Optimizing Stripe API performance in Lambda with caching strategies

Summary

The post addresses performance, cost, latency, and rate-limiting challenges that arise when applications make high-volume requests to Stripe's API from AWS Lambda. It presents a two-level cache using ElastiCache for Redis as the first layer and DynamoDB as the durable second layer, with Lambda coordinating cache reads, Stripe retrieval, and writes. On a miss, the function checks Redis, then DynamoDB, fetches the customer from Stripe if necessary, and stores the response in both layers with one-hour Redis and 24-hour DynamoDB TTLs. The DynamoDB design uses composite keys, a GSI for cache-type and timestamp queries, TTL cleanup, and environment-specific capacity settings; the post also discusses Redis connection pooling, Lambda sizing, capacity planning, cost control, monitoring, and webhook-based invalidation.

Context

High-volume interactions with Stripe's API can encounter rate limits and 429 errors, API latency typically ranging from 100ms to 500ms, and increased AWS infrastructure costs from unnecessary calls.

Approach / What changed

Use AWS Lambda to coordinate a multi-layer cache: check ElastiCache for Redis first, then DynamoDB, and finally Stripe on a cache miss. Store newly fetched data in both layers, using Redis for fast access and DynamoDB for durable storage, TTL management, flexible key designs, and cache maintenance queries.

Takeaways

• Redis is checked before DynamoDB, while Stripe is called only when the requested data is absent from both cache layers.
• The example assigns cached data a one-hour Redis expiry and a 24-hour DynamoDB TTL, with DynamoDB configured for automatic expired-item cleanup.
• The design uses DynamoDB composite keys and a GSI to support multiple item types, stale-entry queries, and bulk cache invalidation.

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