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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

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Table of Contents
  1. 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.
  2. 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
  3. 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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