How to Structure an ElasticSearch Index for Multiple Entity Bindings in an E-commerce Application?

Setting Up ElasticSearch Index Structure with Multiple Entity Bindings

Problem:

Implementing ElasticSearch (ES) for an existing e-commerce application with a MySQL database, how should the index structure be set up to represent a complex database schema involving multiple entity bindings?

Answer:

Denormalization Approach:

To handle multiple entity bindings, a denormalized approach is recommended. Flatten the data structure by creating a one-to-one mapping between documents and entities. In this case, create a single document type called "product" that includes all the necessary fields from the "Products," "Flags," and "flagsProducts" tables.

Product Document Structure:

{
   "id": "00c8234d71c4e94f725cd432ebc04",
   "title": "Alpha",
   "price": 589.0,
   "flags": ["Sellout", "Top Product"]
}

ElasticSearch Mapping:

The mapping type for the "product" index would resemble:

PUT products
{
    "mappings": {
        "product": {
            "properties": {
                "id": {
                    "type": "string",
                    "index": "not_analyzed"
                },
                "title": {
                    "type": "string"
                },
                "price": {
                    "type": "double",
                    "null_value": 0.0
                },
                "flags": {
                    "type": "string",
                    "index": "not_analyzed"
                }
            }
        }
    }
}

SQL Query for Data Extraction:

To extract the necessary data from the database, use the following SQL query:

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