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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