Understanding Index and Mapping in Elasticsearch

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Index in Elasticsearch

An Index in Elasticsearch is similar to a database in traditional database management systems (DBMS). It stores a collection of related documents. Each Index typically corresponds to a specific type of data in your application. For example, in an e-commerce application, you can create an Index to store information about products, another Index to store information about users and orders.

Each Index in Elasticsearch is divided into smaller shards for data distribution. A Shard is a small part of an Index, and each Shard can be stored on a separate node within an Elasticsearch cluster. Splitting data into shards optimizes search and query performance and enhances the scalability of the system.

For example, to create a new Index named products in Elasticsearch, you can use the API or management tools like Kibana to execute the following command:

PUT /products
{
  "settings": {
    "number_of_shards": 3,
    "number_of_replicas": 2
  }
}

In the example above, we have created an Index products with 3 shard and 2 replica of each shard to ensure availability and data backup.

Mapping in Elasticsearch

Mapping is the process of defining how Elasticsearch stores and processes data within an Index. When you add a new document to an Index, Elasticsearch uses Mapping to determine the data type of each field in the document. This helps Elasticsearch understand how to process and search data in different fields.

For example, if we have an Index products and want to define Mapping for the name (product name) and price (product price) fields as text and float types, respectively, we can execute the following command:

PUT /products/_mapping
{
  "properties": {
    "name": {
      "type": "text"
    },
    "price": {
      "type": "float"
    }
  }
}

In the example above, we have defined Mapping for the products Index, with the name field having the data type text and the price field having the data type float. This ensures that when Elasticsearch receives new documents for the products Index, it will store and process the name and "price" fields according to the defined data types.

Index and Mapping play crucial roles in organizing and managing data in Elasticsearch. They help Elasticsearch efficiently understand and process data, optimize search and query operations, and provide flexible scalability capabilities for the system.