Introduction to Geocoding APIs
SQLite serves as a local cache to store geocoding results, preventing redundant API calls to the same locations.
Why Geocoding Needs a Cache
A geocoding program looks up locations through an external API. Each lookup consumes an API call, and most geocoding services impose a daily limit on the number of requests. Looking up the same location more than once wastes one of those calls. A local cache prevents this waste by storing results that have already been retrieved.
In this workflow, SQLite is the cache. SQLite is a lightweight, file-based database that requires no server setup and works well in a local workflow. The geoload.py program stores geocoding results in a file named geodata.sqlite. Before it calls the API for a location, it checks that database. If the location is already stored, the program uses the cached result and skips the API call.
The Geoload Workflow
geoload.py applies the same decision process to each location. It reads a location, checks geodata.sqlite, and determines whether that location is already cached. A cached location is skipped. An uncached location is sent to the geocoding API, and the returned result is stored in SQLite so that a later run can reuse it.
- Read a location from the input data.
- Check the geodata.sqlite database for that location.
- If the location is already cached, skip the API call.
- If the location is not cached, call the geocoding API.
- Store the retrieved result in SQLite.
- Continue with the next location until the run reaches its available work or its configured call limit.
A Two-Run Example
Ten Existing Locations and Five New Ones
Suppose geoload.py first processes a file containing ten locations, then runs again on a file containing those same ten locations plus five new locations.
First run: The database is empty. Each of the ten locations is absent from the cache, so the program calls the API for each one and stores each result.
Second run: original locations: The program reads the original ten locations and finds each one in SQLite. It skips the API call for all ten.
Second run: new locations: When the program reaches the five new locations, they are not in the cache. It calls the API for those five and stores their results.
The second run makes five API calls instead of fifteen because the ten previously processed locations are served from the cache. The source describes this second run as five times faster than the first.
| Location state | SQLite check | API call | Stored result |
|---|---|---|---|
| Already cached | Location is found | Skipped | Existing result is reused |
| Not cached | Location is not found | Made | New result is stored |
Working Within Daily Limits
A daily API rate limit is the maximum number of requests that can be made during a 24-hour period. A small dataset may fit within one run, but a dataset containing thousands of locations may require a multi-day plan. geoload.py includes a counter that limits how many API calls can be made during one run.
Processing 100 New Locations per Run
Suppose the call counter is set to 100 and the input contains more uncached locations than one run should process.
Set the limit: Configure the counter to allow 100 API calls during one run.
Run the program: geoload.py processes uncached locations, stores their results, and stops after reaching the counter limit.
Run again later: On the next run, the program checks the database. Previously stored locations are skipped, so processing continues with the next uncached locations.
Repeated runs over several days allow a large dataset to be processed in batches without exceeding the chosen per-run call limit.
Resetting Stored Results
Sometimes the existing cache should not be reused. You may want to start over if you suspect the cached data is incorrect, want to switch to a different geocoding service, or are testing the program. In these cases, remove the geodata.sqlite file.
Removing geodata.sqlite erases all stored results. When geoload.py runs again, it does not find the old database, creates a new one, and treats every location as uncached. API requests then begin again for all locations that the program processes.
Common Workflow Mistakes
Assuming every location in the input requires a new API call
geoload.py checks geodata.sqlite before calling the API, so previously stored locations should be skipped.
Fix:
Let the cache check decide whether a location needs an API request.Trying to process a very large dataset in one unrestricted run
Most geocoding APIs impose a maximum number of requests during a 24-hour period.
Fix:
Use the program's counter to limit calls per run and continue with multiple runs over several days.Removing geodata.sqlite without realizing that all cached results will be lost
After removal, the new database is empty and every location is treated as uncached.
Fix:
Remove the file only when you intentionally want to re-geocode from scratch.
Practice the Decision Process
Imagine that geodata.sqlite already contains ten locations. Your next input contains those ten locations and five new locations. Explain which locations cause API calls, which locations are skipped, and what is stored after the run.
Hints
- Separate the locations found in SQLite from the locations not found.
- Only uncached locations require API calls.
- Newly retrieved results are stored for later runs.
Now suppose the input contains more uncached locations than the configured per-run counter allows. Describe what happens during the current run and how the next run continues.
Hints
- The counter limits API calls during one run.
- Results completed during the run remain in SQLite.
- The next run skips stored locations and continues with uncached ones.
The Reusable Pattern
- SQLite provides a local cache for geocoding results, so repeated locations do not require repeated API calls.
- geoload.py reads a location, checks geodata.sqlite, skips cached locations, calls the API for uncached locations, and stores new results.
- A request counter limits calls during one run, allowing large datasets to be processed in batches over several days.
- Removing geodata.sqlite clears every stored result; the next run creates a new database and re-geocodes locations from scratch.
- The general pattern is check the cache, call the external service only when needed, and store the result for future use.
Key Takeaways
- SQLite acts as a local cache that prevents redundant geocoding API calls.
- geoload.py checks the cache before every possible API request and stores newly retrieved results.
- A counter and multiple runs help keep processing within daily API rate limits.
- Deleting geodata.sqlite resets the process but erases all cached results and requires re-geocoding.