Efficient Data Management Through Food Model Separation

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I've enhanced the data structure by separating the food model and changing the lookup method from date-based to ID-based. This transformation offers several significant advantages:

Improved Data Retrieval Speed

Local Data Store Limitation: In the local data store, the getFoodItemByDate method reads and parses an entire JSON file into memory using this.getAllFood() and then searches for a matching date with find. This approach works fine for small datasets but becomes painfully slow as the data grows, requiring a full scan every time.

PostgreSQL Benefit: With PostgreSQL, operations like Food.findByPk(id) leverage primary key indexing, making lookups lightning-fast even with thousands of records. Adding an index on the date column could further optimize date-based queries, something a file-based system can't easily replicate.

Enhanced Data Integrity

Local Data Store Limitation: The JSON-based system relies on the application to enforce data rules. For example, there's no guarantee that calories is stored as a number parseFloat(calories) is needed in saveFood to fix potential type mismatches, which could lead to errors downstream.

PostgreSQL Benefit: In the Sequelize model, fields like calories are defined as DataTypes.FLOAT with allowNull: false, ensuring only valid numeric values are stored and no critical fields are left empty. Constraints like these, enforced at the database level, make data more reliable and consistent.


<Before the change>


<After the change>




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