Your API Is Returning Too Much Data And It’s Costing You Performance
A developer writing on Dev.to on the 21st of March made a case that's unglamorous but quietly important. The argument is this: most APIs return far more data than their clients ever use, and every surplus field is a small tax — serialisation time, wire bytes, parse overhead — that compounds quietly until someone notices a performance cliff and starts asking uncomfortable questions about how the API was designed in the first place. The post scored a 7.0, which for Dev.to represents modest but genuine traction. What the previous version of this story identified as the piece's real strength holds: it isn't evangelising GraphQL or preaching REST best practices from on high. It's making the more grounded observation that the gap between what an API *can* return and what any given client *actually needs* tends to widen with every new feature, every new consumer, every endpoint that was easier to expand than to reason about. Field filtering, sparse fieldsets, gateway-layer response shaping — the solutions on offer are familiar. The post's value is in naming the cost clearly rather than in proposing anything novel.
If confirmed as a pattern worth taking seriously, here is what this means. Engineering teams running microservices or BFF layers are likely already absorbing these costs without attributing them correctly — blaming network latency or database query time when the real culprit is a bloated response that nobody ever went back to trim. At scale, the compounding effect is real: mobile clients burning unnecessary bandwidth, edge caches storing responses that are 40% irrelevant data, downstream parsers doing work that serves no one. The second-order effect is organisational as much as technical. Teams tend to add fields to API responses without ceremony and remove them with great reluctance, because removal is a breaking change and addition never feels like one. That asymmetry is where the debt accumulates.
Watch for follow-on posts, benchmarks, or tooling discussions that put numbers to the overhead — specific payload size comparisons or latency deltas would shift this from plausible observation to actionable evidence.
<p>I do a bunch of small things on the side. I wrote a website for a friend's thesis, took on a couple of commissioned full-stack jobs, and I have one actual side hustle that's
<p><em>Part I of the series "Don't Let the User Wait - Render Quickly to Keep Their Attention."</em></p> <h2> The problem hiding inside SSR </h2> <p>Server-Side Rendering promises
<p>In <a href="https://amanksingh.com/blog/production-security-checklist" rel="noopener noreferrer">A Production Security Checklist</a>, I walked through what to lock down before
<p>When you’re building a hobby project, Vue’s reactivity feels like magic. But when that project hits 100,000 concurrent users, that same magic can turn into a memory-leaking
<h2> Why I built it </h2> <p>For one of our Minecraft community projects I needed structured data: HP values, drop tables, spawn biomes, biome temperatures, enchantment max
<blockquote> <p>I created a new website: <a href="https://programmingcentral.vercel.app/books/typescript/" rel="noopener noreferrer">Free Access to the 8 Volumes on Typescript
<p>As engineers, we spend a lot of time optimizing our origin servers. We scale them up, add more instances, and fine-tune our database queries. But what if the biggest
<p>Every backend engineer has heard the advice: add a cache. It will make your system faster. And they are right, it will. But a cache that is not properly understood is one of
NewsHive monitors these sources continuously. All signal titles above link to the original reporting.
Intelligence by NewsHive. Need help navigating what this means for your business? Contact GeekyBee →