How to Design the Optimal Firestore Data Structure for Efficient Provider-Product Search?

Selecting the Optimal Firestore Data Structure for Provider-Product Relationships

Problem:

Devise an efficient data structure in Firestore to enable searching for providers based on product categories.

Optimal Approach:

The proposed data structure, outlined below, is well-suited for the intended use case:

Providers ( Collection )<br>   Provider 1 ( Document )</p>
<pre class="brush:php;toolbar:false">  Name
  City
  Categories

Provider 2

  Name
  City

Products ( Collection )
Product 1 ( Document )

  Name
  Description
  Category
  Provider ID

Product 2

  Name
  Description
  Category
  Provider ID

Justification:

• Data duplication: Storing provider information within product documents (through Provider ID) is an effective denormalization technique, leading to faster read times. Accessing both collections is still possible when necessary.
• Data consistency: While denormalization eliminates the need for multi-document reads, maintaining data consistency remains essential. Updates to provider information need to be reflected in all associated product documents.
• Performance and cost: Duplicating provider data may increase storage usage, but this trade-off is justified by faster queries. Firestore charges for API calls and writes more heavily than for read operations.
• Security: Creating an appropriate security rule to protect provider information while still allowing product-related queries is crucial.

Alternative Structures:

• Storing references only: Holding only provider references in product documents simplifies writing but complicates reading (requiring multiple API calls).
• Complete provider duplication: Copying the entire provider object into product documents eliminates extra calls but increases write complexity and storage usage.

Choosing Optimal Approach:

The most suitable data structure ultimately depends on the specific needs and requirements of the application. Factors to consider include data size, frequency of updates, read performance constraints, and cost implications.

Related Discussions:

• [Firestore Collections, Maps, and Arrays Explained](link to related post)

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