- Elasticsearch Guide: other versions:
- What is Elasticsearch?
- What’s new in 8.10
- Set up Elasticsearch
- Installing Elasticsearch
- Run Elasticsearch locally
- Configuring Elasticsearch
- Important Elasticsearch configuration
- Secure settings
- Auditing settings
- Circuit breaker settings
- Cluster-level shard allocation and routing settings
- Miscellaneous cluster settings
- Cross-cluster replication settings
- Discovery and cluster formation settings
- Field data cache settings
- Health Diagnostic settings
- Index lifecycle management settings
- Index management settings
- Index recovery settings
- Indexing buffer settings
- License settings
- Local gateway settings
- Logging
- Machine learning settings
- Monitoring settings
- Node
- Networking
- Node query cache settings
- Search settings
- Security settings
- Shard request cache settings
- Snapshot and restore settings
- Transforms settings
- Thread pools
- Watcher settings
- Advanced configuration
- Important system configuration
- Bootstrap Checks
- Heap size check
- File descriptor check
- Memory lock check
- Maximum number of threads check
- Max file size check
- Maximum size virtual memory check
- Maximum map count check
- Client JVM check
- Use serial collector check
- System call filter check
- OnError and OnOutOfMemoryError checks
- Early-access check
- All permission check
- Discovery configuration check
- Bootstrap Checks for X-Pack
- Starting Elasticsearch
- Stopping Elasticsearch
- Discovery and cluster formation
- Add and remove nodes in your cluster
- Full-cluster restart and rolling restart
- Remote clusters
- Plugins
- Upgrade Elasticsearch
- Index modules
- Mapping
- Text analysis
- Overview
- Concepts
- Configure text analysis
- Built-in analyzer reference
- Tokenizer reference
- Token filter reference
- Apostrophe
- ASCII folding
- CJK bigram
- CJK width
- Classic
- Common grams
- Conditional
- Decimal digit
- Delimited payload
- Dictionary decompounder
- Edge n-gram
- Elision
- Fingerprint
- Flatten graph
- Hunspell
- Hyphenation decompounder
- Keep types
- Keep words
- Keyword marker
- Keyword repeat
- KStem
- Length
- Limit token count
- Lowercase
- MinHash
- Multiplexer
- N-gram
- Normalization
- Pattern capture
- Pattern replace
- Phonetic
- Porter stem
- Predicate script
- Remove duplicates
- Reverse
- Shingle
- Snowball
- Stemmer
- Stemmer override
- Stop
- Synonym
- Synonym graph
- Trim
- Truncate
- Unique
- Uppercase
- Word delimiter
- Word delimiter graph
- Character filters reference
- Normalizers
- Index templates
- Data streams
- Ingest pipelines
- Example: Parse logs
- Enrich your data
- Processor reference
- Append
- Attachment
- Bytes
- Circle
- Community ID
- Convert
- CSV
- Date
- Date index name
- Dissect
- Dot expander
- Drop
- Enrich
- Fail
- Fingerprint
- Foreach
- Geo-grid
- GeoIP
- Grok
- Gsub
- HTML strip
- Inference
- Join
- JSON
- KV
- Lowercase
- Network direction
- Pipeline
- Redact
- Registered domain
- Remove
- Rename
- Reroute
- Script
- Set
- Set security user
- Sort
- Split
- Trim
- Uppercase
- URL decode
- URI parts
- User agent
- Aliases
- Search your data
- Collapse search results
- Filter search results
- Highlighting
- Long-running searches
- Near real-time search
- Paginate search results
- Retrieve inner hits
- Retrieve selected fields
- Search across clusters
- Search multiple data streams and indices
- Search shard routing
- Search templates
- Search with synonyms
- Sort search results
- kNN search
- Semantic search
- Searching with query rules
- Query DSL
- Aggregations
- Bucket aggregations
- Adjacency matrix
- Auto-interval date histogram
- Categorize text
- Children
- Composite
- Date histogram
- Date range
- Diversified sampler
- Filter
- Filters
- Frequent item sets
- Geo-distance
- Geohash grid
- Geohex grid
- Geotile grid
- Global
- Histogram
- IP prefix
- IP range
- Missing
- Multi Terms
- Nested
- Parent
- Random sampler
- Range
- Rare terms
- Reverse nested
- Sampler
- Significant terms
- Significant text
- Terms
- Time series
- Variable width histogram
- Subtleties of bucketing range fields
- Metrics aggregations
- Pipeline aggregations
- Average bucket
- Bucket script
- Bucket count K-S test
- Bucket correlation
- Bucket selector
- Bucket sort
- Change point
- Cumulative cardinality
- Cumulative sum
- Derivative
- Extended stats bucket
- Inference bucket
- Max bucket
- Min bucket
- Moving function
- Moving percentiles
- Normalize
- Percentiles bucket
- Serial differencing
- Stats bucket
- Sum bucket
- Bucket aggregations
- Geospatial analysis
- EQL
- SQL
- Overview
- Getting Started with SQL
- Conventions and Terminology
- Security
- SQL REST API
- SQL Translate API
- SQL CLI
- SQL JDBC
- SQL ODBC
- SQL Client Applications
- SQL Language
- Functions and Operators
- Comparison Operators
- Logical Operators
- Math Operators
- Cast Operators
- LIKE and RLIKE Operators
- Aggregate Functions
- Grouping Functions
- Date/Time and Interval Functions and Operators
- Full-Text Search Functions
- Mathematical Functions
- String Functions
- Type Conversion Functions
- Geo Functions
- Conditional Functions And Expressions
- System Functions
- Reserved keywords
- SQL Limitations
- Scripting
- Data management
- ILM: Manage the index lifecycle
- Tutorial: Customize built-in policies
- Tutorial: Automate rollover
- Index management in Kibana
- Overview
- Concepts
- Index lifecycle actions
- Configure a lifecycle policy
- Migrate index allocation filters to node roles
- Troubleshooting index lifecycle management errors
- Start and stop index lifecycle management
- Manage existing indices
- Skip rollover
- Restore a managed data stream or index
- Data tiers
- Autoscaling
- Monitor a cluster
- Roll up or transform your data
- Set up a cluster for high availability
- Snapshot and restore
- Secure the Elastic Stack
- Elasticsearch security principles
- Start the Elastic Stack with security enabled automatically
- Manually configure security
- Updating node security certificates
- User authentication
- Built-in users
- Service accounts
- Internal users
- Token-based authentication services
- User profiles
- Realms
- Realm chains
- Security domains
- Active Directory user authentication
- File-based user authentication
- LDAP user authentication
- Native user authentication
- OpenID Connect authentication
- PKI user authentication
- SAML authentication
- Kerberos authentication
- JWT authentication
- Integrating with other authentication systems
- Enabling anonymous access
- Looking up users without authentication
- Controlling the user cache
- Configuring SAML single-sign-on on the Elastic Stack
- Configuring single sign-on to the Elastic Stack using OpenID Connect
- User authorization
- Built-in roles
- Defining roles
- Role restriction
- Security privileges
- Document level security
- Field level security
- Granting privileges for data streams and aliases
- Mapping users and groups to roles
- Setting up field and document level security
- Submitting requests on behalf of other users
- Configuring authorization delegation
- Customizing roles and authorization
- Enable audit logging
- Restricting connections with IP filtering
- Securing clients and integrations
- Operator privileges
- Troubleshooting
- Some settings are not returned via the nodes settings API
- Authorization exceptions
- Users command fails due to extra arguments
- Users are frequently locked out of Active Directory
- Certificate verification fails for curl on Mac
- SSLHandshakeException causes connections to fail
- Common SSL/TLS exceptions
- Common Kerberos exceptions
- Common SAML issues
- Internal Server Error in Kibana
- Setup-passwords command fails due to connection failure
- Failures due to relocation of the configuration files
- Limitations
- Watcher
- Command line tools
- elasticsearch-certgen
- elasticsearch-certutil
- elasticsearch-create-enrollment-token
- elasticsearch-croneval
- elasticsearch-keystore
- elasticsearch-node
- elasticsearch-reconfigure-node
- elasticsearch-reset-password
- elasticsearch-saml-metadata
- elasticsearch-service-tokens
- elasticsearch-setup-passwords
- elasticsearch-shard
- elasticsearch-syskeygen
- elasticsearch-users
- How to
- Troubleshooting
- Fix common cluster issues
- Diagnose unassigned shards
- Add a missing tier to the system
- Allow Elasticsearch to allocate the data in the system
- Allow Elasticsearch to allocate the index
- Indices mix index allocation filters with data tiers node roles to move through data tiers
- Not enough nodes to allocate all shard replicas
- Total number of shards for an index on a single node exceeded
- Total number of shards per node has been reached
- Troubleshooting corruption
- Fix data nodes out of disk
- Fix master nodes out of disk
- Fix other role nodes out of disk
- Start index lifecycle management
- Start Snapshot Lifecycle Management
- Restore from snapshot
- Multiple deployments writing to the same snapshot repository
- Addressing repeated snapshot policy failures
- Troubleshooting an unstable cluster
- Troubleshooting discovery
- Troubleshooting monitoring
- Troubleshooting transforms
- Troubleshooting Watcher
- Troubleshooting searches
- Troubleshooting shards capacity health issues
- REST APIs
- API conventions
- Common options
- REST API compatibility
- Autoscaling APIs
- Behavioral Analytics APIs
- Compact and aligned text (CAT) APIs
- cat aliases
- cat allocation
- cat anomaly detectors
- cat component templates
- cat count
- cat data frame analytics
- cat datafeeds
- cat fielddata
- cat health
- cat indices
- cat master
- cat nodeattrs
- cat nodes
- cat pending tasks
- cat plugins
- cat recovery
- cat repositories
- cat segments
- cat shards
- cat snapshots
- cat task management
- cat templates
- cat thread pool
- cat trained model
- cat transforms
- Cluster APIs
- Cluster allocation explain
- Cluster get settings
- Cluster health
- Health
- Cluster reroute
- Cluster state
- Cluster stats
- Cluster update settings
- Nodes feature usage
- Nodes hot threads
- Nodes info
- Prevalidate node removal
- Nodes reload secure settings
- Nodes stats
- Cluster Info
- Pending cluster tasks
- Remote cluster info
- Task management
- Voting configuration exclusions
- Create or update desired nodes
- Get desired nodes
- Delete desired nodes
- Get desired balance
- Reset desired balance
- Cross-cluster replication APIs
- Data stream APIs
- Document APIs
- Enrich APIs
- EQL APIs
- Features APIs
- Fleet APIs
- Find structure API
- Graph explore API
- Index APIs
- Alias exists
- Aliases
- Analyze
- Analyze index disk usage
- Clear cache
- Clone index
- Close index
- Create index
- Create or update alias
- Create or update component template
- Create or update index template
- Create or update index template (legacy)
- Delete component template
- Delete dangling index
- Delete alias
- Delete index
- Delete index template
- Delete index template (legacy)
- Exists
- Field usage stats
- Flush
- Force merge
- Get alias
- Get component template
- Get field mapping
- Get index
- Get index settings
- Get index template
- Get index template (legacy)
- Get mapping
- Import dangling index
- Index recovery
- Index segments
- Index shard stores
- Index stats
- Index template exists (legacy)
- List dangling indices
- Open index
- Refresh
- Resolve index
- Rollover
- Shrink index
- Simulate index
- Simulate template
- Split index
- Unfreeze index
- Update index settings
- Update mapping
- Index lifecycle management APIs
- Create or update lifecycle policy
- Get policy
- Delete policy
- Move to step
- Remove policy
- Retry policy
- Get index lifecycle management status
- Explain lifecycle
- Start index lifecycle management
- Stop index lifecycle management
- Migrate indices, ILM policies, and legacy, composable and component templates to data tiers routing
- Ingest APIs
- Info API
- Licensing APIs
- Logstash APIs
- Machine learning APIs
- Machine learning anomaly detection APIs
- Add events to calendar
- Add jobs to calendar
- Close jobs
- Create jobs
- Create calendars
- Create datafeeds
- Create filters
- Delete calendars
- Delete datafeeds
- Delete events from calendar
- Delete filters
- Delete forecasts
- Delete jobs
- Delete jobs from calendar
- Delete model snapshots
- Delete expired data
- Estimate model memory
- Flush jobs
- Forecast jobs
- Get buckets
- Get calendars
- Get categories
- Get datafeeds
- Get datafeed statistics
- Get influencers
- Get jobs
- Get job statistics
- Get model snapshots
- Get model snapshot upgrade statistics
- Get overall buckets
- Get scheduled events
- Get filters
- Get records
- Open jobs
- Post data to jobs
- Preview datafeeds
- Reset jobs
- Revert model snapshots
- Start datafeeds
- Stop datafeeds
- Update datafeeds
- Update filters
- Update jobs
- Update model snapshots
- Upgrade model snapshots
- Machine learning data frame analytics APIs
- Create data frame analytics jobs
- Delete data frame analytics jobs
- Evaluate data frame analytics
- Explain data frame analytics
- Get data frame analytics jobs
- Get data frame analytics jobs stats
- Preview data frame analytics
- Start data frame analytics jobs
- Stop data frame analytics jobs
- Update data frame analytics jobs
- Machine learning trained model APIs
- Clear trained model deployment cache
- Create or update trained model aliases
- Create part of a trained model
- Create trained models
- Create trained model vocabulary
- Delete trained model aliases
- Delete trained models
- Get trained models
- Get trained models stats
- Infer trained model
- Start trained model deployment
- Stop trained model deployment
- Update trained model deployment
- Migration APIs
- Node lifecycle APIs
- Query rules APIs
- Reload search analyzers API
- Repositories metering APIs
- Rollup APIs
- Script APIs
- Search APIs
- Search Application APIs
- Searchable snapshots APIs
- Security APIs
- Authenticate
- Change passwords
- Clear cache
- Clear roles cache
- Clear privileges cache
- Clear API key cache
- Clear service account token caches
- Create API keys
- Create or update application privileges
- Create or update role mappings
- Create or update roles
- Create or update users
- Create service account tokens
- Delegate PKI authentication
- Delete application privileges
- Delete role mappings
- Delete roles
- Delete service account token
- Delete users
- Disable users
- Enable users
- Enroll Kibana
- Enroll node
- Get API key information
- Get application privileges
- Get builtin privileges
- Get role mappings
- Get roles
- Get service accounts
- Get service account credentials
- Get token
- Get user privileges
- Get users
- Grant API keys
- Has privileges
- Invalidate API key
- Invalidate token
- OpenID Connect prepare authentication
- OpenID Connect authenticate
- OpenID Connect logout
- Query API key information
- Update API key
- Bulk update API keys
- SAML prepare authentication
- SAML authenticate
- SAML logout
- SAML invalidate
- SAML complete logout
- SAML service provider metadata
- SSL certificate
- Activate user profile
- Disable user profile
- Enable user profile
- Get user profiles
- Suggest user profile
- Update user profile data
- Has privileges user profile
- Create Cross-Cluster API key
- Update Cross-Cluster API key
- Snapshot and restore APIs
- Snapshot lifecycle management APIs
- SQL APIs
- Synonyms APIs
- Transform APIs
- Usage API
- Watcher APIs
- Definitions
- Migration guide
- Release notes
- Elasticsearch version 8.10.4
- Elasticsearch version 8.10.3
- Elasticsearch version 8.10.2
- Elasticsearch version 8.10.1
- Elasticsearch version 8.10.0
- Elasticsearch version 8.9.2
- Elasticsearch version 8.9.1
- Elasticsearch version 8.9.0
- Elasticsearch version 8.8.2
- Elasticsearch version 8.8.1
- Elasticsearch version 8.8.0
- Elasticsearch version 8.7.1
- Elasticsearch version 8.7.0
- Elasticsearch version 8.6.2
- Elasticsearch version 8.6.1
- Elasticsearch version 8.6.0
- Elasticsearch version 8.5.3
- Elasticsearch version 8.5.2
- Elasticsearch version 8.5.1
- Elasticsearch version 8.5.0
- Elasticsearch version 8.4.3
- Elasticsearch version 8.4.2
- Elasticsearch version 8.4.1
- Elasticsearch version 8.4.0
- Elasticsearch version 8.3.3
- Elasticsearch version 8.3.2
- Elasticsearch version 8.3.1
- Elasticsearch version 8.3.0
- Elasticsearch version 8.2.3
- Elasticsearch version 8.2.2
- Elasticsearch version 8.2.1
- Elasticsearch version 8.2.0
- Elasticsearch version 8.1.3
- Elasticsearch version 8.1.2
- Elasticsearch version 8.1.1
- Elasticsearch version 8.1.0
- Elasticsearch version 8.0.1
- Elasticsearch version 8.0.0
- Elasticsearch version 8.0.0-rc2
- Elasticsearch version 8.0.0-rc1
- Elasticsearch version 8.0.0-beta1
- Elasticsearch version 8.0.0-alpha2
- Elasticsearch version 8.0.0-alpha1
- Dependencies and versions
Percolator field type
editPercolator field type
editThe percolator
field type parses a json structure into a native query and
stores that query, so that the percolate query
can use it to match provided documents.
Any field that contains a json object can be configured to be a percolator
field. The percolator field type has no settings. Just configuring the percolator
field type is sufficient to instruct Elasticsearch to treat a field as a
query.
If the following mapping configures the percolator
field type for the
query
field:
response = client.indices.create( index: 'my-index-000001', body: { mappings: { properties: { query: { type: 'percolator' }, field: { type: 'text' } } } } ) puts response
PUT my-index-000001 { "mappings": { "properties": { "query": { "type": "percolator" }, "field": { "type": "text" } } } }
Then you can index a query:
response = client.index( index: 'my-index-000001', id: 'match_value', body: { query: { match: { field: 'value' } } } ) puts response
PUT my-index-000001/_doc/match_value { "query": { "match": { "field": "value" } } }
Fields referred to in a percolator query must already exist in the mapping associated with the index used for percolation. In order to make sure these fields exist, add or update a mapping via the create index or update mapping APIs.
Reindexing your percolator queries
editReindexing percolator queries is sometimes required to benefit from improvements made to the percolator
field type in
new releases.
Reindexing percolator queries can be reindexed by using the reindex api. Lets take a look at the following index with a percolator field type:
response = client.indices.create( index: 'index', body: { mappings: { properties: { query: { type: 'percolator' }, body: { type: 'text' } } } } ) puts response response = client.indices.update_aliases( body: { actions: [ { add: { index: 'index', alias: 'queries' } } ] } ) puts response response = client.index( index: 'queries', id: 1, refresh: true, body: { query: { match: { body: 'quick brown fox' } } } ) puts response
PUT index { "mappings": { "properties": { "query" : { "type" : "percolator" }, "body" : { "type": "text" } } } } POST _aliases { "actions": [ { "add": { "index": "index", "alias": "queries" } } ] } PUT queries/_doc/1?refresh { "query" : { "match" : { "body" : "quick brown fox" } } }
It is always recommended to define an alias for your index, so that in case of a reindex systems / applications don’t need to be changed to know that the percolator queries are now in a different index. |
Lets say you’re going to upgrade to a new major version and in order for the new Elasticsearch version to still be able to read your queries you need to reindex your queries into a new index on the current Elasticsearch version:
response = client.indices.create( index: 'new_index', body: { mappings: { properties: { query: { type: 'percolator' }, body: { type: 'text' } } } } ) puts response response = client.reindex( refresh: true, body: { source: { index: 'index' }, dest: { index: 'new_index' } } ) puts response response = client.indices.update_aliases( body: { actions: [ { remove: { index: 'index', alias: 'queries' } }, { add: { index: 'new_index', alias: 'queries' } } ] } ) puts response
PUT new_index { "mappings": { "properties": { "query" : { "type" : "percolator" }, "body" : { "type": "text" } } } } POST /_reindex?refresh { "source": { "index": "index" }, "dest": { "index": "new_index" } } POST _aliases { "actions": [ { "remove": { "index" : "index", "alias": "queries" } }, { "add": { "index": "new_index", "alias": "queries" } } ] }
Executing the percolate
query via the queries
alias:
response = client.search( index: 'queries', body: { query: { percolate: { field: 'query', document: { body: 'fox jumps over the lazy dog' } } } } ) puts response
GET /queries/_search { "query": { "percolate" : { "field" : "query", "document" : { "body" : "fox jumps over the lazy dog" } } } }
now returns matches from the new index:
{ "took": 3, "timed_out": false, "_shards": { "total": 1, "successful": 1, "skipped" : 0, "failed": 0 }, "hits": { "total" : { "value": 1, "relation": "eq" }, "max_score": 0.13076457, "hits": [ { "_index": "new_index", "_id": "1", "_score": 0.13076457, "_source": { "query": { "match": { "body": "quick brown fox" } } }, "fields" : { "_percolator_document_slot" : [0] } } ] } }
Optimizing query time text analysis
editWhen the percolator verifies a percolator candidate match it is going to parse, perform query time text analysis and actually run
the percolator query on the document being percolated. This is done for each candidate match and every time the percolate
query executes.
If your query time text analysis is relatively expensive part of query parsing then text analysis can become the
dominating factor time is being spent on when percolating. This query parsing overhead can become noticeable when the
percolator ends up verifying many candidate percolator query matches.
To avoid the most expensive part of text analysis at percolate time. One can choose to do the expensive part of text analysis
when indexing the percolator query. This requires using two different analyzers. The first analyzer actually performs
text analysis that needs be performed (expensive part). The second analyzer (usually whitespace) just splits the generated tokens
that the first analyzer has produced. Then before indexing a percolator query, the analyze api should be used to analyze the query
text with the more expensive analyzer. The result of the analyze api, the tokens, should be used to substitute the original query
text in the percolator query. It is important that the query should now be configured to override the analyzer from the mapping and
just the second analyzer. Most text based queries support an analyzer
option (match
, query_string
, simple_query_string
).
Using this approach the expensive text analysis is performed once instead of many times.
Lets demonstrate this workflow via a simplified example.
Lets say we want to index the following percolator query:
{ "query" : { "match" : { "body" : { "query" : "missing bicycles" } } } }
with these settings and mapping:
response = client.indices.create( index: 'test_index', body: { settings: { analysis: { analyzer: { my_analyzer: { tokenizer: 'standard', filter: [ 'lowercase', 'porter_stem' ] } } } }, mappings: { properties: { query: { type: 'percolator' }, body: { type: 'text', analyzer: 'my_analyzer' } } } } ) puts response
PUT /test_index { "settings": { "analysis": { "analyzer": { "my_analyzer" : { "tokenizer": "standard", "filter" : ["lowercase", "porter_stem"] } } } }, "mappings": { "properties": { "query" : { "type": "percolator" }, "body" : { "type": "text", "analyzer": "my_analyzer" } } } }
First we need to use the analyze api to perform the text analysis prior to indexing:
response = client.indices.analyze( index: 'test_index', body: { analyzer: 'my_analyzer', text: 'missing bicycles' } ) puts response
POST /test_index/_analyze { "analyzer" : "my_analyzer", "text" : "missing bicycles" }
This results the following response:
{ "tokens": [ { "token": "miss", "start_offset": 0, "end_offset": 7, "type": "<ALPHANUM>", "position": 0 }, { "token": "bicycl", "start_offset": 8, "end_offset": 16, "type": "<ALPHANUM>", "position": 1 } ] }
All the tokens in the returned order need to replace the query text in the percolator query:
response = client.index( index: 'test_index', id: 1, refresh: true, body: { query: { match: { body: { query: 'miss bicycl', analyzer: 'whitespace' } } } } ) puts response
PUT /test_index/_doc/1?refresh { "query" : { "match" : { "body" : { "query" : "miss bicycl", "analyzer" : "whitespace" } } } }
It is important to select a whitespace analyzer here, otherwise the analyzer defined in the mapping will be used,
which defeats the point of using this workflow. Note that |
The analyze api prior to the indexing the percolator flow should be done for each percolator query.
At percolate time nothing changes and the percolate
query can be defined normally:
response = client.search( index: 'test_index', body: { query: { percolate: { field: 'query', document: { body: 'Bycicles are missing' } } } } ) puts response
GET /test_index/_search { "query": { "percolate" : { "field" : "query", "document" : { "body" : "Bycicles are missing" } } } }
This results in a response like this:
{ "took": 6, "timed_out": false, "_shards": { "total": 1, "successful": 1, "skipped" : 0, "failed": 0 }, "hits": { "total" : { "value": 1, "relation": "eq" }, "max_score": 0.13076457, "hits": [ { "_index": "test_index", "_id": "1", "_score": 0.13076457, "_source": { "query": { "match": { "body": { "query": "miss bicycl", "analyzer": "whitespace" } } } }, "fields" : { "_percolator_document_slot" : [0] } } ] } }
Optimizing wildcard queries.
editWildcard queries are more expensive than other queries for the percolator, especially if the wildcard expressions are large.
In the case of wildcard
queries with prefix wildcard expressions or just the prefix
query,
the edge_ngram
token filter can be used to replace these queries with regular term
query on a field where the edge_ngram
token filter is configured.
Creating an index with custom analysis settings:
response = client.indices.create( index: 'my_queries1', body: { settings: { analysis: { analyzer: { wildcard_prefix: { type: 'custom', tokenizer: 'standard', filter: [ 'lowercase', 'wildcard_edge_ngram' ] } }, filter: { wildcard_edge_ngram: { type: 'edge_ngram', min_gram: 1, max_gram: 32 } } } }, mappings: { properties: { query: { type: 'percolator' }, my_field: { type: 'text', fields: { prefix: { type: 'text', analyzer: 'wildcard_prefix', search_analyzer: 'standard' } } } } } } ) puts response
PUT my_queries1 { "settings": { "analysis": { "analyzer": { "wildcard_prefix": { "type": "custom", "tokenizer": "standard", "filter": [ "lowercase", "wildcard_edge_ngram" ] } }, "filter": { "wildcard_edge_ngram": { "type": "edge_ngram", "min_gram": 1, "max_gram": 32 } } } }, "mappings": { "properties": { "query": { "type": "percolator" }, "my_field": { "type": "text", "fields": { "prefix": { "type": "text", "analyzer": "wildcard_prefix", "search_analyzer": "standard" } } } } } }
The analyzer that generates the prefix tokens to be used at index time only. |
|
Increase the |
|
This multifield should be used to do the prefix search
with a |
Then instead of indexing the following query:
{ "query": { "wildcard": { "my_field": "abc*" } } }
this query below should be indexed:
response = client.index( index: 'my_queries1', id: 1, refresh: true, body: { query: { term: { "my_field.prefix": 'abc' } } } ) puts response
PUT /my_queries1/_doc/1?refresh { "query": { "term": { "my_field.prefix": "abc" } } }
This way can handle the second query more efficiently than the first query.
The following search request will match with the previously indexed percolator query:
response = client.search( index: 'my_queries1', body: { query: { percolate: { field: 'query', document: { my_field: 'abcd' } } } } ) puts response
GET /my_queries1/_search { "query": { "percolate": { "field": "query", "document": { "my_field": "abcd" } } } }
{ "took": 6, "timed_out": false, "_shards": { "total": 1, "successful": 1, "skipped": 0, "failed": 0 }, "hits": { "total" : { "value": 1, "relation": "eq" }, "max_score": 0.18864399, "hits": [ { "_index": "my_queries1", "_id": "1", "_score": 0.18864399, "_source": { "query": { "term": { "my_field.prefix": "abc" } } }, "fields": { "_percolator_document_slot": [ 0 ] } } ] } }
The same technique can also be used to speed up suffix
wildcard searches. By using the reverse
token filter
before the edge_ngram
token filter.
response = client.indices.create( index: 'my_queries2', body: { settings: { analysis: { analyzer: { wildcard_suffix: { type: 'custom', tokenizer: 'standard', filter: [ 'lowercase', 'reverse', 'wildcard_edge_ngram' ] }, wildcard_suffix_search_time: { type: 'custom', tokenizer: 'standard', filter: [ 'lowercase', 'reverse' ] } }, filter: { wildcard_edge_ngram: { type: 'edge_ngram', min_gram: 1, max_gram: 32 } } } }, mappings: { properties: { query: { type: 'percolator' }, my_field: { type: 'text', fields: { suffix: { type: 'text', analyzer: 'wildcard_suffix', search_analyzer: 'wildcard_suffix_search_time' } } } } } } ) puts response
PUT my_queries2 { "settings": { "analysis": { "analyzer": { "wildcard_suffix": { "type": "custom", "tokenizer": "standard", "filter": [ "lowercase", "reverse", "wildcard_edge_ngram" ] }, "wildcard_suffix_search_time": { "type": "custom", "tokenizer": "standard", "filter": [ "lowercase", "reverse" ] } }, "filter": { "wildcard_edge_ngram": { "type": "edge_ngram", "min_gram": 1, "max_gram": 32 } } } }, "mappings": { "properties": { "query": { "type": "percolator" }, "my_field": { "type": "text", "fields": { "suffix": { "type": "text", "analyzer": "wildcard_suffix", "search_analyzer": "wildcard_suffix_search_time" } } } } } }
A custom analyzer is needed at search time too, because otherwise the query terms are not being reversed and would otherwise not match with the reserved suffix tokens. |
Then instead of indexing the following query:
{ "query": { "wildcard": { "my_field": "*xyz" } } }
the following query below should be indexed:
response = client.index( index: 'my_queries2', id: 2, refresh: true, body: { query: { match: { "my_field.suffix": 'xyz' } } } ) puts response
The |
The following search request will match with the previously indexed percolator query:
response = client.search( index: 'my_queries2', body: { query: { percolate: { field: 'query', document: { my_field: 'wxyz' } } } } ) puts response
GET /my_queries2/_search { "query": { "percolate": { "field": "query", "document": { "my_field": "wxyz" } } } }
Dedicated Percolator Index
editPercolate queries can be added to any index. Instead of adding percolate queries to the index the data resides in, these queries can also be added to a dedicated index. The advantage of this is that this dedicated percolator index can have its own index settings (For example the number of primary and replica shards). If you choose to have a dedicated percolate index, you need to make sure that the mappings from the normal index are also available on the percolate index. Otherwise percolate queries can be parsed incorrectly.
Forcing Unmapped Fields to be Handled as Strings
editIn certain cases it is unknown what kind of percolator queries do get registered, and if no field mapping exists for fields
that are referred by percolator queries then adding a percolator query fails. This means the mapping needs to be updated
to have the field with the appropriate settings, and then the percolator query can be added. But sometimes it is sufficient
if all unmapped fields are handled as if these were default text fields. In those cases one can configure the
index.percolator.map_unmapped_fields_as_text
setting to true
(default to false
) and then if a field referred in
a percolator query does not exist, it will be handled as a default text field so that adding the percolator query doesn’t
fail.
Limitations
editParent/child
editBecause the percolate
query is processing one document at a time, it doesn’t support queries and filters that run
against child documents such as has_child
and has_parent
.
Fetching queries
editThere are a number of queries that fetch data via a get call during query parsing. For example the terms
query when
using terms lookup, template
query when using indexed scripts and geo_shape
when using pre-indexed shapes. When these
queries are indexed by the percolator
field type then the get call is executed once. So each time the percolator
query evaluates these queries, the fetches terms, shapes etc. as the were upon index time will be used. Important to note
is that fetching of terms that these queries do, happens both each time the percolator query gets indexed on both primary
and replica shards, so the terms that are actually indexed can be different between shard copies, if the source index
changed while indexing.
Script query
editThe script inside a script
query can only access doc values fields. The percolate
query indexes the provided document
into an in-memory index. This in-memory index doesn’t support stored fields and because of that the _source
field and
other stored fields are not stored. This is the reason why in the script
query the _source
and other stored fields
aren’t available.
Field aliases
editPercolator queries that contain field aliases may not always behave as expected. In particular, if a percolator query is registered that contains a field alias, and then that alias is updated in the mappings to refer to a different field, the stored query will still refer to the original target field. To pick up the change to the field alias, the percolator query must be explicitly reindexed.
On this page