How to Optimize OpenStreetMap Data Fetching with `unified_osm_request` in prettymaps

The unified_osm_request function in prettymaps/fetch.py centralizes all OpenStreetMap data retrieval with built-in caching, query normalization, and rate-limit handling to eliminate redundant network calls.

The prettymaps library transforms geographic data into artistic map visualizations. At the core of this process lies a single fetch utility that dictates performance: unified_osm_request. Understanding how to leverage this function and its caching infrastructure is essential for building fast, repeatable map generation workflows.

What unified_osm_request Does

Located in [prettymaps/fetch.py](https://github.com/marceloprates/prettymaps/blob/main/prettymaps/fetch.py), this helper consolidates four critical responsibilities:

1. Query normalization — converts bounding boxes, center-point/radius pairs, or custom Overpass QL into standardized API calls
2. Disk caching — stores JSON responses keyed by query hash via [prettymaps/utils.py](https://github.com/marceloprates/prettymaps/blob/main/prettymaps/utils.py)
3. Rate-limit compliance — implements exponential backoff for Overpass API throttling
4. Uniform data structure — always returns a dictionary with nodes, ways, and relations keys

Because draw.py, gpx.py, and the CLI in [app.py](https://github.com/marceloprates/prettymaps/blob/main/app.py) all route through this single entry point, optimizations here propagate throughout the entire codebase.

Performance Optimization Strategies

Aggressive Caching for Repeated Regions

The cache eliminates network latency on subsequent runs. The same region request drops from seconds to milliseconds.

from prettymaps.fetch import unified_osm_request

bbox = (-43.21, -22.96, -43.20, -22.95)

# First call: downloads from Overpass API

osm_data = unified_osm_request(bbox=bbox)

# Second call: serves from disk cache instantly

osm_data_cached = unified_osm_request(bbox=bbox)

Custom Cache Directory Locations

Override the default cache path for CI pipelines, containerized deployments, or shared environments.

import os
from prettymaps.utils import set_cache_dir
from prettymaps.fetch import unified_osm_request

set_cache_dir(os.path.expanduser('~/my_prettymaps_cache'))
osm_data = unified_osm_request(bbox=(-43.21, -22.96, -43.20, -22.95))

Query Minimization with Custom Overpass QL

Strip unused tags to reduce payload size. Pass raw Overpass queries when you need precise control.

from prettymaps.fetch import unified_osm_request

custom_query = """
[out:json][timeout:25];
(
  way["highway"]({{bbox}});
  node["amenity"="cafe"]({{bbox}});
);
out body;
>;
out skel qt;
"""

osm_data = unified_osm_request(
    custom_query=custom_query,
    bbox=(-43.21, -22.96, -43.20, -22.95)
)

Radius-Based Fetching for Circular Regions

Use center-point coordinates with meter radius for circular study areas instead of computing bounding boxes manually.

from prettymaps.fetch import unified_osm_request

center = (-43.2096, -22.9515)  # (longitude, latitude)

radius = 500  # meters

osm_data = unified_osm_request(center=center, radius=radius)

How the Cache System Works

The caching layer in [prettymaps/utils.py](https://github.com/marceloprates/prettymaps/blob/main/prettymaps/utils.py) provides:

• Hash-based keys — deterministic cache filenames derived from query parameters
• Filesystem storage — JSON responses serialized to disk
• Cache management — set_cache_dir(), cache_path(), and related helpers

This design enables parallel processing without duplicate downloads. Multiple processes invoking unified_osm_request simultaneously on the same region coordinate through the filesystem cache.

Architecture Benefits of Centralized Fetching

Component	Consumes unified_osm_request	Optimization Benefit
draw.py	Map rendering	Eliminates refetch on style tweaks
gpx.py	GPX export	Reuses same cached data
app.py	CLI interface	Fast iterative development
User scripts	Custom workflows	Network isolation for testing

Summary

• unified_osm_request in prettymaps/fetch.py is the sole gateway for all OpenStreetMap data retrieval
• Disk caching by query hash eliminates redundant network calls across the entire library
• Custom cache directories support deployment flexibility and CI optimization
• Custom Overpass queries allow payload minimization for faster transfers
• Parallel safe design enables multi-region processing without duplicate downloads

Frequently Asked Questions

Where is the cache stored by default?

The default cache location depends on your operating system, typically resolving to a user-level application data directory. Use set_cache_dir() from prettymaps/utils.py to relocate it explicitly.

Can I use a private Overpass instance instead of the public API?

Yes. The unified_osm_request function can target alternative endpoints. Modify the base URL in your call or environment configuration to route queries to a self-hosted Overpass server.

How do I clear the cache to force fresh data?

Delete the cache directory contents directly. The hash-based filenames allow selective removal, or wipe the entire directory for a complete reset. The next request will repopulate with fresh Overpass data.

Does caching work across different prettymaps versions?

Cache compatibility depends on the internal data structure. If the library changes how it parses Overpass responses, cached files from older versions may require regeneration. Monitor release notes for breaking changes in the fetch layer.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}