Storing Geographic Data in Databases
SQLite serves as a local cache to store geocoding results, preventing redundant API calls to the same locations.
The Cost of Repeating a Lookup
A geocoding program looks up a location by sending a request to an external geocoding API. Every lookup consumes an API call, and most geocoding services impose a daily limit on the number of requests. If the same location is processed again, making another request wastes one of those limited calls.
A cache is local storage for results that have already been retrieved. In this workflow, SQLite acts as a local cache for geocoding results. Before contacting the API, geoload.py checks whether the location is already stored in geodata.sqlite. If it is stored, the program skips the API call.
The central pattern is check the cache, call the API only when necessary, and store the result for later use.
The First Run
On the first run, the database may be empty. geoload.py reads a location from the input file and checks geodata.sqlite. Because the location is not present, the program calls the geocoding API. It then stores the returned geocoding result in the database. The next time the same location appears, the stored result can be used instead of making another API request.
Ten Locations on the First Run
Imagine that an input file contains ten locations and geodata.sqlite does not yet contain any of them. What happens during processing?
Read a location: geoload.py reads the first location from the input file.
Check the database: The program checks geodata.sqlite and finds no cached result for that location.
Make the API request: Because the location is not cached, the program calls the geocoding API.
Store the result: The returned geocoding result is stored locally in the database.
Continue processing: The program repeats the same check, request, and storage pattern for the remaining locations.
All ten locations require API calls in this first-run scenario, and their results are stored for later runs.
A Faster Second Run
Caching becomes especially valuable when a later input file contains locations that were already processed. geoload.py checks each location against the existing database. Cached locations are skipped, while new locations follow the API-request path and are stored after their results arrive.
Ten Existing Locations and Five New Locations
A first run processed ten locations. A second input file contains those same ten locations plus five new locations. How many API calls are needed on the second run?
Inspect the original ten: The program checks each of the original ten locations and finds them in geodata.sqlite.
Skip cached locations: Because those ten results are already stored, the program does not call the API for them.
Reach the five new locations: The five new locations are not found in the database.
Request and store new results: The program calls the API for the five new locations and stores their results.
The second run makes five API calls instead of fifteen. The source example describes this second run as five times faster than the first because the ten original locations are served from the cache.
The cache does not eliminate all API calls forever. It eliminates repeated calls for locations whose results are already stored. New locations still require requests the first time they are encountered.
Working Within Daily Limits
A daily API rate limit is a maximum number of requests allowed during a 24-hour period. A small dataset may fit within one run, but a dataset containing thousands of locations may require more requests than the daily limit allows.
geoload.py includes a counter that can limit how many API calls are made in one run. For example, the counter can be set to 100 calls per run. The program processes uncached locations until it reaches that limit, stores the results it has obtained, and stops. A later run can continue with the next uncached locations because the earlier results are now in the database.
| Run | What happens | Why the next run can continue |
|---|---|---|
| First run | Processes new locations until the counter limit is reached | Completed results are stored in geodata.sqlite |
| Later run | Checks the database and processes the next uncached locations | Previously completed locations are skipped |
| Following runs | Repeat the same batch process over several days | The cache records the progress already made |
The counter and persistent local cache work together to divide a large dataset into manageable batches.
Understanding the Stored Result
The database connects a location with the geocoding result retrieved for that location. The important behavior is not that the program stores a particular visual map or makes a new request every time. The important behavior is that a previously retrieved result is stored locally and can be found when the same location is processed again.
The diagram shows the conceptual relationship rather than a complete SQLite schema. The source material establishes that locations and their geocoding results are stored locally, including stored coordinate information, but it does not specify every database column.
Resetting the Cache
Sometimes the existing cache should be discarded. Removing the geodata.sqlite file clears all stored results. When geoload.py is run afterward, it does not find the old database, creates a new one, and treats every location as uncached. The program therefore needs to geocode the locations again from scratch.
- Remove geodata.sqlite when you want to clear all cached data.
- Understand that deletion erases every stored result in the cache.
- Expect the next run to make fresh API calls for all processed locations.
- Use this reset when cached data may be incorrect, when switching to a different geocoding service, or when testing the program.
Mistakes to Avoid
Assuming every location requires an API call on every run.
The cache exists specifically to prevent redundant calls for locations whose results have already been stored.
Fix:
Check the database before requesting data from the API.Using the request counter as though it permanently records all future progress.
The counter limits calls for a run, while the database preserves completed results between runs.
Fix:
Use the counter to control the current batch and geodata.sqlite to remember cached results.Deleting geodata.sqlite without recognizing the consequence.
Removing the file erases all stored results.
Fix:
Delete the file only when you intentionally want a fresh cache and fresh geocoding.Trying to process thousands of locations in one unrestricted run.
Most geocoding APIs impose a maximum number of requests in a 24-hour period.
Fix:
Set a per-run counter and process the dataset in batches over multiple days.
Check Your Understanding
A first run processes 100 locations and stores their results. The next input file contains 100 of those locations and 20 new locations. Explain which locations need API calls and why.
Hints
- Start by separating locations already stored in geodata.sqlite from locations not found there.
- Remember that the cache is checked before the API is called.
- The new locations follow the request-and-store path.
A dataset contains thousands of locations, and the program is configured for 100 API calls per run. Describe what should happen after the first run reaches its limit and what the next run should do.
Hints
- The completed results remain in the local database.
- The next run checks the database before making new requests.
- The process can continue over several days.
What do you think happens?
What happens if geodata.sqlite is removed before running geoload.py again?
Reveal answer
Answer: The program creates a new database and treats locations as uncached.
Removing geodata.sqlite erases all stored results. On the next run, geoload.py does not find the old database, creates a new one, and must re-geocode the locations.
The Repeatable Pattern
SQLite turns geocoding into a repeatable workflow instead of a sequence of independent API requests. geoload.py checks the local database first, requests data only for locations that are not cached, and stores new results for later runs.
- A local SQLite database prevents redundant API calls by storing geocoding results.
- The program checks the cache before calling the API for each location.
- A request counter limits calls per run, allowing large datasets to be processed in batches over several days.
- Deleting geodata.sqlite clears every cached result and causes the next run to start fresh.
- The pattern of checking local results, fetching only when needed, and storing the result applies to other external-data workflows.
Key Takeaways
- SQLite acts as a local cache for geocoding results.
- geoload.py avoids repeated API calls by checking geodata.sqlite before contacting the API.
- A per-run request counter and repeated runs help handle daily API rate limits.
- Removing geodata.sqlite erases the cache and forces fresh geocoding on the next run.
- The overall strategy is check, fetch only when needed, and store.