How to Optimize ElasticSearch Index Structure with Multiple Entity Bindings?

How to Configure ElasticSearch Index Structure with Multiple Entity Bindings

Background

Integrating ElasticSearch (ES) with legacy applications often poses challenges due to differences in data structures and indexing requirements. For complex relational schemas, denormalizing data and flattening out entities can improve performance and simplify queries.

Question:

How can I flatten a database with multiple entity bindings (n:m relationships) for optimal indexing in ES?

Solution:

1. Denormalize Data:

Create product documents that include all relevant data, including embedded arrays of related entities. Here's an example schema:

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

2. Mapping Type:

Configure the mapping type to match the new schema:

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"
        }
      }
    }
  }
}

3. SQL Query:

Retrieve data from the database using a query that joins related entities and concatenates flag titles:

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