- Elasticsearch Guide: other versions:
- Elasticsearch introduction
- Getting started with Elasticsearch
- Set up Elasticsearch
- Installing Elasticsearch
- Configuring Elasticsearch
- Important Elasticsearch 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
- G1GC check
- All permission check
- Discovery configuration check
- Starting Elasticsearch
- Stopping Elasticsearch
- Adding nodes to your cluster
- Set up X-Pack
- Configuring X-Pack Java Clients
- Bootstrap Checks for X-Pack
- Upgrade Elasticsearch
- API conventions
- Document APIs
- Search APIs
- Aggregations
- Metrics Aggregations
- Avg Aggregation
- Weighted 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
- Median Absolute Deviation Aggregation
- Bucket Aggregations
- Adjacency Matrix Aggregation
- Auto-interval Date Histogram Aggregation
- Children Aggregation
- Composite Aggregation
- Date Histogram Aggregation
- Date Range Aggregation
- Diversified Sampler Aggregation
- Filter Aggregation
- Filters Aggregation
- Geo Distance Aggregation
- GeoHash grid Aggregation
- GeoTile Grid Aggregation
- Global Aggregation
- Histogram Aggregation
- IP Range Aggregation
- Missing Aggregation
- Nested Aggregation
- Parent Aggregation
- Range Aggregation
- Reverse nested Aggregation
- Sampler Aggregation
- Significant Terms Aggregation
- Significant Text 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
- Moving Function Aggregation
- Cumulative Sum Aggregation
- Bucket Script Aggregation
- Bucket Selector Aggregation
- Bucket Sort Aggregation
- Serial Differencing Aggregation
- Matrix Aggregations
- Caching heavy aggregations
- Returning only aggregation results
- Aggregation Metadata
- Returning the type of the aggregation
- Metrics Aggregations
- Indices APIs
- Create Index
- Delete Index
- Get Index
- Indices Exists
- Open / Close Index API
- Shrink Index
- Split Index
- Rollover Index
- Put Mapping
- Get Mapping
- Get Field Mapping
- Types Exists
- Index Aliases
- Update Indices Settings
- Get Settings
- Analyze
- Index Templates
- Indices Stats
- Indices Segments
- Indices Recovery
- Indices Shard Stores
- Clear Cache
- Flush
- Refresh
- Force Merge
- cat APIs
- Cluster APIs
- Query DSL
- Scripting
- Mapping
- Analysis
- Anatomy of an analyzer
- Testing analyzers
- Analyzers
- Normalizers
- Tokenizers
- Standard Tokenizer
- Letter Tokenizer
- Lowercase Tokenizer
- Whitespace Tokenizer
- UAX URL Email Tokenizer
- Classic Tokenizer
- Thai Tokenizer
- NGram Tokenizer
- Edge NGram Tokenizer
- Keyword Tokenizer
- Pattern Tokenizer
- Char Group Tokenizer
- Simple Pattern Tokenizer
- Simple Pattern Split Tokenizer
- Path Hierarchy Tokenizer
- Path Hierarchy Tokenizer Examples
- Token Filters
- ASCII Folding Token Filter
- Flatten Graph 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
- Word Delimiter Graph Token Filter
- Multiplexer Token Filter
- Conditional Token Filter
- Predicate Token Filter Script
- 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
- Parsing synonym files
- Synonym Graph Token Filter
- Compound Word Token Filters
- 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
- Exclude mode settings example
- Classic Token Filter
- Apostrophe Token Filter
- Decimal Digit Token Filter
- Fingerprint Token Filter
- MinHash Token Filter
- Remove Duplicates Token Filter
- Character Filters
- Modules
- Index modules
- Ingest node
- Pipeline Definition
- Ingest APIs
- Accessing Data in Pipelines
- Conditional Execution in Pipelines
- Handling Failures in Pipelines
- Processors
- Append Processor
- Bytes Processor
- Convert Processor
- Date Processor
- Date Index Name Processor
- Dissect Processor
- Dot Expander Processor
- Drop Processor
- Fail Processor
- Foreach Processor
- GeoIP Processor
- Grok Processor
- Gsub Processor
- HTML Strip Processor
- Join Processor
- JSON Processor
- KV Processor
- Lowercase Processor
- Pipeline Processor
- Remove Processor
- Rename Processor
- Script Processor
- Set Processor
- Set Security User Processor
- Split Processor
- Sort Processor
- Trim Processor
- Uppercase Processor
- URL Decode Processor
- User Agent processor
- Managing the index lifecycle
- Getting started with index lifecycle management
- Policy phases and actions
- Set up index lifecycle management policy
- Using policies to manage index rollover
- Update policy
- Index lifecycle error handling
- Restoring snapshots of managed indices
- Start and stop index lifecycle management
- Using ILM with existing indices
- SQL access
- 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
- Monitor a cluster
- Frozen indices
- Set up a cluster for high availability
- Roll up or transform your data
- X-Pack APIs
- Info API
- Cross-cluster replication APIs
- Explore API
- Freeze index
- Index lifecycle management API
- Licensing APIs
- Machine learning APIs
- Add events to calendar
- Add jobs to calendar
- Close jobs
- Create jobs
- Create calendar
- Create datafeeds
- Create filter
- Delete calendar
- Delete datafeeds
- Delete events from calendar
- Delete filter
- Delete forecast
- Delete jobs
- Delete jobs from calendar
- Delete model snapshots
- Delete expired data
- Find file structure
- Flush jobs
- Forecast jobs
- Get calendars
- Get buckets
- Get overall buckets
- Get categories
- Get datafeeds
- Get datafeed statistics
- Get influencers
- Get jobs
- Get job statistics
- Get machine learning info
- Get model snapshots
- Get scheduled events
- Get filters
- Get records
- Open jobs
- Post data to jobs
- Preview datafeeds
- Revert model snapshots
- Set upgrade mode
- Start datafeeds
- Stop datafeeds
- Update datafeeds
- Update filter
- Update jobs
- Update model snapshots
- Migration APIs
- Rollup APIs
- Security APIs
- Authenticate
- Change passwords
- Clear cache
- Clear roles cache
- Create API keys
- Create or update application privileges
- Create or update role mappings
- Create or update roles
- Create or update users
- Delete application privileges
- Delete role mappings
- Delete roles
- Delete users
- Disable users
- Enable users
- Get API key information
- Get application privileges
- Get role mappings
- Get roles
- Get token
- Get users
- Has privileges
- Invalidate API key
- Invalidate token
- OpenID Connect Prepare Authentication API
- OpenID Connect Authenticate API
- OpenID Connect Logout API
- SSL certificate
- Transform APIs
- Unfreeze index
- Watcher APIs
- Definitions
- Secure a cluster
- Overview
- Configuring security
- Encrypting communications in Elasticsearch
- Encrypting communications in an Elasticsearch Docker Container
- Enabling cipher suites for stronger encryption
- Separating node-to-node and client traffic
- Configuring an Active Directory realm
- Configuring a file realm
- Configuring an LDAP realm
- Configuring a native realm
- Configuring a PKI realm
- Configuring a SAML realm
- Configuring a Kerberos realm
- Security files
- FIPS 140-2
- How security works
- User authentication
- Built-in users
- Internal users
- Token-based authentication services
- Realms
- Realm chains
- Active Directory user authentication
- File-based user authentication
- LDAP user authentication
- Native user authentication
- OpenID Connect authentication
- PKI user authentication
- SAML authentication
- Kerberos authentication
- Integrating with other authentication systems
- Enabling anonymous access
- 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
- Auditing security events
- Encrypting communications
- Restricting connections with IP filtering
- Cross cluster search, clients, and integrations
- Tutorial: Getting started with security
- Tutorial: Encrypting communications
- 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
- Alerting on cluster and index events
- Command line tools
- How To
- Testing
- Glossary of terms
- Release highlights
- Breaking changes
- Release notes
- Elasticsearch version 7.2.1
- Elasticsearch version 7.2.0
- Elasticsearch version 7.1.1
- Elasticsearch version 7.1.0
- Elasticsearch version 7.0.0
- Elasticsearch version 7.0.0-rc2
- Elasticsearch version 7.0.0-rc1
- Elasticsearch version 7.0.0-beta1
- Elasticsearch version 7.0.0-alpha2
- Elasticsearch version 7.0.0-alpha1
SQL Limitations
editSQL Limitations
editLarge queries may throw ParsingExpection
editExtremely large queries can consume too much memory during the parsing phase, in which case the Elasticsearch SQL engine will abort parsing and throw an error. In such cases, consider reducing the query to a smaller size by potentially simplifying it or splitting it into smaller queries.
Nested fields in SYS COLUMNS
and DESCRIBE TABLE
editElasticsearch has a special type of relationship fields called nested
fields. In Elasticsearch SQL they can be used by referencing their inner
sub-fields. Even though SYS COLUMNS
in non-driver mode (in the CLI and in REST calls) and DESCRIBE TABLE
will still display
them as having the type NESTED
, they cannot be used in a query. One can only reference its sub-fields in the form:
[nested_field_name].[sub_field_name]
For example:
SELECT dep.dep_name.keyword FROM test_emp GROUP BY languages;
Multi-nested fields
editElasticsearch SQL doesn’t support multi-nested documents, so a query cannot reference more than one nested field in an index. This applies to multi-level nested fields, but also multiple nested fields defined on the same level. For example, for this index:
column | type | mapping ----------------------+---------------+------------- nested_A |STRUCT |NESTED nested_A.nested_X |STRUCT |NESTED nested_A.nested_X.text|VARCHAR |KEYWORD nested_A.text |VARCHAR |KEYWORD nested_B |STRUCT |NESTED nested_B.text |VARCHAR |KEYWORD
nested_A
and nested_B
cannot be used at the same time, nor nested_A
/nested_B
and nested_A.nested_X
combination.
For such situations, Elasticsearch SQL will display an error message.
Paginating nested inner hits
editWhen SELECTing a nested field, pagination will not work as expected, Elasticsearch SQL will return at least the page size records. This is because of the way nested queries work in Elasticsearch: the root nested field will be returned and it’s matching inner nested fields as well, pagination taking place on the root nested document and not on its inner hits.
Normalized keyword
fields
editkeyword
fields in Elasticsearch can be normalized by defining a normalizer
. Such fields are not supported in Elasticsearch SQL.
Array type of fields
editArray fields are not supported due to the "invisible" way in which Elasticsearch handles an array of values: the mapping doesn’t indicate whether
a field is an array (has multiple values) or not, so without reading all the data, Elasticsearch SQL cannot know whether a field is a single or multi value.
When multiple values are returned for a field, by default, Elasticsearch SQL will throw an exception. However, it is possible to change this behavior through field_multi_value_leniency
parameter in REST (disabled by default) or
field.multi.value.leniency
in drivers (enabled by default).
Sorting by aggregation
editWhen doing aggregations (GROUP BY
) Elasticsearch SQL relies on Elasticsearch’s composite
aggregation for its support for paginating results.
However this type of aggregation does come with a limitation: sorting can only be applied on the key used for the aggregation’s buckets.
Elasticsearch SQL overcomes this limitation by doing client-side sorting however as a safety measure, allows only up to 512 rows.
It is recommended to use LIMIT
for queries that use sorting by aggregation, essentially indicating the top N results that are desired:
SELECT * FROM test GROUP BY age ORDER BY COUNT(*) LIMIT 100;
It is possible to run the same queries without a LIMIT
however in that case if the maximum size (10000) is passed,
an exception will be returned as Elasticsearch SQL is unable to track (and sort) all the results returned.
Using aggregation functions on top of scalar functions
editAggregation functions like MIN
, MAX
, etc. can only be used
directly on fields, and so queries like SELECT MAX(abs(age)) FROM test
are not possible.
Using a sub-select
editUsing sub-selects (SELECT X FROM (SELECT Y)
) is supported to a small degree: any sub-select that can be "flattened" into a single
SELECT
is possible with Elasticsearch SQL. For example:
SELECT * FROM (SELECT first_name, last_name FROM emp WHERE last_name NOT LIKE '%a%') WHERE first_name LIKE 'A%' ORDER BY 1; first_name | last_name ---------------+--------------- Alejandro |McAlpine Anneke |Preusig Anoosh |Peyn Arumugam |Ossenbruggen
The query above is possible because it is equivalent with:
SELECT first_name, last_name FROM emp WHERE last_name NOT LIKE '%a%' AND first_name LIKE 'A%' ORDER BY 1;
But, if the sub-select would include a GROUP BY
or HAVING
or the enclosing SELECT
would be more complex than SELECT X
FROM (SELECT ...) WHERE [simple_condition]
, this is currently un-supported.
Using FIRST
and LAST
in the HAVING
clause is not supported. The same applies to
MIN
and MAX
when their target column
is of type keyword
as they are internally translated to FIRST
and LAST
.
Using TIME
data type as a grouping key is currently not supported. For example:
SELECT count(*) FROM test GROUP BY CAST(date_created AS TIME);
On the other hand, it can still be used if it’s wrapped with a scalar function that returns another data type, for example:
SELECT count(*) FROM test GROUP BY MINUTE((CAST(date_created AS TIME));
TIME
data type is also currently not supported in histogram grouping function. For example:
SELECT HISTOGRAM(CAST(birth_date AS TIME), INTERVAL '10' MINUTES) as h, COUNT(*) FROM t GROUP BY h
Geo-related functions
editSince geo_shape
fields don’t have doc values these fields cannot be used for filtering, grouping or sorting.
By default,geo_points
fields are indexed and have doc values. However only latitude and longitude are stored and
indexed with some loss of precision from the original values (4.190951585769653E-8 for the latitude and
8.381903171539307E-8 for longitude). The altitude component is accepted but not stored in doc values nor indexed.
Therefore calling ST_Z
function in the filtering, grouping or sorting will return null
.
On this page
- Large queries may throw
ParsingExpection
- Nested fields in
SYS COLUMNS
andDESCRIBE TABLE
- Multi-nested fields
- Paginating nested inner hits
- Normalized
keyword
fields - Array type of fields
- Sorting by aggregation
- Using aggregation functions on top of scalar functions
- Using a sub-select
- Using / aggregation functions in
HAVING
clause - Using TIME data type in GROUP BY or
- Geo-related functions