- Elasticsearch Guide: other versions:
- Getting Started
- Setup Elasticsearch
- Breaking changes
- Breaking changes in 5.1
- Breaking changes in 5.0
- Search and Query DSL changes
- Mapping changes
- Percolator changes
- Suggester changes
- Index APIs changes
- Document API changes
- Settings changes
- Allocation changes
- HTTP changes
- REST API changes
- CAT API changes
- Java API changes
- Packaging
- Plugin changes
- Filesystem related changes
- Path to data on disk
- Aggregation changes
- Script related changes
- API Conventions
- Document APIs
- Search APIs
- Aggregations
- Metrics Aggregations
- Avg Aggregation
- Cardinality Aggregation
- Extended Stats Aggregation
- Geo Bounds Aggregation
- Geo Centroid Aggregation
- Max Aggregation
- Min Aggregation
- Percentiles Aggregation
- Percentile Ranks Aggregation
- Scripted Metric Aggregation
- Stats Aggregation
- Sum Aggregation
- Top hits Aggregation
- Value Count Aggregation
- Bucket Aggregations
- Children Aggregation
- Date Histogram Aggregation
- Date Range Aggregation
- Diversified Sampler Aggregation
- Filter Aggregation
- Filters Aggregation
- Geo Distance Aggregation
- GeoHash grid Aggregation
- Global Aggregation
- Histogram Aggregation
- IP Range Aggregation
- Missing Aggregation
- Nested Aggregation
- Range Aggregation
- Reverse nested Aggregation
- Sampler Aggregation
- Significant Terms Aggregation
- Terms Aggregation
- Pipeline Aggregations
- Avg Bucket Aggregation
- Derivative Aggregation
- Max Bucket Aggregation
- Min Bucket Aggregation
- Sum Bucket Aggregation
- Stats Bucket Aggregation
- Extended Stats Bucket Aggregation
- Percentiles Bucket Aggregation
- Moving Average Aggregation
- Cumulative Sum Aggregation
- Bucket Script Aggregation
- Bucket Selector Aggregation
- Serial Differencing Aggregation
- Matrix Aggregations
- Caching heavy aggregations
- Returning only aggregation results
- Aggregation Metadata
- Metrics Aggregations
- Indices APIs
- Create Index
- Delete Index
- Get Index
- Indices Exists
- Open / Close Index API
- Shrink Index
- Rollover Index
- Put Mapping
- Get Mapping
- Get Field Mapping
- Types Exists
- Index Aliases
- Update Indices Settings
- Get Settings
- Analyze
- Index Templates
- Shadow replica indices
- Indices Stats
- Indices Segments
- Indices Recovery
- Indices Shard Stores
- Clear Cache
- Flush
- Refresh
- Force Merge
- cat APIs
- Cluster APIs
- Query DSL
- Mapping
- Analysis
- Anatomy of an analyzer
- Testing analyzers
- Analyzers
- Tokenizers
- Token Filters
- Standard Token Filter
- ASCII Folding Token Filter
- Length Token Filter
- Lowercase Token Filter
- Uppercase Token Filter
- NGram Token Filter
- Edge NGram Token Filter
- Porter Stem Token Filter
- Shingle Token Filter
- Stop Token Filter
- Word Delimiter Token Filter
- Stemmer Token Filter
- Stemmer Override Token Filter
- Keyword Marker Token Filter
- Keyword Repeat Token Filter
- KStem Token Filter
- Snowball Token Filter
- Phonetic Token Filter
- Synonym Token Filter
- Compound Word Token Filter
- Reverse Token Filter
- Elision Token Filter
- Truncate Token Filter
- Unique Token Filter
- Pattern Capture Token Filter
- Pattern Replace Token Filter
- Trim Token Filter
- Limit Token Count Token Filter
- Hunspell Token Filter
- Common Grams Token Filter
- Normalization Token Filter
- CJK Width Token Filter
- CJK Bigram Token Filter
- Delimited Payload Token Filter
- Keep Words Token Filter
- Keep Types Token Filter
- Classic Token Filter
- Apostrophe Token Filter
- Decimal Digit Token Filter
- Fingerprint Token Filter
- Minhash Token Filter
- Character Filters
- Modules
- Index Modules
- Ingest Node
- Pipeline Definition
- Ingest APIs
- Accessing Data in Pipelines
- Handling Failures in Pipelines
- Processors
- Append Processor
- Convert Processor
- Date Processor
- Date Index Name Processor
- Fail Processor
- Foreach Processor
- Grok Processor
- Gsub Processor
- Join Processor
- JSON Processor
- Lowercase Processor
- Remove Processor
- Rename Processor
- Script Processor
- Set Processor
- Split Processor
- Sort Processor
- Trim Processor
- Uppercase Processor
- Dot Expander Processor
- How To
- Testing
- Glossary of terms
- Release Notes
- 5.1.2 Release Notes
- 5.1.1 Release Notes
- 5.1.0 Release Notes
- 5.0.2 Release Notes
- 5.0.1 Release Notes
- 5.0.0 Combined Release Notes
- 5.0.0 GA Release Notes
- 5.0.0-rc1 Release Notes
- 5.0.0-beta1 Release Notes
- 5.0.0-alpha5 Release Notes
- 5.0.0-alpha4 Release Notes
- 5.0.0-alpha3 Release Notes
- 5.0.0-alpha2 Release Notes
- 5.0.0-alpha1 Release Notes
- 5.0.0-alpha1 Release Notes (Changes previously released in 2.x)
WARNING: Version 5.1 of Elasticsearch has passed its EOL date.
This documentation is no longer being maintained and may be removed. If you are running this version, we strongly advise you to upgrade. For the latest information, see the current release documentation.
Rescoring
editRescoring
editRescoring can help to improve precision by reordering just the top (eg
100 - 500) documents returned by the
query
and
post_filter
phases, using a
secondary (usually more costly) algorithm, instead of applying the
costly algorithm to all documents in the index.
A rescore
request is executed on each shard before it returns its
results to be sorted by the node handling the overall search request.
Currently the rescore API has only one implementation: the query rescorer, which uses a query to tweak the scoring. In the future, alternative rescorers may be made available, for example, a pair-wise rescorer.
the rescore
phase is not executed when sort
is used.
when exposing pagination to your users, you should not change
window_size
as you step through each page (by passing different
from
values) since that can alter the top hits causing results to
confusingly shift as the user steps through pages.
Query rescorer
editThe query rescorer executes a second query only on the Top-K results
returned by the query
and
post_filter
phases. The
number of docs which will be examined on each shard can be controlled by
the window_size
parameter, which defaults to
from
and size
.
By default the scores from the original query and the rescore query are
combined linearly to produce the final _score
for each document. The
relative importance of the original query and of the rescore query can
be controlled with the query_weight
and rescore_query_weight
respectively. Both default to 1
.
For example:
curl -s -XPOST 'localhost:9200/_search' -d '{ "query" : { "match" : { "field1" : { "operator" : "or", "query" : "the quick brown", "type" : "boolean" } } }, "rescore" : { "window_size" : 50, "query" : { "rescore_query" : { "match" : { "field1" : { "query" : "the quick brown", "type" : "phrase", "slop" : 2 } } }, "query_weight" : 0.7, "rescore_query_weight" : 1.2 } } } '
The way the scores are combined can be controlled with the score_mode
:
Score Mode | Description |
---|---|
|
Add the original score and the rescore query score. The default. |
|
Multiply the original score by the rescore query score. Useful
for |
|
Average the original score and the rescore query score. |
|
Take the max of original score and the rescore query score. |
|
Take the min of the original score and the rescore query score. |
Multiple Rescores
editIt is also possible to execute multiple rescores in sequence:
curl -s -XPOST 'localhost:9200/_search' -d '{ "query" : { "match" : { "field1" : { "operator" : "or", "query" : "the quick brown", "type" : "boolean" } } }, "rescore" : [ { "window_size" : 100, "query" : { "rescore_query" : { "match" : { "field1" : { "query" : "the quick brown", "type" : "phrase", "slop" : 2 } } }, "query_weight" : 0.7, "rescore_query_weight" : 1.2 } }, { "window_size" : 10, "query" : { "score_mode": "multiply", "rescore_query" : { "function_score" : { "script_score": { "script": { "lang": "painless", "inline": "Math.log10(doc['numeric'].value + 2)" } } } } } } ] } '
The first one gets the results of the query then the second one gets the results of the first, etc. The second rescore will "see" the sorting done by the first rescore so it is possible to use a large window on the first rescore to pull documents into a smaller window for the second rescore.
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