Concepts / Error Handling and Retry Logic in API Calls

Error Handling and Retry Logic in API Calls

Rate limits are a deliberate constraint on API usage designed to protect server resources and ensure fair access across users.

  • Programming

When Requests Stop

Imagine processing thousands of locations with a geocoding API. Your application sends requests one after another, but the service does not allow unlimited access. Eventually, the requests can fail because the application has reached the service's rate limit. The data-processing task then stalls unless the workflow can pause and continue later.

Definition

A rate limit is a deliberate restriction on how many requests a single application can make to a service within a given time window. Rate limits protect server resources and help distribute access fairly among users.

API serviceServer resourcesFair accessRate limitApplication requestsmanagessetsrestrictssupportsprotects
How do rate limits protect a shared service while multiple applications use it?

The Two-Phase Workflow

A reliable workflow separates the task into two phases. Phase 1 collects data from the API. Phase 2 analyzes the collected data locally. This separation matters because analysis does not need to keep making API requests. If phase 1 reaches the rate limit, it can pause while phase 2 remains a separate activity. When the quota resets, phase 1 can resume without losing the progress that was already collected.

LocationsPhase 1 collectionStored API resultsPhase 2 analysisPause and resumesend needed requestssave resultsread collected datarate limit reachedquota resets
How does data move through collection and analysis without requiring every task to remain connected to the API?
  1. 1
    Use phase 1 to request and store API results.
  2. 2
    If the rate limit is reached, pause phase 1 rather than treating the entire task as lost.
  3. 3
    Wait for the quota to reset.
  4. 4
    Resume phase 1 using the data already stored as progress.
  5. 5
    Run phase 2 against the collected data for analysis.

A Rate-Limit Failure

Exceeding the limit does not mean that the dataset is unusable. It means that the application cannot continue making requests at that moment. The recovery sequence is to pause phase 1, wait for the quota to reset, and then resume collection. The important design choice is to preserve the results already collected instead of starting the entire job again.

API requestRate limit reachedPause phase 1Quota resetResume collectionresponse statereachedhold requestsafter reset
What happens after a request reaches the service's rate limit?
Worked example

A Large Location Dataset

A process must geocode thousands of locations, but the service limits how many requests one application can make during a time window.

  1. 1
    Collect

    Phase 1 sends requests and stores the results that arrive successfully.

  2. 2
    Pause

    When the rate limit is reached, phase 1 stops sending requests temporarily.

  3. 3
    Wait

    The process waits for the service quota to reset.

  4. 4
    Resume

    Phase 1 continues collection rather than discarding the results already stored.

  5. 5
    Analyze

    Phase 2 works with the collected data as a separate step.

Result

The workflow manages a rate-limited collection task by separating requests from analysis and preserving progress between collection attempts.

The Cache Decision

Local caching stores API results in a local database so the application can check whether data already exists before making another request. If the requested data is already cached, the application can use the stored result instead of making a duplicate API call. If it is not cached, phase 1 makes the API request and stores the result for later use.

LocationCheck local databaseCached resultUse dataAPI requestSave resultlook upexistsdoes not existstore responsereuseread stored result
How does the application decide between using stored data and sending a new API request?
  1. 1
    Choose the location or other item that needs data.
  2. 2
    Check the local database for an existing result.
  3. 3
    If the result exists, use it without making another API call.
  4. 4
    If the result does not exist, request it from the API.
  5. 5
    Store the new result locally so a later run can reuse it.

Resetting Stored Data

A cache is useful when its stored results are still appropriate. Sometimes you need to start over because the input data contains an error or because you want to geocode a subset of locations using updated parameters. In that situation, delete the geodata.sqlite file. When phase 1 runs again, it finds no cached data and makes fresh API calls for every location.

geodata.sqlitecached dataDelete cacheNo cached dataFresh API callsevery locationreset requestedcache removedphase 1 runs
What changes when the local dataset is reset?
Before

Existing geodata.sqlite file Phase 1 checks the cache Stored results can be reused

Previously collected results are available locally.

After

geodata.sqlite file deleted Phase 1 finds no cached data Fresh API calls are made for every location

The next collection run starts without cached results.

Mistakes in Recovery Design

  • Common mistake

    Treating a rate-limit failure as if all collected progress were lost.

    What it looks like

    Restarting the entire collection task without preserving the results already obtained.

    Why it fails

    The two-phase workflow is designed to pause phase 1 and resume after the quota resets.

    Do this instead

    Preserve the collected data, wait for the quota to reset, and resume phase 1.

  • Common mistake

    Sending a new API request when the result is already stored locally.

    What it looks like

    Ignoring the local database and requesting the same location again on every run.

    Why it fails

    Repeated requests consume the limited API allowance unnecessarily.

    Do this instead

    Check the local database before making a request and reuse an existing result.

  • Common mistake

    Analyzing data while treating analysis and API collection as one inseparable operation.

    What it looks like

    Continuing to depend on API requests during the analysis phase after the rate limit has been reached.

    Why it fails

    The two-phase strategy separates collection from analysis so collection can pause without preventing later analysis of stored data.

    Do this instead

    Collect and store results in phase 1, then analyze the collected data in phase 2.

  • Common mistake

    Keeping a cache after deciding that its data is invalid or outdated for the task.

    What it looks like

    Reusing cached geocoding results after correcting input data or changing parameters.

    Why it fails

    The stored results may no longer represent the data or parameters you want to process.

    Do this instead

    Delete geodata.sqlite so the next phase 1 run makes fresh API calls.

Apply the Workflow

MEDIUM

A local database contains results from an earlier run. You correct some location data and want those locations to be processed again with fresh API calls. Describe the sequence of actions you would take, including what happens to the cache and what phase performs the new requests.

Hints
  • Identify whether the existing cached data should still be reused.
  • Recall the specific file used for the local cache.
  • Separate the new requests from the later analysis phase.
Worked example

Reset and Recollect

A corrected dataset must be geocoded again instead of using the previous cached results.

  1. 1
    Decide to reset

    The previous cached data should not be reused because the input data has changed.

  2. 2
    Delete the cache

    Delete the geodata.sqlite file.

  3. 3
    Run phase 1

    The collection process finds no cached data and makes fresh API calls for every location.

  4. 4
    Handle the limit

    If the rate limit is reached, pause phase 1, wait for the quota to reset, and resume collection.

  5. 5
    Run phase 2

    Analyze the newly collected data separately from the API-collection work.

Result

The corrected dataset is recollected from the API, while the two-phase workflow and caching strategy continue to manage rate-limited requests.

Key Takeaways

  1. 1
    Rate limits protect server resources and help provide fair access among users.
  2. 2
    A two-phase workflow separates API collection from local analysis, making it possible to pause and resume collection.
  3. 3
    A local database cache prevents duplicate API calls by checking for existing data first.
  4. 4
    When a rate limit is reached, pause phase 1, wait for the quota to reset, and resume.
  5. 5
    Delete geodata.sqlite when you need a fresh dataset; the next phase 1 run then makes new API calls for every location.

At a glance

  • Rate limits constrain requests to protect shared API resources and distribute access fairly.
  • Separate collection from analysis so rate-limit interruptions affect phase 1 rather than discarding the whole workflow.
  • Check the local cache before making a request to avoid redundant API calls.
  • Pause when the quota is reached, wait for the reset, and resume with stored progress.
  • Delete geodata.sqlite when the cached dataset must be replaced with fresh API results.

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