Concepts / Exporting and Visualizing Cached Geocoding Results

Exporting and Visualizing Cached Geocoding Results

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.

  • Programming

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.

Definition

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.

LocationSQLite cachegeodata.sqliteCached resultGeocoding APIStored resultcheckfoundnot foundretrieve and save
How does geoload.py decide whether to reuse a geocoding result or send a new request?

A First Run Through the Program

Worked example

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?

  1. 1
    Read a location

    The program takes the first location from the input.

  2. 2
    Check SQLite

    The database contains no result for that location, so the location is uncached.

  3. 3
    Call the API

    Because the result is missing, the program sends a geocoding request.

  4. 4
    Store the result

    The retrieved geocoding result is saved in the local database.

  5. 5
    Continue

    The same read, check, request, and store pattern is repeated for the remaining locations.

Result

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.

Read locationCheck cacheCached resultUncached locationAPI requestSave resultNext locationfoundnot foundskip request
What sequence does geoload.py follow while reading locations, consulting SQLite, calling the API, and storing results?

What Changes on the Second Run

Worked example

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?

  1. 1
    Process the original ten

    geoload.py checks each location in SQLite, finds its stored result, and skips the API call.

  2. 2
    Reach the five new locations

    These locations are not in the cache, so the program sends API requests for them.

  3. 3
    Store new results

    The five newly retrieved results are added to the database.

Result

The second run makes five API calls instead of fifteen. According to the source example, it is five times faster than the first run.

Key point

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.

Worked example

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?

  1. 1
    Set the counter

    Configure the counter so one run can make up to 100 API calls.

  2. 2
    Run the program

    The program processes uncached locations until it reaches the counter limit, then stops.

  3. 3
    Keep the stored results

    Results obtained during the run remain in the SQLite database.

  4. 4
    Run again later

    On the next run, cached locations are skipped and the program continues with the next uncached locations.

Result

Repeated runs over several days gradually process the dataset while limiting the number of new API requests made in each run.

geoload.pySQLite cacheGeocoding APIRun 1: check locationsRun 1: request up to counter limitRun 1: store resultsRun 2: skip stored locationsRun 2: request next uncached batch
How do a request counter and repeated runs control API calls while cached results accumulate?

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.

geodata.sqlitestored resultsNew databaseno stored resultsCached locationsreused without API callsAll locationstreated as uncachedcontains results forstarts empty
What changes when the SQLite cache is deleted, and why does the next run request locations again?

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.

Key point

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

  • Common mistake

    Assuming every input location causes a new API request.

    What it looks like

    A second run contains the same ten locations as the first run.

    Why it fails

    geoload.py checks geodata.sqlite first and skips the API call when a result is already cached.

    Do this instead

    Distinguish between reading and checking a location and actually sending a new request.

  • Common mistake

    Ignoring the request counter for a large dataset.

    What it looks like

    Trying to process thousands of locations in one run without limiting calls.

    Why it fails

    Geocoding APIs commonly impose daily rate limits.

    Do this instead

    Set a per-run counter and process uncached locations in batches across multiple runs.

  • Common mistake

    Deleting geodata.sqlite without expecting re-geocoding.

    What it looks like

    Removing the database and assuming the next run will still know which locations were completed.

    Why it fails

    Removing the file erases all stored results.

    Do this instead

    Delete the file only when a full reset is intended, such as correcting suspect cached data, changing services, or testing.

Check Your Understanding

MEDIUM

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?

  • Ten new API calls are made

  • The ten cached results are reused and no new API calls are needed for them

  • The database is automatically deleted

  • The program must wait for the daily limit to reset

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

  1. 1
    SQLite provides a local cache for geocoding results, so repeated locations do not require repeated API calls.
  2. 2
    geoload.py reads a location, checks the cache, requests data only for an uncached location, and stores the new result.
  3. 3
    A request counter limits calls in one run, allowing large datasets to be processed in batches over several days.
  4. 4
    Removing geodata.sqlite clears every stored result; the next run creates a new database and re-geocodes locations from scratch.
  5. 5
    The cache-and-reuse pattern supports repeatable external-data workflows and prepares stored results for later data use.

At a glance

  • 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.

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