When an API feels “slow”, it’s rarely one thing. It’s usually too much data, too many calls, or too much work per request. Here are practical levers that scale. 👇
🔸 TL;DR
▪️ Return only what’s needed
▪️ Keep APIs stateless to scale horizontally
▪️ Paginate big collections (and control “count”)
▪️ Reduce heavy payloads (encoding + compression + caching)
▪️ Cache smartly (server + client)
🔸 ONLY WHAT YOU NEED (LESS DATA, LESS WORK)
▪️ Avoid “god endpoints” returning everything “just in case”
▪️ Add optional fields/params (fields=..., include=...) to let clients choose and filetring even more
▪️ Create a dedicated endpoint for expensive data retrieval (opt-in), instead of making every request pay the cost 💸
▪️ Bonus: split “summary vs details” responses (fast list, rich item)
🔸 STATELESSNESS (DEFINE IT + WHY IT’S FAST)
Stateless = the server doesn’t store client session state between requests. Each request contains what it needs. ✅
▪️ Makes horizontal scaling easy: add more instances behind a load balancer
▪️ Reduces sticky sessions and hidden memory costs
▪️ Improves resiliency: any node can serve any request
🔸 LIMIT LARGE COLLECTIONS (PAGINATE LIKE A PRO)
▪️ Always paginate lists (limit/offset or better: cursor pagination) 📄
▪️ Offer specific queries (filter/sort/search) so users don’t fetch the world to filter client-side
▪️ For UIs: consider 2 calls
1️⃣ a cheap count call
2️⃣ and only if the count is reasonable → allow the data call
▪️ Put hard caps (maxLimit) to prevent accidental “download the database” moments 😅
🔸 OPTIMIZE LARGE OBJECTS (PAYLOADS CAN BE THE BOTTLENECK)
▪️ Use the right encoding: avoid over-nesting; consider lighter representations (avoid JSON for binary payload)
▪️ Enable compression (gzip/br) especially for JSON/text 📦
▪️ Cache heavy objects (or precompute) when they’re requested often
▪️ If payloads are huge: consider partial responses, streaming, or async export
🔸 CACHING (DO IT INTENTIONALLY)
▪️ Cache what’s read often and changes rarely
▪️ Use eviction strategies:
1️⃣ LRU (Least Recently Used) 🧠
2️⃣ LFU (Least Frequently Used) 📊
▪️ In Spring: @Cacheable for read paths (and don’t forget invalidation with @CacheEvict)
▪️ Put TTLs everywhere: stale data is sometimes worse than slow data ⚠️
🔸 CACHING ON CLIENT SIDE (YES, IT COUNTS)
▪️ Use HTTP caching properly: ETag, If-None-Match, Cache-Control 🔁
▪️ Let clients reuse responses when safe (especially for reference data)
▪️ Great for mobile apps and high-latency networks
🔸 TAKEAWAYS
▪️ Speed is often “less work” + “less data”, not “more CPUs”
▪️ Pagination + selective fields prevent accidental performance disasters
▪️ Stateless + caching is the classic combo for scale
▪️ Optimize payloads before over-optimizing code
#API #Performance #Backend #Microservices #SpringBoot #Caching #Scalability #REST
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