- Elasticsearch Guide: other versions:
- Getting Started
- Set up Elasticsearch
- Installing Elasticsearch
- Configuring Elasticsearch
- Important Elasticsearch configuration
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- Extended Stats Bucket Aggregation
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- Bucket Script Aggregation
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- Serial Differencing Aggregation
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- Caching heavy aggregations
- Returning only aggregation results
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- Metrics Aggregations
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- cat APIs
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- Analysis
- Anatomy of an analyzer
- Testing analyzers
- Analyzers
- Normalizers
- Tokenizers
- 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
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- 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
- Classic Token Filter
- Apostrophe Token Filter
- Decimal Digit Token Filter
- Fingerprint Token Filter
- Minhash Token Filter
- Character Filters
- Modules
- Index Modules
- Ingest Node
- Pipeline Definition
- Ingest APIs
- Accessing Data in Pipelines
- Handling Failures in Pipelines
- Processors
- Append Processor
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- Fail Processor
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- Monitoring Elasticsearch
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- Close Jobs
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- Security APIs
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- Deprecation Info APIs
- Definitions
- X-Pack Commands
- How To
- Testing
- Glossary of terms
- Release Notes
- 6.1.4 Release Notes
- 6.1.3 Release Notes
- 6.1.2 Release Notes
- 6.1.1 Release Notes
- 6.1.0 Release Notes
- 6.0.1 Release Notes
- 6.0.0 Release Notes
- 6.0.0-rc2 Release Notes
- 6.0.0-rc1 Release Notes
- 6.0.0-beta2 Release Notes
- 6.0.0-beta1 Release Notes
- 6.0.0-alpha2 Release Notes
- 6.0.0-alpha1 Release Notes
- 6.0.0-alpha1 Release Notes (Changes previously released in 5.x)
- X-Pack Release Notes
WARNING: Version 6.1 of Elasticsearch has passed its EOL date.
This documentation is no longer being maintained and may be removed. If you are running this version, we strongly advise you to upgrade. For the latest information, see the current release documentation.
Anatomy of an analyzer
editAnatomy of an analyzer
editAn analyzer — whether built-in or custom — is just a package which contains three lower-level building blocks: character filters, tokenizers, and token filters.
The built-in analyzers pre-package these building
blocks into analyzers suitable for different languages and types of text.
Elasticsearch also exposes the individual building blocks so that they can be
combined to define new custom
analyzers.
Character filters
editA character filter receives the original text as a stream of characters and
can transform the stream by adding, removing, or changing characters. For
instance, a character filter could be used to convert Hindu-Arabic numerals
(٠١٢٣٤٥٦٧٨٩) into their Arabic-Latin equivalents (0123456789), or to strip HTML
elements like <b>
from the stream.
An analyzer may have zero or more character filters, which are applied in order.
Tokenizer
editA tokenizer receives a stream of characters, breaks it up into individual
tokens (usually individual words), and outputs a stream of tokens. For
instance, a whitespace
tokenizer breaks
text into tokens whenever it sees any whitespace. It would convert the text
"Quick brown fox!"
into the terms [Quick, brown, fox!]
.
The tokenizer is also responsible for recording the order or position of each term and the start and end character offsets of the original word which the term represents.
An analyzer must have exactly one tokenizer.
Token filters
editA token filter receives the token stream and may add, remove, or change
tokens. For example, a lowercase
token
filter converts all tokens to lowercase, a
stop
token filter removes common words
(stop words) like the
from the token stream, and a
synonym
token filter introduces synonyms
into the token stream.
Token filters are not allowed to change the position or character offsets of each token.
An analyzer may have zero or more token filters, which are applied in order.
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