Database Design for Caching
Rate limits are a deliberate constraint on API usage designed to protect server resources and ensure fair access across users.
The Rate-Limit Problem
An API is a service shared by many users. A provider cannot allow every application to make unlimited requests, so it restricts how many requests one application can make during a time window. This restriction is called a rate limit. Rate limits protect server resources and help provide fair access across users.
The problem becomes visible when a dataset contains thousands of locations that must be sent to a geocoding API. The application may eventually reach the API's request ceiling. At that point, requests fail and the process stalls. A caching design does not remove the limit; it reduces the number of requests that the application needs to make.
Two Phases, One Dataset
The central strategy is to divide the work into two phases. Phase 1 collects data from the API. Phase 2 analyzes the data that has already been collected. Keeping these activities separate means that analysis can continue locally even when the API is temporarily unavailable or its request quota has been reached.
A Pausable Location Workflow
A dataset contains many locations that need geocoding, but the API limits how many requests the application can make during a time window.
- 1Collect
Phase 1 sends API requests for data that is not already in the local cache.
- 2Store
The returned data is placed in the local database so that the application has a record of what it has already collected.
- 3Pause
If the API rate limit is reached, phase 1 stops because additional requests fail.
- 4Resume
After the quota resets, phase 1 resumes and continues with data that is still missing from the cache.
- 5Analyze
Phase 2 reads the collected data locally rather than requiring a new API request for every analysis operation.
The workflow can pause and resume without losing the progress already stored in the cache.
Cache Lookup Before Request
A local cache changes the decision made for each item. Before requesting data, the application checks whether that item's data already exists locally. If it exists, the application uses the cached data and avoids another API call. If it does not exist, the application makes the API request and stores the result locally. This lookup is the key database behavior in the caching design.
| Cache state | Action | API request |
|---|---|---|
| Data already exists | Read the local cached data | Avoided |
| Data does not exist | Request the data and store the result | Made |
The cache lookup determines whether an API request is necessary.
When the Quota Stops Work
Reaching the rate limit does not mean that all collected progress has disappeared. It means phase 1 must pause because further requests fail. The application can wait for the quota to reset and then resume phase 1. Previously cached data remains useful, so subsequent runs need requests only for data that is not already cached.
Resetting Stale Data
A cache is useful when its stored data is the data you want to keep using. You may instead need to start over after discovering an error in the input data or deciding to geocode a subset of locations again with updated parameters.
For this workflow, reset the cached dataset by deleting the geodata.sqlite file. When phase 1 runs again, it finds no cached data and makes fresh API calls for every location. Resetting therefore restores the complete-miss condition: no previous results are available to prevent requests.
| Situation | Cache action | Effect on phase 1 |
|---|---|---|
| Continue a paused or later run | Keep the cached dataset | Use cached data and request only missing data |
| Input data contains an error | Delete geodata.sqlite | Make fresh API calls for every location |
| Updated parameters require re-geocoding | Delete geodata.sqlite | Start again with no cached data |
Keep the cache for continuation; reset it when the stored dataset should no longer be used.
Common Caching Mistakes
Common mistake Treating the rate limit as a reason to abandon the entire process.
What it looks likeThe application stops after requests fail at the limit, and the collected results are discarded.
Why it failsThe two-phase workflow is designed to pause phase 1, wait for the quota to reset, and resume without losing cached progress.
Do this insteadKeep the local cache, pause phase 1, and resume after the quota resets.
Common mistake Making an API request every time an item is processed.
What it looks likeThe application requests data again even though the item already exists in the local database.
Why it failsThis creates duplicate API calls and consumes requests that the cache could have avoided.
Do this insteadCheck whether the data exists locally before making the request.
Common mistake Deleting the cache when only a temporary rate-limit pause is needed.
What it looks likeThe application deletes geodata.sqlite immediately after requests fail.
Why it failsDeleting the file removes the cached dataset and causes the next phase 1 run to make fresh requests for every location.
Do this insteadDelete the cache only when the stored dataset should be replaced, such as after an input error or a change requiring re-geocoding.
Common mistake Assuming that a reset will reduce API usage.
What it looks likeThe application deletes geodata.sqlite and expects the next run to use fewer requests.
Why it failsAfter the reset, no cached data exists, so phase 1 makes fresh API calls for every location.
Do this insteadReset deliberately, understanding that it creates a complete cache miss on the next run.
Check Your Reasoning
A first run has stored results for some locations in the local cache. The API rate limit is then reached before the remaining locations are collected. Describe what the application should do now and what should happen after the quota resets.
Hints
- Separate phase 1 from phase 2.
- Identify which results are already protected by the cache.
- Decide whether the cache should be kept or deleted.
What do you think happens?
If geodata.sqlite is deleted before the next phase 1 run, will the application make API calls only for missing locations or for every location?
Reveal answer
For every location
Deleting geodata.sqlite removes the cached data. The next phase 1 run finds no cached entries and makes fresh API calls for every location.
Design Summary
- 1Rate limits protect server resources and help ensure fair access among API users.
- 2A two-phase workflow separates API data collection from local analysis, so collection can pause and resume.
- 3A local cache prevents duplicate API calls by checking for existing data before requesting new data.
- 4When the rate limit is reached, pause phase 1, wait for the quota to reset, and resume with the cache intact.
- 5Delete
geodata.sqliteonly when the cached dataset should be replaced; after deletion, the next phase 1 run makes fresh requests for every location.
At a glance
- Rate limits are deliberate constraints that protect shared API resources and support fair access.
- Separating collection and analysis into two phases makes a rate-limited workflow pausable and resumable.
- A cache lookup turns repeated requests into local reads when data already exists.
- A reached quota should pause collection, not erase progress.
- Deleting
geodata.sqliteresets the dataset and causes fresh API requests for every location on the next collection run.