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/*
* Licensed to Elasticsearch under one or more contributor
* license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright
* ownership. Elasticsearch 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.elasticsearch.action.search;
import com.carrotsearch.hppc.IntArrayList;
import com.carrotsearch.hppc.ObjectObjectHashMap;
import org.apache.lucene.index.Term;
import org.apache.lucene.search.CollectionStatistics;
import org.apache.lucene.search.FieldDoc;
import org.apache.lucene.search.ScoreDoc;
import org.apache.lucene.search.Sort;
import org.apache.lucene.search.SortField;
import org.apache.lucene.search.TermStatistics;
import org.apache.lucene.search.TopDocs;
import org.apache.lucene.search.TopFieldDocs;
import org.elasticsearch.cluster.service.ClusterService;
import org.elasticsearch.common.collect.HppcMaps;
import org.elasticsearch.common.component.AbstractComponent;
import org.elasticsearch.common.inject.Inject;
import org.elasticsearch.common.lucene.Lucene;
import org.elasticsearch.common.settings.Settings;
import org.elasticsearch.common.util.BigArrays;
import org.elasticsearch.common.util.concurrent.AtomicArray;
import org.elasticsearch.script.ScriptService;
import org.elasticsearch.search.aggregations.InternalAggregation;
import org.elasticsearch.search.aggregations.InternalAggregation.ReduceContext;
import org.elasticsearch.search.aggregations.InternalAggregations;
import org.elasticsearch.search.aggregations.pipeline.SiblingPipelineAggregator;
import org.elasticsearch.search.dfs.AggregatedDfs;
import org.elasticsearch.search.dfs.DfsSearchResult;
import org.elasticsearch.search.fetch.FetchSearchResult;
import org.elasticsearch.search.fetch.FetchSearchResultProvider;
import org.elasticsearch.search.internal.InternalSearchHit;
import org.elasticsearch.search.internal.InternalSearchHits;
import org.elasticsearch.search.internal.InternalSearchResponse;
import org.elasticsearch.search.profile.ProfileShardResult;
import org.elasticsearch.search.profile.SearchProfileShardResults;
import org.elasticsearch.search.query.QuerySearchResult;
import org.elasticsearch.search.query.QuerySearchResultProvider;
import org.elasticsearch.search.suggest.Suggest;
import org.elasticsearch.search.suggest.Suggest.Suggestion;
import org.elasticsearch.search.suggest.Suggest.Suggestion.Entry;
import org.elasticsearch.search.suggest.completion.CompletionSuggestion;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.Comparator;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;
import java.util.stream.StreamSupport;
/**
*
*/
public class SearchPhaseController extends AbstractComponent {
public static final Comparator> QUERY_RESULT_ORDERING = (o1, o2) -> {
int i = o1.value.shardTarget().index().compareTo(o2.value.shardTarget().index());
if (i == 0) {
i = o1.value.shardTarget().shardId().id() - o2.value.shardTarget().shardId().id();
}
return i;
};
public static final ScoreDoc[] EMPTY_DOCS = new ScoreDoc[0];
private final BigArrays bigArrays;
private final ScriptService scriptService;
private final ClusterService clusterService;
SearchPhaseController(Settings settings, BigArrays bigArrays, ScriptService scriptService, ClusterService clusterService) {
super(settings);
this.bigArrays = bigArrays;
this.scriptService = scriptService;
this.clusterService = clusterService;
}
public AggregatedDfs aggregateDfs(AtomicArray results) {
ObjectObjectHashMap termStatistics = HppcMaps.newNoNullKeysMap();
ObjectObjectHashMap fieldStatistics = HppcMaps.newNoNullKeysMap();
long aggMaxDoc = 0;
for (AtomicArray.Entry lEntry : results.asList()) {
final Term[] terms = lEntry.value.terms();
final TermStatistics[] stats = lEntry.value.termStatistics();
assert terms.length == stats.length;
for (int i = 0; i < terms.length; i++) {
assert terms[i] != null;
TermStatistics existing = termStatistics.get(terms[i]);
if (existing != null) {
assert terms[i].bytes().equals(existing.term());
// totalTermFrequency is an optional statistic we need to check if either one or both
// are set to -1 which means not present and then set it globally to -1
termStatistics.put(terms[i], new TermStatistics(existing.term(),
existing.docFreq() + stats[i].docFreq(),
optionalSum(existing.totalTermFreq(), stats[i].totalTermFreq())));
} else {
termStatistics.put(terms[i], stats[i]);
}
}
assert !lEntry.value.fieldStatistics().containsKey(null);
final Object[] keys = lEntry.value.fieldStatistics().keys;
final Object[] values = lEntry.value.fieldStatistics().values;
for (int i = 0; i < keys.length; i++) {
if (keys[i] != null) {
String key = (String) keys[i];
CollectionStatistics value = (CollectionStatistics) values[i];
assert key != null;
CollectionStatistics existing = fieldStatistics.get(key);
if (existing != null) {
CollectionStatistics merged = new CollectionStatistics(
key, existing.maxDoc() + value.maxDoc(),
optionalSum(existing.docCount(), value.docCount()),
optionalSum(existing.sumTotalTermFreq(), value.sumTotalTermFreq()),
optionalSum(existing.sumDocFreq(), value.sumDocFreq())
);
fieldStatistics.put(key, merged);
} else {
fieldStatistics.put(key, value);
}
}
}
aggMaxDoc += lEntry.value.maxDoc();
}
return new AggregatedDfs(termStatistics, fieldStatistics, aggMaxDoc);
}
private static long optionalSum(long left, long right) {
return Math.min(left, right) == -1 ? -1 : left + right;
}
/**
* Returns a score doc array of top N search docs across all shards, followed by top suggest docs for each
* named completion suggestion across all shards. If more than one named completion suggestion is specified in the
* request, the suggest docs for a named suggestion are ordered by the suggestion name.
*
* @param ignoreFrom Whether to ignore the from and sort all hits in each shard result.
* Enabled only for scroll search, because that only retrieves hits of length 'size' in the query phase.
* @param resultsArr Shard result holder
*/
public ScoreDoc[] sortDocs(boolean ignoreFrom, AtomicArray resultsArr) throws IOException {
List> results = resultsArr.asList();
if (results.isEmpty()) {
return EMPTY_DOCS;
}
boolean canOptimize = false;
QuerySearchResult result = null;
int shardIndex = -1;
if (results.size() == 1) {
canOptimize = true;
result = results.get(0).value.queryResult();
shardIndex = results.get(0).index;
} else {
// lets see if we only got hits from a single shard, if so, we can optimize...
for (AtomicArray.Entry entry : results) {
if (entry.value.queryResult().hasHits()) {
if (result != null) { // we already have one, can't really optimize
canOptimize = false;
break;
}
canOptimize = true;
result = entry.value.queryResult();
shardIndex = entry.index;
}
}
}
if (canOptimize) {
int offset = result.from();
if (ignoreFrom) {
offset = 0;
}
ScoreDoc[] scoreDocs = result.topDocs().scoreDocs;
ScoreDoc[] docs;
int numSuggestDocs = 0;
final Suggest suggest = result.queryResult().suggest();
final List completionSuggestions;
if (suggest != null) {
completionSuggestions = suggest.filter(CompletionSuggestion.class);
for (CompletionSuggestion suggestion : completionSuggestions) {
numSuggestDocs += suggestion.getOptions().size();
}
} else {
completionSuggestions = Collections.emptyList();
}
int docsOffset = 0;
if (scoreDocs.length == 0 || scoreDocs.length < offset) {
docs = new ScoreDoc[numSuggestDocs];
} else {
int resultDocsSize = result.size();
if ((scoreDocs.length - offset) < resultDocsSize) {
resultDocsSize = scoreDocs.length - offset;
}
docs = new ScoreDoc[resultDocsSize + numSuggestDocs];
for (int i = 0; i < resultDocsSize; i++) {
ScoreDoc scoreDoc = scoreDocs[offset + i];
scoreDoc.shardIndex = shardIndex;
docs[i] = scoreDoc;
docsOffset++;
}
}
for (CompletionSuggestion suggestion: completionSuggestions) {
for (CompletionSuggestion.Entry.Option option : suggestion.getOptions()) {
ScoreDoc doc = option.getDoc();
doc.shardIndex = shardIndex;
docs[docsOffset++] = doc;
}
}
return docs;
}
@SuppressWarnings("unchecked")
AtomicArray.Entry[] sortedResults = results.toArray(new AtomicArray.Entry[results.size()]);
Arrays.sort(sortedResults, QUERY_RESULT_ORDERING);
QuerySearchResultProvider firstResult = sortedResults[0].value;
int topN = topN(results);
int from = firstResult.queryResult().from();
if (ignoreFrom) {
from = 0;
}
final TopDocs mergedTopDocs;
if (firstResult.queryResult().topDocs() instanceof TopFieldDocs) {
TopFieldDocs firstTopDocs = (TopFieldDocs) firstResult.queryResult().topDocs();
final Sort sort = new Sort(firstTopDocs.fields);
final TopFieldDocs[] shardTopDocs = new TopFieldDocs[resultsArr.length()];
for (AtomicArray.Entry sortedResult : sortedResults) {
TopDocs topDocs = sortedResult.value.queryResult().topDocs();
// the 'index' field is the position in the resultsArr atomic array
shardTopDocs[sortedResult.index] = (TopFieldDocs) topDocs;
}
// TopDocs#merge can't deal with null shard TopDocs
for (int i = 0; i < shardTopDocs.length; ++i) {
if (shardTopDocs[i] == null) {
shardTopDocs[i] = new TopFieldDocs(0, new FieldDoc[0], sort.getSort(), Float.NaN);
}
}
mergedTopDocs = TopDocs.merge(sort, from, topN, shardTopDocs);
} else {
final TopDocs[] shardTopDocs = new TopDocs[resultsArr.length()];
for (AtomicArray.Entry sortedResult : sortedResults) {
TopDocs topDocs = sortedResult.value.queryResult().topDocs();
// the 'index' field is the position in the resultsArr atomic array
shardTopDocs[sortedResult.index] = topDocs;
}
// TopDocs#merge can't deal with null shard TopDocs
for (int i = 0; i < shardTopDocs.length; ++i) {
if (shardTopDocs[i] == null) {
shardTopDocs[i] = Lucene.EMPTY_TOP_DOCS;
}
}
mergedTopDocs = TopDocs.merge(from, topN, shardTopDocs);
}
ScoreDoc[] scoreDocs = mergedTopDocs.scoreDocs;
final Map>> groupedCompletionSuggestions = new HashMap<>();
// group suggestions and assign shard index
for (AtomicArray.Entry sortedResult : sortedResults) {
Suggest shardSuggest = sortedResult.value.queryResult().suggest();
if (shardSuggest != null) {
for (CompletionSuggestion suggestion : shardSuggest.filter(CompletionSuggestion.class)) {
suggestion.setShardIndex(sortedResult.index);
List> suggestions =
groupedCompletionSuggestions.computeIfAbsent(suggestion.getName(), s -> new ArrayList<>());
suggestions.add(suggestion);
}
}
}
if (groupedCompletionSuggestions.isEmpty() == false) {
int numSuggestDocs = 0;
List>> completionSuggestions =
new ArrayList<>(groupedCompletionSuggestions.size());
for (List> groupedSuggestions : groupedCompletionSuggestions.values()) {
final CompletionSuggestion completionSuggestion = CompletionSuggestion.reduceTo(groupedSuggestions);
assert completionSuggestion != null;
numSuggestDocs += completionSuggestion.getOptions().size();
completionSuggestions.add(completionSuggestion);
}
scoreDocs = new ScoreDoc[mergedTopDocs.scoreDocs.length + numSuggestDocs];
System.arraycopy(mergedTopDocs.scoreDocs, 0, scoreDocs, 0, mergedTopDocs.scoreDocs.length);
int offset = mergedTopDocs.scoreDocs.length;
Suggest suggestions = new Suggest(completionSuggestions);
for (CompletionSuggestion completionSuggestion : suggestions.filter(CompletionSuggestion.class)) {
for (CompletionSuggestion.Entry.Option option : completionSuggestion.getOptions()) {
scoreDocs[offset++] = option.getDoc();
}
}
}
return scoreDocs;
}
public ScoreDoc[] getLastEmittedDocPerShard(List> queryResults,
ScoreDoc[] sortedScoreDocs, int numShards) {
ScoreDoc[] lastEmittedDocPerShard = new ScoreDoc[numShards];
if (queryResults.isEmpty() == false) {
long fetchHits = 0;
for (AtomicArray.Entry queryResult : queryResults) {
fetchHits += queryResult.value.queryResult().topDocs().scoreDocs.length;
}
// from is always zero as when we use scroll, we ignore from
long size = Math.min(fetchHits, topN(queryResults));
for (int sortedDocsIndex = 0; sortedDocsIndex < size; sortedDocsIndex++) {
ScoreDoc scoreDoc = sortedScoreDocs[sortedDocsIndex];
lastEmittedDocPerShard[scoreDoc.shardIndex] = scoreDoc;
}
}
return lastEmittedDocPerShard;
}
/**
* Builds an array, with potential null elements, with docs to load.
*/
public void fillDocIdsToLoad(AtomicArray docIdsToLoad, ScoreDoc[] shardDocs) {
for (ScoreDoc shardDoc : shardDocs) {
IntArrayList shardDocIdsToLoad = docIdsToLoad.get(shardDoc.shardIndex);
if (shardDocIdsToLoad == null) {
shardDocIdsToLoad = new IntArrayList(); // can't be shared!, uses unsafe on it later on
docIdsToLoad.set(shardDoc.shardIndex, shardDocIdsToLoad);
}
shardDocIdsToLoad.add(shardDoc.doc);
}
}
/**
* Enriches search hits and completion suggestion hits from sortedDocs using fetchResultsArr,
* merges suggestions, aggregations and profile results
*
* Expects sortedDocs to have top search docs across all shards, optionally followed by top suggest docs for each named
* completion suggestion ordered by suggestion name
*/
public InternalSearchResponse merge(boolean ignoreFrom, ScoreDoc[] sortedDocs,
AtomicArray queryResultsArr,
AtomicArray fetchResultsArr) {
List> queryResults = queryResultsArr.asList();
List> fetchResults = fetchResultsArr.asList();
if (queryResults.isEmpty()) {
return InternalSearchResponse.empty();
}
QuerySearchResult firstResult = queryResults.get(0).value.queryResult();
boolean sorted = false;
int sortScoreIndex = -1;
if (firstResult.topDocs() instanceof TopFieldDocs) {
sorted = true;
TopFieldDocs fieldDocs = (TopFieldDocs) firstResult.queryResult().topDocs();
for (int i = 0; i < fieldDocs.fields.length; i++) {
if (fieldDocs.fields[i].getType() == SortField.Type.SCORE) {
sortScoreIndex = i;
}
}
}
// count the total (we use the query result provider here, since we might not get any hits (we scrolled past them))
long totalHits = 0;
long fetchHits = 0;
float maxScore = Float.NEGATIVE_INFINITY;
boolean timedOut = false;
Boolean terminatedEarly = null;
for (AtomicArray.Entry entry : queryResults) {
QuerySearchResult result = entry.value.queryResult();
if (result.searchTimedOut()) {
timedOut = true;
}
if (result.terminatedEarly() != null) {
if (terminatedEarly == null) {
terminatedEarly = result.terminatedEarly();
} else if (result.terminatedEarly()) {
terminatedEarly = true;
}
}
totalHits += result.topDocs().totalHits;
fetchHits += result.topDocs().scoreDocs.length;
if (!Float.isNaN(result.topDocs().getMaxScore())) {
maxScore = Math.max(maxScore, result.topDocs().getMaxScore());
}
}
if (Float.isInfinite(maxScore)) {
maxScore = Float.NaN;
}
// clean the fetch counter
for (AtomicArray.Entry entry : fetchResults) {
entry.value.fetchResult().initCounter();
}
int from = ignoreFrom ? 0 : firstResult.queryResult().from();
int numSearchHits = (int) Math.min(fetchHits - from, topN(queryResults));
// merge hits
List hits = new ArrayList<>();
if (!fetchResults.isEmpty()) {
for (int i = 0; i < numSearchHits; i++) {
ScoreDoc shardDoc = sortedDocs[i];
FetchSearchResultProvider fetchResultProvider = fetchResultsArr.get(shardDoc.shardIndex);
if (fetchResultProvider == null) {
continue;
}
FetchSearchResult fetchResult = fetchResultProvider.fetchResult();
int index = fetchResult.counterGetAndIncrement();
if (index < fetchResult.hits().internalHits().length) {
InternalSearchHit searchHit = fetchResult.hits().internalHits()[index];
searchHit.score(shardDoc.score);
searchHit.shard(fetchResult.shardTarget());
if (sorted) {
FieldDoc fieldDoc = (FieldDoc) shardDoc;
searchHit.sortValues(fieldDoc.fields, firstResult.sortValueFormats());
if (sortScoreIndex != -1) {
searchHit.score(((Number) fieldDoc.fields[sortScoreIndex]).floatValue());
}
}
hits.add(searchHit);
}
}
}
// merge suggest results
Suggest suggest = null;
if (firstResult.suggest() != null) {
final Map> groupedSuggestions = new HashMap<>();
for (AtomicArray.Entry queryResult : queryResults) {
Suggest shardSuggest = queryResult.value.queryResult().suggest();
if (shardSuggest != null) {
for (Suggestion> suggestion : shardSuggest) {
List suggestionList = groupedSuggestions.computeIfAbsent(suggestion.getName(), s -> new ArrayList<>());
suggestionList.add(suggestion);
}
}
}
if (groupedSuggestions.isEmpty() == false) {
suggest = new Suggest(Suggest.reduce(groupedSuggestions));
if (!fetchResults.isEmpty()) {
int currentOffset = numSearchHits;
for (CompletionSuggestion suggestion : suggest.filter(CompletionSuggestion.class)) {
final List suggestionOptions = suggestion.getOptions();
for (int scoreDocIndex = currentOffset; scoreDocIndex < currentOffset + suggestionOptions.size(); scoreDocIndex++) {
ScoreDoc shardDoc = sortedDocs[scoreDocIndex];
FetchSearchResultProvider fetchSearchResultProvider = fetchResultsArr.get(shardDoc.shardIndex);
if (fetchSearchResultProvider == null) {
continue;
}
FetchSearchResult fetchResult = fetchSearchResultProvider.fetchResult();
int fetchResultIndex = fetchResult.counterGetAndIncrement();
if (fetchResultIndex < fetchResult.hits().internalHits().length) {
InternalSearchHit hit = fetchResult.hits().internalHits()[fetchResultIndex];
CompletionSuggestion.Entry.Option suggestOption =
suggestionOptions.get(scoreDocIndex - currentOffset);
hit.score(shardDoc.score);
hit.shard(fetchResult.shardTarget());
suggestOption.setHit(hit);
}
}
currentOffset += suggestionOptions.size();
}
assert currentOffset == sortedDocs.length : "expected no more score doc slices";
}
}
}
// merge Aggregation
InternalAggregations aggregations = null;
if (firstResult.aggregations() != null && firstResult.aggregations().asList() != null) {
List aggregationsList = new ArrayList<>(queryResults.size());
for (AtomicArray.Entry entry : queryResults) {
aggregationsList.add((InternalAggregations) entry.value.queryResult().aggregations());
}
ReduceContext reduceContext = new ReduceContext(bigArrays, scriptService, clusterService.state());
aggregations = InternalAggregations.reduce(aggregationsList, reduceContext);
List pipelineAggregators = firstResult.pipelineAggregators();
if (pipelineAggregators != null) {
List newAggs = StreamSupport.stream(aggregations.spliterator(), false)
.map((p) -> (InternalAggregation) p)
.collect(Collectors.toList());
for (SiblingPipelineAggregator pipelineAggregator : pipelineAggregators) {
InternalAggregation newAgg = pipelineAggregator.doReduce(new InternalAggregations(newAggs), reduceContext);
newAggs.add(newAgg);
}
aggregations = new InternalAggregations(newAggs);
}
}
//Collect profile results
SearchProfileShardResults shardResults = null;
if (firstResult.profileResults() != null) {
Map profileResults = new HashMap<>(queryResults.size());
for (AtomicArray.Entry entry : queryResults) {
String key = entry.value.queryResult().shardTarget().toString();
profileResults.put(key, entry.value.queryResult().profileResults());
}
shardResults = new SearchProfileShardResults(profileResults);
}
InternalSearchHits searchHits = new InternalSearchHits(hits.toArray(new InternalSearchHit[hits.size()]), totalHits, maxScore);
return new InternalSearchResponse(searchHits, aggregations, suggest, shardResults, timedOut, terminatedEarly);
}
/**
* returns the number of top results to be considered across all shards
*/
private static int topN(List> queryResults) {
QuerySearchResultProvider firstResult = queryResults.get(0).value;
int topN = firstResult.queryResult().size();
if (firstResult.includeFetch()) {
// if we did both query and fetch on the same go, we have fetched all the docs from each shards already, use them...
// this is also important since we shortcut and fetch only docs from "from" and up to "size"
topN *= queryResults.size();
}
return topN;
}
}