Solving a Data Update Issue: Lessons in Consistency and Stability

Recently, I encountered an issue in my application where updating an item in both the "food" and "training" sections didn’t behave as expected. Even though the update itself was successful, the app didn’t redirect properly or throw an invalid error when it should have. After some investigation, I identified the root causes and implemented two solutions to address the problem. Here’s what I did, why it mattered, and the key takeaways from the experience.

The Problem

When updating an item, I noticed that the system wasn’t handling missing or invalid data gracefully. For example, if a date field was missing or invalid, the update would still complete, but it wouldn’t trigger the expected redirect or validation error. This led to inconsistencies in the UI and potential data integrity issues. I needed a way to ensure the system remained stable and user-friendly, even when the data wasn’t perfect.

Solution 1: Defaulting to Today’s Date

The first fix was simple but effective: instead of allowing invalid or missing dates, I set the default date to today’s date (March 19, 2025, in this case). This ensured consistency across the UI and safeguarded data integrity. Here’s how it works:

(It was the same case for Training)


  • How It’s Done: If an item’s date is missing or invalid, the system automatically fills it with the current date.

  • Why It Helps:

    • System Stability: By ensuring every item has a valid date, the system avoids unexpected errors or crashes caused by missing data.
    • Improved User Experience: Users no longer see blank date fields or need to manually correct them, today’s date is filled in automatically, reducing friction.

This small change made a big difference in keeping the app predictable and user-friendly.


Solution 2: Fixing the Update Logic

The second issue was in the code itself. Originally, the logic for updating an item looked like this:


const
updateItem = response.data || {...updatedFood};

(It was the same case for Training)


While this seemed reasonable at first, it had a serious flaw:

The root of the problem was that the update logic didn’t account for preserving existing data (like userId, name, or date from editingFood) while applying only the updated values (from updatedFood). For example, if updatedFood only contained { name, calorie, date } and omitted other values(not editable by users), the final updateItem would lose critical information.

Here’s how I fixed it (imagine this as the "picture" you mentioned):


(It was the same case for Training)


  • How It Works: This merges the original data  with the updated data, ensuring no essential fields are lost while applying the changes.

  • Advantages:

    • Preserving Existing Data: All properties from editingFood (e.g., userId, name, date, calorie, and so on) are kept, and only the fields provided in updateFood are updated.

    • Explicit Property Control: By defining exactly what gets updated, I avoid accidental omissions or incorrect data sneaking in.

    • Predictability: The resulting updateItem always follows the structure of editingFood, with updates applied consistently.

Key Takeaways

From this experience, I learned a few valuable lessons about building robust applications:

  1. Prioritize Data Integrity: Defaulting to today’s date ensured every item had a valid value, maintaining consistency and preventing errors.

  2. Design for Stability: The updated merge logic (...editingFood, ...updateFood or ...editingTraining, ...updateTraining) guarantees the system works reliably, even with partial or mismatched data.

  3. Enhance User Experience: Small tweaks, like auto-filling dates, save users time and effort, making the app more intuitive.

By addressing these issues, I not only fixed the immediate problem but also made the codebase more maintainable and the app more resilient. It’s a reminder that even small oversights, such as an undefined response.data or a missing date, can ripple through an application if not handled thoughtfully.









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