Rendering Data That Disagree

The main focus of Tom's talk is how to handle rendering data that may be inconsistent when fetching from databases for web applications, and strategies to ensure data consistency.

Data consistency is important because inconsistencies can lead to visual errors or logical errors in applications, such as missing users or messages, which can confuse or frustrate users.

Running separate SQL queries can lead to inconsistencies, such as a message appearing without a corresponding user if the queries are not executed in a single transaction.

Inconsistent data can cause visual inconsistencies on the user interface, leading to confusion or concerns, such as thinking a purchase was made incorrectly or missing important information.

Tom suggests using transactions to ensure data consistency and leveraging back-end systems like Convex to maintain consistency from the database to the client.

React can compound data consistency issues due to its asynchronous nature and the way it handles state updates, potentially leading to flashes or inconsistencies if not managed carefully.

Batching updates can help by ensuring that multiple state updates occur simultaneously, reducing the chance of rendering inconsistent data between updates.

Convex is a system that allows subscribing to multiple queries and receiving consistent updates at the same logical timestamp, ensuring a consistent client view and extending database guarantees to the front end.

Developers can account for inconsistencies by clearly documenting which data sources are consistent with each other, and by using systems that ensure consistency, like Convex.

Separate API endpoints might lead to data inconsistency because they may involve different transactions or databases, making it challenging to synchronize data updates effectively.