Refactoring Quarantine Status From Product To Batch Entity Database And Repository Changes

by StackCamp Team 91 views

In this article, we'll dive deep into a significant refactoring effort focused on enhancing data integrity within our inventory management system. The core of this refactoring involves moving the quarantine status from the Product entity to the Batch entity. This architectural shift addresses a critical logical inconsistency and ensures the long-term reliability of our data.

Context and Problem Statement

Currently, the system incorrectly assigns the quarantine status to the Product entity. Guys, imagine this scenario: the status of an entire product is dictated by the status of the last batch received. This means a single "perfect" batch can override the status of a product, marking it as 'ACTIVE' even if there are other "imperfect" batches sitting in quarantine. This is a major issue, as it compromises the accuracy of our inventory data. Basically, it’s like saying one good apple makes the whole bunch good, even if some are rotten!

To illustrate the problem further, consider a product that has multiple batches. Some batches might have quality issues and need to be quarantined, while others are perfectly fine. With the current setup, if the last batch received is of good quality, the entire product is marked as 'ACTIVE,' regardless of the quarantined batches. This can lead to inaccurate stock levels and potentially shipping out products that shouldn't be. Therefore, we must address the problem of incorrect quarantine status in the product entity to fix long-term data integrity of the inventory.

Another issue we're tackling is the non-idempotent nature of our batch persistence logic. What this means is that re-importing the same NF-e (Nota Fiscal Eletrônica, a Brazilian electronic invoice) results in duplicate entries in the batchs table. This data redundancy not only wastes storage space but can also lead to performance degradation over time. It’s like having multiple copies of the same receipt – unnecessary and messy!

Proposed Solution: Moving Quarantine Status to the Batch Entity

To address these challenges, we're embarking on a major architectural refactoring. The primary goal is to move the state ownership to the correct entity – the Batch – and ensure data integrity. This will involve changes to both the database schema and the repository logic.

1. Schema Refactoring (database.py)

The first step is to modify the database schema to reflect the new architecture. This involves the following changes to the database.py file:

  • Removing Columns from products Table: We'll remove the status and quarantine_reason columns from the products table. These fields no longer belong at the product level.
  • Adding Columns to batchs Table: We'll add status TEXT NOT NULL and quarantine_reason TEXT NULL columns to the batchs table. This is where the quarantine status will now be stored, making it batch-specific.
  • Adding a Unique Constraint: We'll add a UNIQUE (product_id, physical_id) constraint to the batchs table. This is a crucial step to enforce data integrity at the database level. This constraint will prevent duplicate batch entries for the same product and physical ID, resolving the idempotency issue.

By adding the unique constraint, we ensure that the database itself prevents the insertion of duplicate batches. This is a robust solution that guarantees data integrity and avoids the complexities of handling duplicates in the application logic.

2. Repository Refactoring (product_repository.py)

The next step is to refactor the repository logic to work with the new schema. This involves several changes to the product_repository.py file:

  • Adapting _determine_product_status Logic: The logic in _determine_product_status will be modified to operate on batch data and return the status for the batch, not the product. This function will now determine the status of a batch based on its individual characteristics.
  • Removing _update_table_products: The _update_table_products method, in its current form for status updates, will become obsolete and should be removed. This method was responsible for updating the product status, which is no longer necessary.
  • Implementing Upsert Logic for Batches: We'll implement a new "Upsert" (Update or Insert) logic for the Batch entity. This is a key part of addressing the idempotency issue. Before inserting a batch, the repository will check if a batch with the same (product_id, physical_id) already exists.
    • If it does not exist: The repository will INSERT the new batch with its determined status.
    • If it does exist: The repository will UPDATE the existing batch by summing the quantities. This ensures that we don't create duplicate entries and instead update the existing batch with the new quantity information.

This Upsert logic is crucial for ensuring that our batch data remains consistent and accurate. It prevents the creation of duplicate entries and handles updates gracefully.

3. Test Suite Refactoring (test_product_repository.py)

Finally, we need to update the test suite to reflect the new schema and logic. This involves significant changes to the test_product_repository.py file:

  • Updating Tests to Assert Batch Status: The entire test suite for the repository must be updated to reflect the new schema and logic. Tests will now need to assert the status on the batchs table, not the products table. This ensures that our tests are validating the correct behavior in the new architecture.

By thoroughly testing the changes, we can ensure that the refactoring has been implemented correctly and that the system is functioning as expected. This includes testing the Upsert logic, the status determination for batches, and the overall data integrity.

Acceptance Criteria

To ensure the success of this refactoring, we've defined clear acceptance criteria:

  • The status and quarantine_reason columns must be successfully removed from the products table.
  • The status and quarantine_reason columns must be successfully added to the batchs table.
  • The UNIQUE (product_id, physical_id) constraint must be successfully added to the batchs table.
  • The _determine_product_status logic must be correctly adapted to run on batch data and return the status for the batch.
  • The _update_table_products method must be removed.
  • The Upsert logic for the Batch entity must be implemented correctly, ensuring that new batches are inserted and existing batches are updated.
  • The test suite must be updated to reflect the new schema and logic, and all tests must pass.

Benefits of the Refactoring

This refactoring brings several significant benefits to our inventory management system:

  • Improved Data Integrity: Moving the quarantine status to the Batch entity ensures that the status is accurately associated with the specific batch, rather than being overwritten by the last received batch. This eliminates the logical inconsistency of the previous design and provides a more reliable representation of our inventory.
  • Elimination of Data Redundancy: The Upsert logic and the unique constraint prevent duplicate batch entries, reducing data redundancy and improving storage efficiency. This also simplifies data management and reduces the risk of inconsistencies.
  • Enhanced Performance: By preventing duplicate entries and optimizing the data structure, we can improve the overall performance of the system. This is especially important as the system scales and handles larger volumes of data.
  • Simplified Logic: The new architecture simplifies the logic for determining product status, as it is now based on individual batch statuses rather than a potentially misleading product-level status. This makes the system easier to understand and maintain.
  • Increased Scalability: The improved data integrity and performance make the system more scalable, allowing it to handle future growth and increased demand.

Conclusion

This refactoring is a crucial step in ensuring the long-term health and reliability of our inventory management system. By moving the quarantine status to the Batch entity and implementing Upsert logic, we're addressing critical data integrity issues and laying the foundation for a more robust and scalable system. It's like giving our inventory system a much-needed checkup and a fresh start, guys! This meticulous approach to data management will undoubtedly benefit our operations in the long run.