- Elasticsearch Guide: other versions:
- Elasticsearch basics
- Quick starts
- Set up Elasticsearch
- Run Elasticsearch locally
- Installing Elasticsearch
- 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
- Data stream lifecycle settings
- Field data cache settings
- Local gateway settings
- Health Diagnostic settings
- Index lifecycle management settings
- Index management settings
- Index recovery settings
- Indexing buffer settings
- Inference settings
- License settings
- Machine learning settings
- Monitoring settings
- Node settings
- Networking
- Node query cache settings
- Path settings
- Search settings
- Security settings
- Shard request cache settings
- Snapshot and restore settings
- Transforms settings
- Thread pools
- Watcher settings
- Set JVM options
- 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
- Search your data
- The search API
- Sort search results
- Paginate search results
- Retrieve selected fields
- Search multiple data streams and indices using a query
- Collapse search results
- Filter search results
- Highlighting
- Long-running searches
- Near real-time search
- Retrieve inner hits
- Search shard routing
- Searching with query rules
- Search templates
- Full-text search
- Search relevance optimizations
- Retrievers
- kNN search
- Semantic search
- Retrieval augmented generation
- Search across clusters
- Search with synonyms
- Search Applications
- Search analytics
- The search API
- Re-ranking
- Index modules
- Index templates
- Aliases
- Mapping
- Dynamic mapping
- Explicit mapping
- Runtime fields
- Field data types
- Aggregate metric
- Alias
- Arrays
- Binary
- Boolean
- Completion
- Date
- Date nanoseconds
- Dense vector
- Flattened
- Geopoint
- Geoshape
- Histogram
- IP
- Join
- Keyword
- Nested
- Numeric
- Object
- Pass-through object
- Percolator
- Point
- Range
- Rank feature
- Rank features
- Rank Vectors
- Search-as-you-type
- Semantic text
- Shape
- Sparse vector
- Text
- Token count
- Unsigned long
- Version
- Metadata fields
- Mapping parameters
analyzer
coerce
copy_to
doc_values
dynamic
eager_global_ordinals
enabled
format
ignore_above
index.mapping.ignore_above
ignore_malformed
index
index_options
index_phrases
index_prefixes
meta
fields
normalizer
norms
null_value
position_increment_gap
properties
search_analyzer
similarity
store
subobjects
term_vector
- Mapping limit settings
- Removal of mapping types
- 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
- 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
- IP Location
- Join
- JSON
- KV
- Lowercase
- Network direction
- Pipeline
- Redact
- Registered domain
- Remove
- Rename
- Reroute
- Script
- Set
- Set security user
- Sort
- Split
- Terminate
- Trim
- Uppercase
- URL decode
- URI parts
- User agent
- Ingest pipelines in Search
- Connectors
- Data streams
- 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
- Roll up or transform your data
- Query DSL
- EQL
- ES|QL
- 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
- 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
- Watcher
- Monitor a cluster
- 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
- Set up a cluster for high availability
- Optimizations
- Autoscaling
- Snapshot and restore
- Cross-cluster replication
- Data store architecture
- 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
- Connector APIs
- Create connector
- Delete connector
- Get connector
- List connectors
- Update connector API key id
- Update connector configuration
- Update connector index name
- Update connector features
- Update connector filtering
- Update connector name and description
- Update connector pipeline
- Update connector scheduling
- Update connector service type
- Create connector sync job
- Cancel connector sync job
- Delete connector sync job
- Get connector sync job
- List connector sync jobs
- Check in a connector
- Update connector error
- Update connector last sync stats
- Update connector status
- Check in connector sync job
- Claim connector sync job
- Set connector sync job error
- Set connector sync job stats
- Data stream APIs
- Document APIs
- Enrich APIs
- EQL APIs
- ES|QL APIs
- Features APIs
- Fleet APIs
- 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
- Resolve cluster
- Advantages of using this endpoint before a cross-cluster search
- Rollover
- Shrink index
- Simulate index
- Simulate template
- Split 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
- Inference APIs
- Delete inference API
- Get inference API
- Perform inference API
- Chat completion inference API
- Create inference API
- Stream inference API
- Update inference API
- Elastic Inference Service (EIS)
- AlibabaCloud AI Search inference integration
- Amazon Bedrock inference integration
- Anthropic inference integration
- Azure AI studio inference integration
- Azure OpenAI inference integration
- Cohere inference integration
- Elasticsearch inference integration
- ELSER inference integration
- Google AI Studio inference integration
- Google Vertex AI inference integration
- HuggingFace inference integration
- JinaAI inference integration
- Mistral inference integration
- OpenAI inference integration
- Watsonx inference integration
- Info API
- Ingest APIs
- 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
- Root API
- 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
- Bulk create or update roles API
- Bulk delete roles API
- 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
- Query Role
- Get service accounts
- Get service account credentials
- Get Security settings
- 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
- Query User
- Update API key
- Update Security settings
- 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
- Text structure APIs
- Transform APIs
- Usage API
- Watcher APIs
- Definitions
- 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
- 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
- Troubleshooting broken repositories
- Addressing repeated snapshot policy failures
- Troubleshooting an unstable cluster
- Troubleshooting discovery
- Troubleshooting monitoring
- Troubleshooting transforms
- Troubleshooting Watcher
- Troubleshooting searches
- Troubleshooting shards capacity health issues
- Troubleshooting an unbalanced cluster
- Capture diagnostics
- Upgrade Elasticsearch
- Migration guide
- Release notes
- Dependencies and versions
Data processing with DISSECT and GROK
editData processing with DISSECT and GROK
editYour data may contain unstructured strings that you want to structure. This makes it easier to analyze the data. For example, log messages may contain IP addresses that you want to extract so you can find the most active IP addresses.
Elasticsearch can structure your data at index time or query time. At index time, you can
use the Dissect and Grok ingest
processors, or the Logstash Dissect and
Grok filters. At query time, you can
use the ES|QL DISSECT
and GROK
commands.
DISSECT
or GROK
? Or both?
editDISSECT
works by breaking up a string using a delimiter-based pattern. GROK
works similarly, but uses regular expressions. This makes GROK
more powerful,
but generally also slower. DISSECT
works well when data is reliably repeated.
GROK
is a better choice when you really need the power of regular expressions,
for example when the structure of your text varies from row to row.
You can use both DISSECT
and GROK
for hybrid use cases. For example when a
section of the line is reliably repeated, but the entire line is not. DISSECT
can deconstruct the section of the line that is repeated. GROK
can process the
remaining field values using regular expressions.
Process data with DISSECT
editThe DISSECT
processing command matches a string against a
delimiter-based pattern, and extracts the specified keys as columns.
For example, the following pattern:
%{clientip} [%{@timestamp}] %{status}
matches a log line of this format:
1.2.3.4 [2023-01-23T12:15:00.000Z] Connected
and results in adding the following columns to the input table:
clientip:keyword | @timestamp:keyword | status:keyword |
---|---|---|
1.2.3.4 |
2023-01-23T12:15:00.000Z |
Connected |
Dissect patterns
editA dissect pattern is defined by the parts of the string that will be discarded. In the previous example, the first part
to be discarded is a single space. Dissect finds this space, then assigns the value of clientip
everything up
until that space.
Next, dissect matches the [
and then ]
and then assigns @timestamp
to everything in-between [
and ]
.
Paying special attention to the parts of the string to discard will help build successful dissect patterns.
An empty key (%{}
) or named skip key can be used to
match values, but exclude the value from the output.
All matched values are output as keyword string data types. Use the Type conversion functions to convert to another data type.
Dissect also supports key modifiers that can change dissect’s default behavior. For example, you can instruct dissect to ignore certain fields, append fields, skip over padding, etc.
Terminology
edit- dissect pattern
-
the set of fields and delimiters describing the textual
format. Also known as a dissection.
The dissection is described using a set of
%{}
sections:%{a} - %{b} - %{c}
- field
-
the text from
%{
to}
inclusive. - delimiter
-
the text between
}
and the next%{
characters. Any set of characters other than%{
,'not }'
, or}
is a delimiter. - key
-
the text between the
%{
and}
, exclusive of the?
,+
,&
prefixes and the ordinal suffix.Examples:
-
%{?aaa}
- the key isaaa
-
%{+bbb/3}
- the key isbbb
-
%{&ccc}
- the key isccc
-
Examples
editThe following example parses a string that contains a timestamp, some text, and an IP address:
ROW a = "2023-01-23T12:15:00.000Z - some text - 127.0.0.1" | DISSECT a """%{date} - %{msg} - %{ip}""" | KEEP date, msg, ip
date:keyword | msg:keyword | ip:keyword |
---|---|---|
2023-01-23T12:15:00.000Z |
some text |
127.0.0.1 |
By default, DISSECT
outputs keyword string columns. To convert to another
type, use Type conversion functions:
ROW a = "2023-01-23T12:15:00.000Z - some text - 127.0.0.1" | DISSECT a """%{date} - %{msg} - %{ip}""" | KEEP date, msg, ip | EVAL date = TO_DATETIME(date)
msg:keyword | ip:keyword | date:date |
---|---|---|
some text |
127.0.0.1 |
2023-01-23T12:15:00.000Z |
Dissect key modifiers
editKey modifiers can change the default behavior for dissection. Key modifiers may be found on the left or right
of the %{keyname}
always inside the %{
and }
. For example %{+keyname ->}
has the append and right padding
modifiers.
Table 52. Dissect key modifiers
Modifier | Name | Position | Example | Description | Details |
---|---|---|---|---|---|
|
Skip right padding |
(far) right |
|
Skips any repeated characters to the right |
|
|
Append |
left |
|
Appends two or more fields together |
|
|
Append with order |
left and right |
|
Appends two or more fields together in the order specified |
|
|
Named skip key |
left |
|
Skips the matched value in the output. Same behavior as |
Right padding modifier (->
)
editThe algorithm that performs the dissection is very strict in that it requires all characters in the pattern to match
the source string. For example, the pattern %{fookey} %{barkey}
(1 space), will match the string "foo bar"
(1 space), but will not match the string "foo bar" (2 spaces) since the pattern has only 1 space and the
source string has 2 spaces.
The right padding modifier helps with this case. Adding the right padding modifier to the pattern %{fookey->} %{barkey}
,
It will now will match "foo bar" (1 space) and "foo bar" (2 spaces)
and even "foo bar" (10 spaces).
Use the right padding modifier to allow for repetition of the characters after a %{keyname->}
.
The right padding modifier may be placed on any key with any other modifiers. It should always be the furthest right
modifier. For example: %{+keyname/1->}
and %{->}
For example:
ROW message="1998-08-10T17:15:42 WARN" | DISSECT message """%{ts->} %{level}"""
message:keyword | ts:keyword | level:keyword |
---|---|---|
1998-08-10T17:15:42 WARN |
1998-08-10T17:15:42 |
WARN |
The right padding modifier may be used with an empty key to help skip unwanted data. For example, the same input string, but wrapped with brackets requires the use of an empty right padded key to achieve the same result.
For example:
ROW message="[1998-08-10T17:15:42] [WARN]" | DISSECT message """[%{ts}]%{->}[%{level}]"""
message:keyword | ts:keyword | level:keyword |
---|---|---|
["[1998-08-10T17:15:42] [WARN]"] |
1998-08-10T17:15:42 |
WARN |
Append modifier (+
)
editDissect supports appending two or more results together for the output. Values are appended left to right. An append separator can be specified. In this example the append_separator is defined as a space.
ROW message="john jacob jingleheimer schmidt" | DISSECT message """%{+name} %{+name} %{+name} %{+name}""" APPEND_SEPARATOR=" "
message:keyword | name:keyword |
---|---|
john jacob jingleheimer schmidt |
john jacob jingleheimer schmidt |
Append with order modifier (+
and /n
)
editDissect supports appending two or more results together for the output.
Values are appended based on the order defined (/n
). An append separator can be specified.
In this example the append_separator is defined as a comma.
ROW message="john jacob jingleheimer schmidt" | DISSECT message """%{+name/2} %{+name/4} %{+name/3} %{+name/1}""" APPEND_SEPARATOR=","
message:keyword | name:keyword |
---|---|
john jacob jingleheimer schmidt |
schmidt,john,jingleheimer,jacob |
Named skip key (?
)
editDissect supports ignoring matches in the final result. This can be done with an empty key %{}
, but for readability
it may be desired to give that empty key a name.
This can be done with a named skip key using the {?name}
syntax. In the
following query, ident
and auth
are not added to the output table:
ROW message="1.2.3.4 - - 30/Apr/1998:22:00:52 +0000" | DISSECT message """%{clientip} %{?ident} %{?auth} %{@timestamp}"""
message:keyword | clientip:keyword | @timestamp:keyword |
---|---|---|
1.2.3.4 - - 30/Apr/1998:22:00:52 +0000 |
1.2.3.4 |
30/Apr/1998:22:00:52 +0000 |
Limitations
editThe DISSECT
command does not support reference keys.
Process data with GROK
editThe GROK
processing command matches a string against a pattern based on
regular expressions, and extracts the specified keys as columns.
For example, the following pattern:
%{IP:ip} \[%{TIMESTAMP_ISO8601:@timestamp}\] %{GREEDYDATA:status}
matches a log line of this format:
1.2.3.4 [2023-01-23T12:15:00.000Z] Connected
Putting it together as an ES|QL query:
ROW a = "1.2.3.4 [2023-01-23T12:15:00.000Z] Connected" | GROK a """%{IP:ip} \[%{TIMESTAMP_ISO8601:@timestamp}\] %{GREEDYDATA:status}"""
GROK
adds the following columns to the input table:
@timestamp:keyword | ip:keyword | status:keyword |
---|---|---|
2023-01-23T12:15:00.000Z |
1.2.3.4 |
Connected |
Special regex characters in grok patterns, like [
and ]
need to be escaped
with a \
. For example, in the earlier pattern:
%{IP:ip} \[%{TIMESTAMP_ISO8601:@timestamp}\] %{GREEDYDATA:status}
In ES|QL queries, when using single quotes for strings, the backslash character itself is a special character that
needs to be escaped with another \
. For this example, the corresponding ES|QL
query becomes:
ROW a = "1.2.3.4 [2023-01-23T12:15:00.000Z] Connected" | GROK a "%{IP:ip} \\[%{TIMESTAMP_ISO8601:@timestamp}\\] %{GREEDYDATA:status}"
For this reason, in general it is more convenient to use triple quotes """
for GROK patterns,
that do not require escaping for backslash.
ROW a = "1.2.3.4 [2023-01-23T12:15:00.000Z] Connected" | GROK a """%{IP:ip} \[%{TIMESTAMP_ISO8601:@timestamp}\] %{GREEDYDATA:status}"""
Grok patterns
editThe syntax for a grok pattern is %{SYNTAX:SEMANTIC}
The SYNTAX
is the name of the pattern that matches your text. For example,
3.44
is matched by the NUMBER
pattern and 55.3.244.1
is matched by the
IP
pattern. The syntax is how you match.
The SEMANTIC
is the identifier you give to the piece of text being matched.
For example, 3.44
could be the duration of an event, so you could call it
simply duration
. Further, a string 55.3.244.1
might identify the client
making a request.
By default, matched values are output as keyword string data types. To convert a
semantic’s data type, suffix it with the target data type. For example
%{NUMBER:num:int}
, which converts the num
semantic from a string to an
integer. Currently the only supported conversions are int
and float
. For
other types, use the Type conversion functions.
For an overview of the available patterns, refer to GitHub. You can also retrieve a list of all patterns using a REST API.
Regular expressions
editGrok is based on regular expressions. Any regular expressions are valid in grok as well. Grok uses the Oniguruma regular expression library. Refer to the Oniguruma GitHub repository for the full supported regexp syntax.
Custom patterns
editIf grok doesn’t have a pattern you need, you can use the Oniguruma syntax for named capture which lets you match a piece of text and save it as a column:
(?<field_name>the pattern here)
For example, postfix logs have a queue id
that is a 10 or 11-character
hexadecimal value. This can be captured to a column named queue_id
with:
(?<queue_id>[0-9A-F]{10,11})
Examples
editThe following example parses a string that contains a timestamp, an IP address, an email address, and a number:
ROW a = "2023-01-23T12:15:00.000Z 127.0.0.1 some.email@foo.com 42" | GROK a """%{TIMESTAMP_ISO8601:date} %{IP:ip} %{EMAILADDRESS:email} %{NUMBER:num}""" | KEEP date, ip, email, num
date:keyword | ip:keyword | email:keyword | num:keyword |
---|---|---|---|
2023-01-23T12:15:00.000Z |
127.0.0.1 |
42 |
By default, GROK
outputs keyword string columns. int
and float
types can
be converted by appending :type
to the semantics in the pattern. For example
{NUMBER:num:int}
:
ROW a = "2023-01-23T12:15:00.000Z 127.0.0.1 some.email@foo.com 42" | GROK a """%{TIMESTAMP_ISO8601:date} %{IP:ip} %{EMAILADDRESS:email} %{NUMBER:num:int}""" | KEEP date, ip, email, num
date:keyword | ip:keyword | email:keyword | num:integer |
---|---|---|---|
2023-01-23T12:15:00.000Z |
127.0.0.1 |
42 |
For other type conversions, use Type conversion functions:
ROW a = "2023-01-23T12:15:00.000Z 127.0.0.1 some.email@foo.com 42" | GROK a """%{TIMESTAMP_ISO8601:date} %{IP:ip} %{EMAILADDRESS:email} %{NUMBER:num:int}""" | KEEP date, ip, email, num | EVAL date = TO_DATETIME(date)
ip:keyword | email:keyword | num:integer | date:date |
---|---|---|---|
127.0.0.1 |
42 |
2023-01-23T12:15:00.000Z |
If a field name is used more than once, GROK
creates a multi-valued
column:
FROM addresses | KEEP city.name, zip_code | GROK zip_code """%{WORD:zip_parts} %{WORD:zip_parts}"""
city.name:keyword | zip_code:keyword | zip_parts:keyword |
---|---|---|
Amsterdam |
1016 ED |
["1016", "ED"] |
San Francisco |
CA 94108 |
["CA", "94108"] |
Tokyo |
100-7014 |
null |
Grok debugger
editTo write and debug grok patterns, you can use the
Grok Debugger. It provides a UI for
testing patterns against sample data. Under the covers, it uses the same engine
as the GROK
command.
Limitations
editThe GROK
command does not support configuring custom
patterns, or multiple patterns. The GROK
command is not
subject to Grok watchdog settings.
On this page
DISSECT
orGROK
? Or both?- Process data with
DISSECT
- Dissect patterns
- Terminology
- Examples
- Dissect key modifiers
- Right padding modifier (
->
) - Append modifier (
+
) - Append with order modifier (
+
and/n
) - Named skip key (
?
) - Limitations
- Process data with
GROK
- Grok patterns
- Regular expressions
- Custom patterns
- Examples
- Grok debugger
- Limitations