Caching for Optimizing Google Maps API

⚠ Important: Before implementing caching, consult with your legal team to ensure compliance with Google Maps API's Terms of Service. Lunar.dev provides a platform for building API middleware and tools to work within API provider boundaries, but it is the user's responsibility to enforce compliance correctly.

Use Cases

• Reduced Latency – Serve responses faster by avoiding redundant API calls for frequently accessed data.
• Cost Optimization – Reduce API call costs by minimizing requests to third-party providers.
• Failover Support – Provide cached data when APIs are unavailable or experiencing downtime.

Cache Key Differentiation – Store responses based on request parameters, headers, or API keys for more granular caching.

Solution Structure

Step-by-Step Breakdown

1. Request Interception – The middleware captures incoming requests before they are sent to Google Maps API.
2. Cache Lookup – Checks if a response exists in the cache based on a computed cache key (e.g., request URL + query parameters + headers).
3. Cache Hit Handling – If a matching entry is found, the cached response is returned instantly.
4. Cache Miss Handling – If no cached entry exists, the request is forwarded to Google Maps API, and the response is stored in the cache.
5. Expiration & Invalidation – Cached responses are stored with a TTL (time-to-live). Old entries are evicted based on expiration policies or explicit invalidation.

How It Works Together

Caching reduces redundant API calls by storing frequently accessed responses in-memory. When a request is made, the middleware first checks if an identical request has been cached. If found, the stored response is returned, eliminating the need for an external API call. If not, the request proceeds to Google Maps API, and the response is cached for future use. This optimizes performance, reduces costs, and adds resilience against outages.

Concrete Example

Problem:
A ride-hailing app frequently requests geolocation data from Google Maps API, often for the same locations. Each request incurs latency and API charges. Google Maps API enforces rate limits (e.g., 50 requests/sec) and per-call pricing ($5 per 1,000 geocoding requests). Without caching, costs and response times scale unnecessarily.

Solution:
Introduce a caching layer to store responses for repeated geolocation queries. Cached responses are indexed using a cache key combining the request URL, query parameters, and relevant headers.

Caching Flow YAML Configuration

The following configuration sets up a caching flow for Google Maps API using Lunar.dev:

yaml
CopyEdit
endpoints:
  - url: "https://maps.googleapis.com/maps/api/geocode/json"
    method: GET
    remedies:
      - name: Smart Caching
        enabled: true
        config:
          caching:
            ttl_seconds: 86400  # 24-hour cache expiration
            cache_key_format: "{url}-{query_params}-{headers['User-Agent']}"  # Define cache keys
            store_response_body: true  # Store full API response
            max_record_size_bytes: 1048576  # (Optional) Max 1MB per cached entry
            max_cache_size_megabytes: 100  # (Optional) Max 100MB total cache size

Explanation:

• Requests to Google Maps API’s geocoding endpoint are intercepted.
• Cached responses are indexed using URL, query parameters, and headers, ensuring that different queries don’t overwrite each other.
• Cached entries expire after 24 hours to prevent stale data.
• The middleware stores full responses to serve repeated requests instantly.
• Optional size limits help manage cache memory usage.

This approach reduces API costs and latency, ensuring that frequently requested geolocation data is quickly available while staying within API limits.

Final Thoughts

Caching is a powerful tool to optimize Google Maps API usage, reducing API costs, response times, and dependency on third-party availability. However, always review the API provider’s Terms of Service to ensure compliance. By using Lunar.dev's middleware, developers gain flexible caching controls while maintaining full responsibility for enforcing API policies correctly.