use shard_min_doc_count also in TermsAggregation
This was discussed in issue #6041 and #5998 . closes #6143
This commit is contained in:
Britta Weber
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@ -292,6 +292,8 @@ If the number of unique terms is greater than `size`, the returned list can be s
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(it could be that the term counts are slightly off and it could even be that a term that should have been in the top
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size buckets was not returned).
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coming[1.2.0] If set to `0`, the `size` will be set to `Integer.MAX_VALUE`.
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To ensure better accuracy a multiple of the final `size` is used as the number of terms to request from each shard
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using a heuristic based on the number of shards. To take manual control of this setting the `shard_size` parameter
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can be used to control the volumes of candidate terms produced by each shard.
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@ -300,7 +302,11 @@ Low-frequency terms can turn out to be the most interesting ones once all result
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significant_terms aggregation can produce higher-quality results when the `shard_size` parameter is set to
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values significantly higher than the `size` setting. This ensures that a bigger volume of promising candidate terms are given
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a consolidated review by the reducing node before the final selection. Obviously large candidate term lists
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will cause extra network traffic and RAM usage so this is quality/cost trade off that needs to be balanced.
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will cause extra network traffic and RAM usage so this is quality/cost trade off that needs to be balanced. If `shard_size` is set to -1 (the default) then `shard_size` will be automatically estimated based on the number of shards and the `size` parameter.
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coming[1.2.0] If set to `0`, the `shard_size` will be set to `Integer.MAX_VALUE`.
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NOTE: `shard_size` cannot be smaller than `size` (as it doesn't make much sense). When it is, elasticsearch will
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override it and reset it to be equal to `size`.
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@ -330,6 +336,8 @@ The above aggregation would only return tags which have been found in 10 hits or
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Terms that score highly will be collected on a shard level and merged with the terms collected from other shards in a second step. However, the shard does not have the information about the global term frequencies available. The decision if a term is added to a candidate list depends only on the score computed on the shard using local shard frequencies, not the global frequencies of the word. The `min_doc_count` criterion is only applied after merging local terms statistics of all shards. In a way the decision to add the term as a candidate is made without being very _certain_ about if the term will actually reach the required `min_doc_count`. This might cause many (globally) high frequent terms to be missing in the final result if low frequent but high scoring terms populated the candidate lists. To avoid this, the `shard_size` parameter can be increased to allow more candidate terms on the shards. However, this increases memory consumption and network traffic.
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coming[1.2.0] `shard_min_doc_count` parameter
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The parameter `shard_min_doc_count` regulates the _certainty_ a shard has if the term should actually be added to the candidate list or not with respect to the `min_doc_count`. Terms will only be considered if their local shard frequency within the set is higher than the `shard_min_doc_count`. If your dictionary contains many low frequent words and you are not interested in these (for example misspellings), then you can set the `shard_min_doc_count` parameter to filter out candidate terms on a shard level that will with a resonable certainty not reach the required `min_doc_count` even after merging the local frequencies. `shard_min_doc_count` is set to `1` per default and has no effect unless you explicitly set it.
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@ -50,7 +50,8 @@ default, the node coordinating the search process will request each shard to pro
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and once all shards respond, it will reduce the results to the final list that will then be returned to the client.
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This means that if the number of unique terms is greater than `size`, the returned list is slightly off and not accurate
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(it could be that the term counts are slightly off and it could even be that a term that should have been in the top
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size buckets was not returned).
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size buckets was not returned). If set to `0`, the `size` will be set to `Integer.MAX_VALUE`.
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The higher the requested `size` is, the more accurate the results will be, but also, the more expensive it will be to
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compute the final results (both due to bigger priority queues that are managed on a shard level and due to bigger data
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@ -60,7 +61,8 @@ The `shard_size` parameter can be used to minimize the extra work that comes wi
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it will determine how many terms the coordinating node will request from each shard. Once all the shards responded, the
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coordinating node will then reduce them to a final result which will be based on the `size` parameter - this way,
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one can increase the accuracy of the returned terms and avoid the overhead of streaming a big list of buckets back to
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the client.
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the client. If set to `0`, the `shard_size` will be set to `Integer.MAX_VALUE`.
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NOTE: `shard_size` cannot be smaller than `size` (as it doesn't make much sense). When it is, elasticsearch will
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override it and reset it to be equal to `size`.
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@ -204,6 +206,15 @@ It is possible to only return terms that match more than a configured number of
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The above aggregation would only return tags which have been found in 10 hits or more. Default value is `1`.
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Terms are collected and ordered on a shard level and merged with the terms collected from other shards in a second step. However, the shard does not have the information about the global document count available. The decision if a term is added to a candidate list depends only on the order computed on the shard using local shard frequencies. The `min_doc_count` criterion is only applied after merging local terms statistics of all shards. In a way the decision to add the term as a candidate is made without being very _certain_ about if the term will actually reach the required `min_doc_count`. This might cause many (globally) high frequent terms to be missing in the final result if low frequent terms populated the candidate lists. To avoid this, the `shard_size` parameter can be increased to allow more candidate terms on the shards. However, this increases memory consumption and network traffic.
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coming[1.2.0] `shard_min_doc_count` parameter
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The parameter `shard_min_doc_count` regulates the _certainty_ a shard has if the term should actually be added to the candidate list or not with respect to the `min_doc_count`. Terms will only be considered if their local shard frequency within the set is higher than the `shard_min_doc_count`. If your dictionary contains many low frequent terms and you are not interested in those (for example misspellings), then you can set the `shard_min_doc_count` parameter to filter out candidate terms on a shard level that will with a resonable certainty not reach the required `min_doc_count` even after merging the local counts. `shard_min_doc_count` is set to `0` per default and has no effect unless you explicitly set it.
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NOTE: Setting `min_doc_count`=`0` will also return buckets for terms that didn't match any hit. However, some of
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the returned terms which have a document count of zero might only belong to deleted documents, so there is
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no warranty that a `match_all` query would find a positive document count for those terms.
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@ -211,6 +222,7 @@ NOTE: Setting `min_doc_count`=`0` will also return buckets for terms that did
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WARNING: When NOT sorting on `doc_count` descending, high values of `min_doc_count` may return a number of buckets
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which is less than `size` because not enough data was gathered from the shards. Missing buckets can be
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back by increasing `shard_size`.
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Setting `shard_min_doc_count` too high will cause terms to be filtered out on a shard level. This value should be set much lower than `min_doc_count/#shards`.
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==== Script
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@ -112,8 +112,10 @@ public class DoubleTermsAggregator extends TermsAggregator {
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spare.term = Double.longBitsToDouble(bucketOrds.get(i));
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spare.docCount = bucketDocCount(i);
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spare.bucketOrd = i;
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if (bucketCountThresholds.getShardMinDocCount() <= spare.docCount) {
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spare = (DoubleTerms.Bucket) ordered.insertWithOverflow(spare);
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}
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}
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final InternalTerms.Bucket[] list = new InternalTerms.Bucket[ordered.size()];
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for (int i = ordered.size() - 1; i >= 0; --i) {
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@ -138,8 +138,10 @@ public class GlobalOrdinalsStringTermsAggregator extends AbstractStringTermsAggr
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spare.bucketOrd = bucketOrd;
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spare.docCount = bucketDocCount;
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copy(globalValues.getValueByOrd(globalTermOrd), spare.termBytes);
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if (bucketCountThresholds.getShardMinDocCount() <= spare.docCount) {
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spare = (StringTerms.Bucket) ordered.insertWithOverflow(spare);
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}
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}
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final InternalTerms.Bucket[] list = new InternalTerms.Bucket[ordered.size()];
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for (int i = ordered.size() - 1; i >= 0; --i) {
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@ -112,8 +112,10 @@ public class LongTermsAggregator extends TermsAggregator {
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spare.term = bucketOrds.get(i);
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spare.docCount = bucketDocCount(i);
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spare.bucketOrd = i;
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if (bucketCountThresholds.getShardMinDocCount() <= spare.docCount) {
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spare = (LongTerms.Bucket) ordered.insertWithOverflow(spare);
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}
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}
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final InternalTerms.Bucket[] list = new InternalTerms.Bucket[ordered.size()];
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for (int i = ordered.size() - 1; i >= 0; --i) {
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@ -221,8 +221,10 @@ public class StringTermsAggregator extends AbstractStringTermsAggregator {
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bucketOrds.get(i, spare.termBytes);
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spare.docCount = bucketDocCount(i);
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spare.bucketOrd = i;
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if (bucketCountThresholds.getShardMinDocCount() <= spare.docCount) {
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spare = (StringTerms.Bucket) ordered.insertWithOverflow(spare);
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}
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}
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final InternalTerms.Bucket[] list = new InternalTerms.Bucket[ordered.size()];
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for (int i = ordered.size() - 1; i >= 0; --i) {
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@ -69,6 +69,14 @@ public class TermsBuilder extends ValuesSourceAggregationBuilder<TermsBuilder> {
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return this;
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}
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/**
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* Set the minimum document count terms should have on the shard in order to appear in the response.
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*/
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public TermsBuilder shardMinDocCount(long shardMinDocCount) {
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bucketCountThresholds.setShardMinDocCount(shardMinDocCount);
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return this;
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}
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/**
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* Define a regular expression that will determine what terms should be aggregated. The regular expression is based
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* on the {@link java.util.regex.Pattern} class.
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@ -26,6 +26,8 @@ import org.elasticsearch.search.aggregations.bucket.filter.InternalFilter;
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import org.elasticsearch.search.aggregations.bucket.significant.SignificantTerms;
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import org.elasticsearch.search.aggregations.bucket.significant.SignificantTermsAggregatorFactory;
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import org.elasticsearch.search.aggregations.bucket.significant.SignificantTermsBuilder;
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import org.elasticsearch.search.aggregations.bucket.terms.Terms;
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import org.elasticsearch.search.aggregations.bucket.terms.TermsBuilder;
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import org.elasticsearch.test.ElasticsearchIntegrationTest;
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import org.junit.Test;
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@ -41,7 +43,7 @@ import static org.hamcrest.Matchers.equalTo;
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/**
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*
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*/
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public class SignificantTermsMinDocCountTests extends ElasticsearchIntegrationTest {
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public class TermsShardMinDocCountTests extends ElasticsearchIntegrationTest {
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private static final String index = "someindex";
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private static final String type = "testtype";
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public String randomExecutionHint() {
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@ -50,7 +52,7 @@ public class SignificantTermsMinDocCountTests extends ElasticsearchIntegrationTe
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// see https://github.com/elasticsearch/elasticsearch/issues/5998
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@Test
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public void shardMinDocCountTest() throws Exception {
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public void shardMinDocCountSignificantTermsTest() throws Exception {
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String termtype = "string";
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if (randomBoolean()) {
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@ -108,4 +110,53 @@ public class SignificantTermsMinDocCountTests extends ElasticsearchIntegrationTe
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}
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}
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// see https://github.com/elasticsearch/elasticsearch/issues/5998
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@Test
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public void shardMinDocCountTermsTest() throws Exception {
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final String [] termTypes = {"string", "long", "integer", "float", "double"};
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String termtype = termTypes[randomInt(termTypes.length - 1)];
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assertAcked(prepareCreate(index).setSettings(SETTING_NUMBER_OF_SHARDS, 1, SETTING_NUMBER_OF_REPLICAS, 0).addMapping(type, "{\"properties\":{\"text\": {\"type\": \"" + termtype + "\"}}}"));
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ensureYellow(index);
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List<IndexRequestBuilder> indexBuilders = new ArrayList<>();
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addTermsDocs("1", 1, indexBuilders);//low doc freq but high score
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addTermsDocs("2", 1, indexBuilders);
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addTermsDocs("3", 1, indexBuilders);
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addTermsDocs("4", 1, indexBuilders);
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addTermsDocs("5", 3, indexBuilders);//low score but high doc freq
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addTermsDocs("6", 3, indexBuilders);
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indexRandom(true, indexBuilders);
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// first, check that indeed when not setting the shardMinDocCount parameter 0 terms are returned
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SearchResponse response = client().prepareSearch(index)
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.addAggregation(
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new TermsBuilder("myTerms").field("text").minDocCount(2).size(2).executionHint(randomExecutionHint()).order(Terms.Order.term(true))
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)
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.execute()
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.actionGet();
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assertSearchResponse(response);
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Terms sigterms = response.getAggregations().get("myTerms");
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assertThat(sigterms.getBuckets().size(), equalTo(0));
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response = client().prepareSearch(index)
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.addAggregation(
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new TermsBuilder("myTerms").field("text").minDocCount(2).shardMinDocCount(2).size(2).executionHint(randomExecutionHint()).order(Terms.Order.term(true))
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)
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.execute()
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.actionGet();
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assertSearchResponse(response);
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sigterms = response.getAggregations().get("myTerms");
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assertThat(sigterms.getBuckets().size(), equalTo(2));
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}
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private void addTermsDocs(String term, int numDocs, List<IndexRequestBuilder> builders) {
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String sourceClass = "{\"text\": \"" + term + "\"}";
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for (int i = 0; i < numDocs; i++) {
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builders.add(client().prepareIndex(index, type).setSource(sourceClass));
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}
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}
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}
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