What are the things we can define in the mappings of Elastic Search?

There are many different things that you can define in the mappings of an Elasticsearch index. Some of the most common things to define include:

• Field types: You can define the data type of each field in your index. This will help Elasticsearch to optimize the storage and retrieval of data. Some common field types include:
  • Text
  • Keyword
  • Long
  • Integer
  • Date
  • Float
  • Boolean
• Analyzers: You can define different analyzers for your text fields. This allows you to support multiple languages and different types of text analysis. Some common analyzers include:
  • Standard analyzer: This analyzer removes punctuation and stop words, and it splits text into individual tokens.
  • Keyword analyzer: This analyzer does not perform any analysis on the text. It simply stores the text as it is.
  • Date analyzer: This analyzer converts dates to a standard format, which makes it easier to search for and analyze dates.
• Validation rules: You can define validation rules for your fields. This can help to prevent invalid or incomplete data from being stored in your indices. For example, you can require that a field contain a minimum number of characters or that a field contain a valid email address.
• Boosting factors: You can boost the importance of individual fields in your search results. This can be useful for highlighting important fields or for ranking documents based on the relevance of their content.

In addition to these common things, you can also define many other things in the mappings of an Elasticsearch index. For more information, please see the Elasticsearch documentation: https://www.elastic.co/guide/en/elasticsearch/reference/current/mapping.html.

Here are some examples of how you can use mappings to improve the performance, storage efficiency, data quality, and searchability of your data:

• Performance: You can improve the performance of your searches by defining the data types of your fields and how they should be indexed. For example, if you have a field that contains dates, you can define it as a date field. This will allow Elasticsearch to use a specialized index for date fields, which can improve the performance of date-related searches.
• Storage efficiency: You can reduce the amount of storage space required by your indices by defining the data types of your fields. For example, if you have a field that contains integers, you can define it as an integer field. This will prevent Elasticsearch from storing unnecessary data, such as decimal places.
• Data quality: You can improve the quality of your data by using mappings to define validation rules for your fields. For example, you can require that a field contain a minimum number of characters or that a field contain a valid email address. This can help to prevent invalid or incomplete data from being stored in your indices.
• Searchability: You can make your data more searchable by using mappings to customize the way that your text fields are analyzed. For example, you can use a stemmer to remove prefixes and suffixes from words, or you can use a synonym filter to expand the search results to include synonyms of the search terms.

Overall, mappings are a powerful tool that can help you to improve the performance, storage efficiency, data quality, and searchability of your data in Elasticsearch.