How to Optimize ElasticSearch Index Structure for Multiple Entity Bindings?

How to Set Up ElasticSearch Index Structure with Multiple Entity Bindings

Introduction

Establishing an effective ElasticSearch (ES) index structure is crucial for efficiently managing and querying data. When working with multiple entity bindings, it's essential to optimize the index for optimal search and retrieval performance.

Flattening Out the Data Structure

In the provided database structure, multiple tables are used to represent products and their associated flags. To simplify the ES index, it's recommended to flatten the data structure and denormalize it. By creating product documents that include all relevant information, including flags, we eliminate the N:M relationship between products and flags. This approach allows for easier queries on flag attributes.

Example Product Documents

{
   "id": "00c8234d71c4e94f725cd432ebc04",
   "title": "Alpha",
   "price": 589.0,
   "flags": ["Sellout", "Top Product"]
}
{
   "id": "018357657529fef056cf396626812",
   "title": "Beta",
   "price": 355.0,
   "flags": ["Discount"]
}
{
   "id": "01a2c32ceeff0fc6b7dd4fc4302ab",
   "title": "Gamma",
   "price": 0.0,
   "flags": ["Discount"]
}

Product Mapping Type

The ES mapping type for the product documents would be:

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 Fetching Data

To extract the required data from the database, a modified SQL query is necessary:

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