Category.LIST) {
throw new UDFArgumentTypeException(0,
"Only list type arguments are accepted but "
+ parameters[0].getTypeName() + " was passed as parameter 1.");
}
switch (((ListTypeInfo) parameters[0]).getListElementTypeInfo().getCategory()) {
case PRIMITIVE:
// Parameter 1 was an array of primitives, so make sure the primitives are strings.
pti = (PrimitiveTypeInfo) ((ListTypeInfo) parameters[0]).getListElementTypeInfo();
break;
case LIST:
// Parameter 1 was an array of arrays, so make sure that the inner arrays contain
// primitive strings.
ListTypeInfo lti = (ListTypeInfo)
((ListTypeInfo) parameters[0]).getListElementTypeInfo();
pti = (PrimitiveTypeInfo) lti.getListElementTypeInfo();
break;
default:
throw new UDFArgumentTypeException(0,
"Only arrays of strings or arrays of arrays of strings are accepted but "
+ parameters[0].getTypeName() + " was passed as parameter 1.");
}
if(pti.getPrimitiveCategory() != PrimitiveObjectInspector.PrimitiveCategory.STRING) {
throw new UDFArgumentTypeException(0,
"Only array or array> is allowed, but "
+ parameters[0].getTypeName() + " was passed as parameter 1.");
}
// Validate the second parameter, which should be an array of strings
if(parameters[1].getCategory() != ObjectInspector.Category.LIST ||
((ListTypeInfo) parameters[1]).getListElementTypeInfo().getCategory() !=
ObjectInspector.Category.PRIMITIVE) {
throw new UDFArgumentTypeException(1, "Only arrays of strings are accepted but "
+ parameters[1].getTypeName() + " was passed as parameter 2.");
}
if(((PrimitiveTypeInfo) ((ListTypeInfo)parameters[1]).getListElementTypeInfo()).
getPrimitiveCategory() != PrimitiveObjectInspector.PrimitiveCategory.STRING) {
throw new UDFArgumentTypeException(1, "Only arrays of strings are accepted but "
+ parameters[1].getTypeName() + " was passed as parameter 2.");
}
// Validate the third parameter, which should be an integer to represent 'k'
if(parameters[2].getCategory() != ObjectInspector.Category.PRIMITIVE) {
throw new UDFArgumentTypeException(2, "Only integers are accepted but "
+ parameters[2].getTypeName() + " was passed as parameter 3.");
}
switch(((PrimitiveTypeInfo) parameters[2]).getPrimitiveCategory()) {
case BYTE:
case SHORT:
case INT:
case LONG:
case TIMESTAMP:
break;
default:
throw new UDFArgumentTypeException(2, "Only integers are accepted but "
+ parameters[2].getTypeName() + " was passed as parameter 3.");
}
// If the fourth parameter -- precision factor 'pf' -- has been specified, make sure it's
// an integer.
if(parameters.length == 4) {
if(parameters[3].getCategory() != ObjectInspector.Category.PRIMITIVE) {
throw new UDFArgumentTypeException(3, "Only integers are accepted but "
+ parameters[3].getTypeName() + " was passed as parameter 4.");
}
switch(((PrimitiveTypeInfo) parameters[3]).getPrimitiveCategory()) {
case BYTE:
case SHORT:
case INT:
case LONG:
case TIMESTAMP:
break;
default:
throw new UDFArgumentTypeException(3, "Only integers are accepted but "
+ parameters[3].getTypeName() + " was passed as parameter 4.");
}
}
return new GenericUDAFContextNGramEvaluator();
}
/**
* A constant-space heuristic to estimate the top-k contextual n-grams.
*/
public static class GenericUDAFContextNGramEvaluator extends GenericUDAFEvaluator {
// For PARTIAL1 and COMPLETE: ObjectInspectors for original data
private transient ListObjectInspector outerInputOI;
private transient StandardListObjectInspector innerInputOI;
private transient ListObjectInspector contextListOI;
private PrimitiveObjectInspector contextOI;
private PrimitiveObjectInspector inputOI;
private transient PrimitiveObjectInspector kOI;
private transient PrimitiveObjectInspector pOI;
// For PARTIAL2 and FINAL: ObjectInspectors for partial aggregations
private transient ListObjectInspector loi;
@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) {
outerInputOI = (ListObjectInspector) parameters[0];
if(outerInputOI.getListElementObjectInspector().getCategory() ==
ObjectInspector.Category.LIST) {
// We're dealing with input that is an array of arrays of strings
innerInputOI = (StandardListObjectInspector) outerInputOI.getListElementObjectInspector();
inputOI = (PrimitiveObjectInspector) innerInputOI.getListElementObjectInspector();
} else {
// We're dealing with input that is an array of strings
inputOI = (PrimitiveObjectInspector) outerInputOI.getListElementObjectInspector();
innerInputOI = null;
}
contextListOI = (ListObjectInspector) parameters[1];
contextOI = (PrimitiveObjectInspector) contextListOI.getListElementObjectInspector();
kOI = (PrimitiveObjectInspector) parameters[2];
if(parameters.length == 4) {
pOI = (PrimitiveObjectInspector) parameters[3];
} else {
pOI = null;
}
} else {
// Init the list object inspector for handling partial aggregations
loi = (ListObjectInspector) parameters[0];
}
// Init output object inspectors.
//
// The return type for a partial aggregation is still a list of strings.
//
// The return type for FINAL and COMPLETE is a full aggregation result, which is
// an array of structures containing the n-gram and its estimated frequency.
if (m == Mode.PARTIAL1 || m == Mode.PARTIAL2) {
return ObjectInspectorFactory.getStandardListObjectInspector(
PrimitiveObjectInspectorFactory.writableStringObjectInspector);
} else {
// Final return type that goes back to Hive: a list of structs with n-grams and their
// estimated frequencies.
ArrayList foi = new ArrayList();
foi.add(ObjectInspectorFactory.getStandardListObjectInspector(
PrimitiveObjectInspectorFactory.writableStringObjectInspector));
foi.add(PrimitiveObjectInspectorFactory.writableDoubleObjectInspector);
ArrayList fname = new ArrayList();
fname.add("ngram");
fname.add("estfrequency");
return ObjectInspectorFactory.getStandardListObjectInspector(
ObjectInspectorFactory.getStandardStructObjectInspector(fname, foi) );
}
}
@Override
public void merge(AggregationBuffer agg, Object obj) throws HiveException {
if(obj == null) {
return;
}
NGramAggBuf myagg = (NGramAggBuf) agg;
List partial = (List) loi.getList(obj);
// remove the context words from the end of the list
int contextSize = Integer.parseInt( partial.get(partial.size()-1).toString() );
partial.remove(partial.size()-1);
if(myagg.context.size() > 0) {
if(contextSize != myagg.context.size()) {
throw new HiveException(getClass().getSimpleName() + ": found a mismatch in the" +
" context string lengths. This is usually caused by passing a non-constant" +
" expression for the context.");
}
} else {
for(int i = partial.size()-contextSize; i < partial.size(); i++) {
String word = partial.get(i).toString();
if(word.equals("")) {
myagg.context.add( null );
} else {
myagg.context.add( word );
}
}
partial.subList(partial.size()-contextSize, partial.size()).clear();
myagg.nge.merge(partial);
}
}
@Override
public Object terminatePartial(AggregationBuffer agg) throws HiveException {
NGramAggBuf myagg = (NGramAggBuf) agg;
ArrayList result = myagg.nge.serialize();
// push the context on to the end of the serialized n-gram estimation
for(int i = 0; i < myagg.context.size(); i++) {
if(myagg.context.get(i) == null) {
result.add(new Text(""));
} else {
result.add(new Text(myagg.context.get(i)));
}
}
result.add(new Text(Integer.toString(myagg.context.size())));
return result;
}
// Finds all contextual n-grams in a sequence of words, and passes the n-grams to the
// n-gram estimator object
private void processNgrams(NGramAggBuf agg, ArrayList seq) throws HiveException {
// generate n-grams wherever the context matches
assert(agg.context.size() > 0);
ArrayList ng = new ArrayList();
for(int i = seq.size() - agg.context.size(); i >= 0; i--) {
// check if the context matches
boolean contextMatches = true;
ng.clear();
for(int j = 0; j < agg.context.size(); j++) {
String contextWord = agg.context.get(j);
if(contextWord == null) {
ng.add(seq.get(i+j));
} else {
if(!contextWord.equals(seq.get(i+j))) {
contextMatches = false;
break;
}
}
}
// add to n-gram estimation only if the context matches
if(contextMatches) {
agg.nge.add(ng);
ng = new ArrayList();
}
}
}
@Override
public void iterate(AggregationBuffer agg, Object[] parameters) throws HiveException {
assert (parameters.length == 3 || parameters.length == 4);
if(parameters[0] == null || parameters[1] == null || parameters[2] == null) {
return;
}
NGramAggBuf myagg = (NGramAggBuf) agg;
// Parse out the context and 'k' if we haven't already done so, and while we're at it,
// also parse out the precision factor 'pf' if the user has supplied one.
if(!myagg.nge.isInitialized()) {
int k = PrimitiveObjectInspectorUtils.getInt(parameters[2], kOI);
int pf = 0;
if(k < 1) {
throw new HiveException(getClass().getSimpleName() + " needs 'k' to be at least 1, "
+ "but you supplied " + k);
}
if(parameters.length == 4) {
pf = PrimitiveObjectInspectorUtils.getInt(parameters[3], pOI);
if(pf < 1) {
throw new HiveException(getClass().getSimpleName() + " needs 'pf' to be at least 1, "
+ "but you supplied " + pf);
}
} else {
pf = 1; // placeholder; minimum pf value is enforced in NGramEstimator
}
// Parse out the context and make sure it isn't empty
myagg.context.clear();
List context = (List) contextListOI.getList(parameters[1]);
int contextNulls = 0;
for(int i = 0; i < context.size(); i++) {
String word = PrimitiveObjectInspectorUtils.getString(context.get(i), contextOI);
if(word == null) {
contextNulls++;
}
myagg.context.add(word);
}
if(context.size() == 0) {
throw new HiveException(getClass().getSimpleName() + " needs a context array " +
"with at least one element.");
}
if(contextNulls == 0) {
throw new HiveException(getClass().getSimpleName() + " the context array needs to " +
"contain at least one 'null' value to indicate what should be counted.");
}
// Set parameters in the n-gram estimator object
myagg.nge.initialize(k, pf, contextNulls);
}
// get the input expression
List outer = (List) outerInputOI.getList(parameters[0]);
if(innerInputOI != null) {
// we're dealing with an array of arrays of strings
for(int i = 0; i < outer.size(); i++) {
List inner = (List) innerInputOI.getList(outer.get(i));
ArrayList words = new ArrayList();
for(int j = 0; j < inner.size(); j++) {
String word = PrimitiveObjectInspectorUtils.getString(inner.get(j), inputOI);
words.add(word);
}
// parse out n-grams, update frequency counts
processNgrams(myagg, words);
}
} else {
// we're dealing with an array of strings
ArrayList words = new ArrayList();
for(int i = 0; i < outer.size(); i++) {
String word = PrimitiveObjectInspectorUtils.getString(outer.get(i), inputOI);
words.add(word);
}
// parse out n-grams, update frequency counts
processNgrams(myagg, words);
}
}
@Override
public Object terminate(AggregationBuffer agg) throws HiveException {
NGramAggBuf myagg = (NGramAggBuf) agg;
return myagg.nge.getNGrams();
}
// Aggregation buffer methods.
static class NGramAggBuf extends AbstractAggregationBuffer {
ArrayList context;
NGramEstimator nge;
};
@Override
public AggregationBuffer getNewAggregationBuffer() throws HiveException {
NGramAggBuf result = new NGramAggBuf();
result.nge = new NGramEstimator();
result.context = new ArrayList();
reset(result);
return result;
}
@Override
public void reset(AggregationBuffer agg) throws HiveException {
NGramAggBuf result = (NGramAggBuf) agg;
result.context.clear();
result.nge.reset();
}
}
}
