Defining Custom Analyzers, Multi-Fields, and Mappings in Elasticsearch 7.13

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Defining Custom Analyzers, Multi-Fields, and Mappings in Elasticsearch 7.13

Elasticsearch has a plethora of built-in analyzers, as well as the ability to automatically detect the data type for each of your fields. If you want to fine-tune how your fields are analyzed and mapped, then Elasticsearch has you covered. In this hands-on lab, you will create and use custom analyzers and multi-fields, and define both explicit and dynamic mappings in Elasticsearch.

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Lab Info

Level

Advanced

Last updated

Sep 23, 2025

Duration

3h 0m

Challenge

Create the strings_as_keywords Component Template
From the Kibana console, create the strings_as_keywords component template with the following requirements:
- Dynamically maps string fields as the keyword datatype.
- Only indexes the first 256 characters.
Challenge

Create the social_media_analyzer Component Template
From the Kibana console, create the social_media_analyzer component template with the following requirements:
- Creates a custom analyzer called social_media that uses the classic tokenizer.
- Uses the lowercase filter and a custom english_stop filter. The english_stop filter should remove English stop words.
- Uses a custom emoticons mapping character filter with the following mappings:
  
  :) as happy
  
  :D as laughing
  
  :( as sad
  
  :') as crying
  
  :O as surprised
  
  ;) as winking
Challenge

Create the twitter_template Index Template
From the Kibana console, create the twitter_template index template with the following requirements:
- Matches all indices that start with "twitter-".
- Composed of the strings_as_keywords and social_media_analyzer component templates.
- Maps the tweet field as an analyzed string field with the social_media analyzer.
- Maps a tweet.keyword multi-field as type keyword and only indexes the first 280 characters.
- Sets the number of shards to 1 and replicas to 0.

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