- Elasticsearch Guide: other versions:
- Getting Started
- 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
- Starting Elasticsearch
- Stopping Elasticsearch
- Adding nodes to your cluster
- Installing X-Pack
- Set up X-Pack
- Configuring X-Pack Java Clients
- X-Pack Settings
- 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
- Bucket Aggregations
- Adjacency Matrix Aggregation
- Auto-interval Date Histogram Aggregation
- Intervals
- Children Aggregation
- Composite Aggregation
- Date Histogram Aggregation
- Date Range Aggregation
- Diversified Sampler Aggregation
- Filter Aggregation
- Filters Aggregation
- Geo Distance Aggregation
- GeoHash grid Aggregation
- Global Aggregation
- Histogram Aggregation
- IP Range Aggregation
- Missing Aggregation
- Nested Aggregation
- Range Aggregation
- Reverse nested Aggregation
- Sampler Aggregation
- Significant Terms Aggregation
- 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
- 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
- Standard Token Filter
- 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
- 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
- Drop Processor
- Dot Expander Processor
- Fail Processor
- Foreach 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
- SQL Access
- Monitor a cluster
- Rolling up historical data
- Set up a cluster for high availability
- 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 settings
- Security files
- Auditing settings
- How security works
- User authentication
- Built-in users
- Internal users
- 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, tribe, clients, and integrations
- Tutorial: Getting started with security
- Tutorial: Encrypting communications
- Troubleshooting
- Can’t log in after upgrading to 6.5.4
- 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
- X-Pack APIs
- Info API
- Cross-cluster replication APIs
- Explore 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
- 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
- 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 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 application privileges
- Get role mappings
- Get roles
- Get token
- Get users
- Has privileges
- Invalidate token
- SSL certificate
- Watcher APIs
- Definitions
- Release Highlights
- Breaking changes
- Release Notes
- Elasticsearch version 6.5.4
- Elasticsearch version 6.5.3
- Elasticsearch version 6.5.2
- Elasticsearch version 6.5.1
- Elasticsearch version 6.5.0
- Elasticsearch version 6.4.3
- Elasticsearch version 6.4.2
- Elasticsearch version 6.4.1
- Elasticsearch version 6.4.0
- Elasticsearch version 6.3.2
- Elasticsearch version 6.3.1
- Elasticsearch version 6.3.0
- Elasticsearch version 6.2.4
- Elasticsearch version 6.2.3
- Elasticsearch version 6.2.2
- Elasticsearch version 6.2.1
- Elasticsearch version 6.2.0
- Elasticsearch version 6.1.4
- Elasticsearch version 6.1.3
- Elasticsearch version 6.1.2
- Elasticsearch version 6.1.1
- Elasticsearch version 6.1.0
- Elasticsearch version 6.0.1
- Elasticsearch version 6.0.0
- Elasticsearch version 6.0.0-rc2
- Elasticsearch version 6.0.0-rc1
- Elasticsearch version 6.0.0-beta2
- Elasticsearch version 6.0.0-beta1
- Elasticsearch version 6.0.0-alpha2
- Elasticsearch version 6.0.0-alpha1
- Elasticsearch version 6.0.0-alpha1 (Changes previously released in 5.x)
Dissect Processor
editDissect Processor
editSimilar to the Grok Processor, dissect also extracts structured fields out of a single text field within a document. However unlike the Grok Processor, dissect does not use Regular Expressions. This allows dissect’s syntax to be simple and for some cases faster than the Grok Processor.
Dissect matches a single text field against a defined pattern.
For example the following pattern:
%{clientip} %{ident} %{auth} [%{@timestamp}] \"%{verb} %{request} HTTP/%{httpversion}\" %{status} %{size}
will match a log line of this format:
1.2.3.4 - - [30/Apr/1998:22:00:52 +0000] \"GET /english/venues/cities/images/montpellier/18.gif HTTP/1.0\" 200 3171
and result in a document with the following fields:
"doc": { "_index": "_index", "_type": "_type", "_id": "_id", "_source": { "request": "/english/venues/cities/images/montpellier/18.gif", "auth": "-", "ident": "-", "verb": "GET", "@timestamp": "30/Apr/1998:22:00:52 +0000", "size": "3171", "clientip": "1.2.3.4", "httpversion": "1.0", "status": "200" } }
A dissect pattern is defined by the parts of the string that will be discarded. In the example above the first part
to be discarded is a single space. Dissect finds this space, then assigns the value of clientip
is everything up
until that space.
Later dissect matches the [
and then ]
and then assigns @timestamp
to everything in-between [
and ]
.
Paying special attention the parts of the string to discard will help build successful dissect patterns.
Successful matches require all keys in a pattern to have a value. If any of the %{keyname}
defined in the pattern do
not have a value, then an exception is thrown and may be handled by the on_falure directive.
An empty key %{}
or a named skip key can be used to match values, but exclude the value from
the final document. All matched values are represented as string data types. The convert processor
may be used to convert to expected 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. See below for more information.
Table 33. Dissect Options
Name | Required | Default | Description |
---|---|---|---|
|
yes |
- |
The field to dissect |
|
yes |
- |
The pattern to apply to the field |
|
no |
"" (empty string) |
The character(s) that separate the appended fields. |
|
no |
false |
If |
|
no |
- |
Conditionally execute this processor. |
|
no |
- |
Handle failures for this processor. See Handling Failures in Pipelines. |
|
no |
|
Ignore failures for this processor. See Handling Failures in Pipelines. |
|
no |
- |
An identifier for this processor. Useful for debugging and metrics. |
{ "dissect": { "field": "message", "pattern" : "%{clientip} %{ident} %{auth} [%{@timestamp}] \"%{verb} %{request} HTTP/%{httpversion}\" %{status} %{size}" } }
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 34. 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 |
|
|
Reference keys |
left |
|
Sets the output key as value of |
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 %{->}
Right padding modifier example
Pattern |
|
Input |
1998-08-10T17:15:42,466 WARN |
Result |
|
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.
Right padding modifier with empty key example
Pattern |
|
Input |
[1998-08-10T17:15:42,466] [WARN] |
Result |
|
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.
Append modifier example
Pattern |
|
Input |
john jacob jingleheimer schmidt |
Result |
|
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.
Append with order modifier example
Pattern |
|
Input |
john jacob jingleheimer schmidt |
Result |
|
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.
Named skip key modifier example
Pattern |
|
Input |
1.2.3.4 - - [30/Apr/1998:22:00:52 +0000] |
Result |
|
Reference keys (*
and &
)
editDissect support using parsed values as the key/value pairings for the structured content. Imagine a system that partially logs in key/value pairs. Reference keys allow you to maintain that key/value relationship.
Reference key modifier example
Pattern |
|
Input |
[2018-08-10T17:15:42,466] [ERR] ip:1.2.3.4 error:REFUSED |
Result |
|
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