Exporting and Visualizing Cached Geocoding Results
SQLite serves as a local cache to store geocoding results, preventing redundant API calls to the same locations.
Why Cache Geocoding Results
A geocoding program looks up locations through an external API. Every lookup consumes an API request, and most geocoding services impose a daily limit on how many requests can be made. Repeating a lookup for the same location wastes one of those requests. The central idea of this article is to keep the result locally so that the program can reuse it instead of asking the API again.
A cache is a local store of previously retrieved results. In this workflow, SQLite acts as the cache: geoload.py checks the geodata.sqlite database before making an API request and skips the request when the location is already stored.
A First Run Through the Program
Ten Locations in an Empty Cache
What happens when geoload.py is run for the first time with a file containing ten locations and an empty database?
Read a location: The program takes the first location from the input.
Check SQLite: The database contains no result for that location, so the location is uncached.
Call the API: Because the result is missing, the program sends a geocoding request.
Store the result: The retrieved geocoding result is saved in the local database.
Continue: The same read, check, request, and store pattern is repeated for the remaining locations.
All ten locations are processed and their results are stored in SQLite. This first run makes ten geocoding API calls.
The important state change is from an empty cache to a cache containing results. After a location has been stored, its result is available for a later run. The cache therefore grows as uncached locations are successfully processed.
What Changes on the Second Run
Ten Known Locations and Five New Ones
Suppose the input now contains the original ten locations plus five new locations. How many API calls does the second run need?
Process the original ten: geoload.py checks each location in SQLite, finds its stored result, and skips the API call.
Reach the five new locations: These locations are not in the cache, so the program sends API requests for them.
Store new results: The five newly retrieved results are added to the database.
The second run makes five API calls instead of fifteen. According to the source example, it is five times faster than the first run.
Caching does not mean that every input location is ignored. The program still reads and checks each location. The saving occurs when a check finds a stored result: that location is reused without another API request.
Working Within Daily Limits
A small dataset may fit within one day's request allowance, but a dataset containing thousands of locations may exceed the API's daily rate limit. geoload.py addresses this situation with a counter that limits how many API calls can be made during one run.
Processing One Hundred Requests at a Time
How can a large dataset be processed when the program is configured to make at most 100 API calls per run?
Set the counter: Configure the counter so one run can make up to 100 API calls.
Run the program: The program processes uncached locations until it reaches the counter limit, then stops.
Keep the stored results: Results obtained during the run remain in the SQLite database.
Run again later: On the next run, cached locations are skipped and the program continues with the next uncached locations.
Repeated runs over several days gradually process the dataset while limiting the number of new API requests made in each run.
Resetting the Local Database
Sometimes the existing cache should not be reused. If you suspect the stored data is incorrect, want to switch to a different geocoding service, or are testing the program, remove the geodata.sqlite file. This deletes all stored results.
Using Stored Results Later
The SQLite database is the local store that makes the workflow repeatable. Once geoload.py has saved geocoding results, later processing can consult those stored records instead of repeatedly contacting the external service. The source material emphasizes the cache, the request-saving workflow, and the ability to extend collection over multiple runs; it does not specify a particular export file format or visualization library.
For an export or visualization step, the essential preparation described here is that the geocoding results already exist locally in SQLite. The cache preserves the results collected so far, while the program's lookup behavior prevents the same locations from consuming unnecessary API calls.
Common Cache Mistakes
Assuming every input location causes a new API request.
geoload.py checks geodata.sqlite first and skips the API call when a result is already cached.
Fix:
Distinguish between reading and checking a location and actually sending a new request.Ignoring the request counter for a large dataset.
Geocoding APIs commonly impose daily rate limits.
Fix:
Set a per-run counter and process uncached locations in batches across multiple runs.Deleting geodata.sqlite without expecting re-geocoding.
Removing the file erases all stored results.
Fix:
Delete the file only when a full reset is intended, such as correcting suspect cached data, changing services, or testing.
Check Your Understanding
A first run stores results for 40 locations. A later input contains those same 40 locations and 12 new locations. The program's request counter allows 5 API calls per run. Explain what happens during the next three runs and state when deleting geodata.sqlite would be appropriate.
Hints
- Begin by identifying which locations are already cached.
- Only new locations consume API calls.
- Apply the five-call limit to the uncached locations across repeated runs.
- Remember that deleting geodata.sqlite removes every stored result.
What do you think happens?
After the first run has cached ten locations, what happens when the same ten locations are processed again?
Reveal answer
Answer: The ten cached results are reused and no new API calls are needed for them.
geoload.py checks SQLite before calling the API and skips locations whose results are already stored.
Key Takeaways
- SQLite provides a local cache for geocoding results, so repeated locations do not require repeated API calls.
- geoload.py reads a location, checks the cache, requests data only for an uncached location, and stores the new result.
- A request counter limits calls in 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 cache-and-reuse pattern supports repeatable external-data workflows and prepares stored results for later data use.
Key Takeaways
- SQLite prevents redundant geocoding requests by storing results locally.
- geoload.py checks the cache before contacting the API and stores results for future runs.
- A configurable counter and multiple runs help keep API usage within daily limits.
- Deleting geodata.sqlite resets the workflow but erases all cached results.
- Cached results provide the local foundation for later export or visualization work.