Data Fetching Lifecycle
The data fetching lifecycle follows one read request. It moves through waiting to success or error. Success may contain data or be empty.
Another example
A guide list keeps its last result while a new filter is loading.
Main parts
- 01Trigger
- 02Pending state
- 03Parsed result
- 04Empty branch
- 05Error recovery
Use it when
Use it whenever a screen reads data over a network.
Do not use it when
Do not show one endless spinner. Show what is loading and how to recover.
Name used in code
trigger -> request -> pending -> parse -> result Before you ship
Check the runtime and version, integration limits, dependencies and licenses, and platform support. Verify security, failure, speed, monitoring, deploy, support owner, upgrade path, and rollback.
Outcome: Use or review Data Fetching Lifecycle as a technical building block that the team can maintain. User context: A guide list keeps its last result while a new filter is loading. Technology or engineering concept: Data Fetching Lifecycle. Why it fits: Use it whenever a screen reads data over a network. Do not use it when: Do not show one endless spinner. Show what is loading and how to recover. Engineering fit: Define where it runs and which version to use. Set its job, interfaces, data flow, dependencies, supported systems, security, and speed limits. Explain how it fits the current stack. Lifecycle safeguards: Check dependency health and licenses. Add monitoring and a fallback. Name the upgrade owner. Do not add a second tool for the same job without a measured benefit. Acceptance criteria: A small integration proves the needed behavior. Test compatibility, security, failure, and performance. Document deploy, upgrade, support, and rollback steps. Evidence and limits (evidence boundary): No single official standard controls the whole term. Its meaning may change across teams, platforms, or frameworks. Unknowns to confirm: Current stack, runtime and version rules, target systems, dependency owner, performance budget, support period, and migration limits.
Check this request
Review the current use of Data Fetching Lifecycle. Definition: The data fetching lifecycle follows one read request. It moves through waiting to success or error. Success may contain data or be empty. Release checks: Check the runtime and version, integration limits, dependencies and licenses, and platform support. Verify security, failure, speed, monitoring, deploy, support owner, upgrade path, and rollback. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.