- 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
- 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
- Monitor a cluster
- Rolling up historical data
- Frozen indices
- Set up a cluster for high availability
- X-Pack APIs
- Info API
- Cross-cluster replication APIs
- Explore API
- Freeze index
- Index lifecycle management API
- Licensing APIs
- Migration APIs
- Machine learning APIs
- Add events to calendar
- Add jobs to calendar
- Close jobs
- Create calendar
- Create datafeeds
- Create filter
- Create jobs
- 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
- 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
- SSL certificate
- 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
- FIPS 140-2
- Security files
- 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
- 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
- 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
Lexical Structure
editLexical Structure
editThis section covers the major lexical structure of SQL, which for the most part, is going to resemble that of ANSI SQL itself hence why low-levels details are not discussed in depth.
Elasticsearch SQL currently accepts only one command at a time. A command is a sequence of tokens terminated by the end of input stream.
A token can be a key word, an identifier (quoted or unquoted), a literal (or constant) or a special character symbol (typically a delimiter). Tokens are typically separated by whitespace (be it space, tab) though in some cases, where there is no ambiguity (typically due to a character symbol) this is not needed - however for readability purposes this should be avoided.
Key Words
editTake the following example:
SELECT * FROM table
This query has four tokens: SELECT
, *
, FROM
and table
. The first three, namely SELECT
, *
and FROM
are key words meaning words that have a fixed meaning in SQL. The token table
is an identifier meaning it identifies (by name) an entity inside SQL such as a table (in this case), a column, etc…
As one can see, both key words and identifiers have the same lexical structure and thus one cannot know whether a token is one or the other without knowing the SQL language; the complete list of key words is available in the reserved appendix. Do note that key words are case-insensitive meaning the previous example can be written as:
select * fRoM table;
Identifiers however are not - as Elasticsearch is case sensitive, Elasticsearch SQL uses the received value verbatim.
To help differentiate between the two, through-out the documentation the SQL key words are upper-cased a convention we find increases readability and thus recommend to others.
Identifiers
editIdentifiers can be of two types: quoted and unquoted:
SELECT ip_address FROM "hosts-*"
This query has two identifiers, ip_address
and hosts-*
(an index pattern). As ip_address
does not clash with any key words it can be used verbatim, hosts-*
on the other hand cannot as it clashes with -
(minus operation) and *
hence the double quotes.
Another example:
SELECT "from" FROM "<logstash-{now/d}>"
The first identifier from needs to quoted as otherwise it clashes with the FROM
key word (which is case insensitive as thus can be written as from
) while the second identifier using Elasticsearch Date math support in index names would have otherwise confuse the parser.
Hence why in general, especially when dealing with user input it is highly recommended to use quotes for identifiers. It adds minimal increase to your queries and in return offers clarity and disambiguation.
Literals (Constants)
editElasticsearch SQL supports two kind of implicitly-typed literals: strings and numbers.
String Literals
editA string literal is an arbitrary number of characters bounded by single quotes '
: 'Giant Robot'
.
To include a single quote in the string, escape it using another single quote: 'Captain EO''s Voyage'
.
An escaped single quote is not a double quote ("
), but a single quote '
repeated (''
).
Numeric Literals
editNumeric literals are accepted both in decimal and scientific notation with exponent marker (e
or E
), starting either with a digit or decimal point .
:
1969 -- integer notation 3.14 -- decimal notation .1234 -- decimal notation starting with decimal point 4E5 -- scientific notation (with exponent marker) 1.2e-3 -- scientific notation with decimal point
Numeric literals that contain a decimal point are always interpreted as being of type double
. Those without are considered integer
if they fit otherwise their type is long
(or BIGINT
in ANSI SQL types).
Generic Literals
editWhen dealing with arbitrary type literal, one creates the object by casting, typically, the string representation to the desired type. This can be achieved through the dedicated functions:
CAST('1969-05-13T12:34:56' AS TIMESTAMP) -- cast the given string to datetime CONVERT('10.0.0.1', IP) -- cast '10.0.0.1' to an IP
Do note that Elasticsearch SQL provides functions that out of the box return popular literals (like E()
) or provide dedicated parsing for certain strings.
Single vs Double Quotes
editIt is worth pointing out that in SQL, single quotes '
and double quotes "
have different meaning and cannot be used interchangeably.
Single quotes are used to declare a string literal while double quotes for identifiers.
To wit:
Special characters
editA few characters that are not alphanumeric have a dedicated meaning different from that of an operator. For completeness these are specified below:
Char |
Description |
|
The asterisk (or wildcard) is used in some contexts to denote all fields for a table. Can be also used as an argument to some aggregate functions. |
|
Commas are used to enumerate the elements of a list. |
|
Used in numeric constants or to separate identifiers qualifiers (catalog, table, column names, etc…). |
|
Parentheses are used for specific SQL commands, function declarations or to enforce precedence. |
Operators
editMost operators in Elasticsearch SQL have the same precedence and are left-associative. As this is done at parsing time, parenthesis need to be used to enforce a different precedence.
The following table indicates the supported operators and their precendence (highest to lowest);
Operator/Element |
Associativity |
Description |
|
left |
qualifier separator |
|
right |
unary plus and minus (numeric literal sign) |
|
left |
multiplication, division, modulo |
|
left |
addition, substraction |
|
range containment, string matching |
|
|
comparison |
|
|
right |
logical negation |
|
left |
logical conjunction |
|
left |
logical disjunction |
Comments
editElasticsearch SQL allows comments which are sequence of characters ignored by the parsers.
Two styles are supported:
- Single Line
-
Comments start with a double dash
--
and continue until the end of the line. - Multi line
-
Comments that start with
/*
and end with*/
(also known as C-style).
-- single line comment /* multi line comment that supports /* nested comments */ */
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