We romanticize big engineering wins.
The performance chart that hockey-sticks up. The keynote slide with the impossible number. The "we hit a billion requests per second" humble brag.
The truth behind those moments is never one big breakthrough. It's dozens, possibly even hundreds, of small changes and enhancements. The kind that look ordinary when they land in a PR. The kind that your eyes casually skip over in release notes. The kind nobody celebrates… until they compound into something revolutionary.
I was recently reminded of this while listening to the Cache It podcast. Valkey project maintainer Harkrishn Patro described how his team scaled their system to handle a billion requests per second. He wasn't describing a feature launch, but rather the result of continuous refinement across every corner of the system.
Engineers, myself included, often hone in on small pieces of a system and lose sight of the forest for the trees. But with bold headlines and sensational marketing, we forget the forest is made from trees. As a result, major innovations overshadow the hard work that was done in the years and months leading up to "the big reveal."
So what happens if we take a stroll through that forest to see what the big announcement was really about?
Innovation removes friction
We tend to think innovation shows up wearing a cape. A disruptive new framework. A patented algorithm. A major architectural redesign. But most real innovation is far less glamorous – it's the process of shaving off tiny sources of drag until the entire system starts to glide. The "death by a thousand paper cuts" phenomenon is real.
Distributed systems make this painfully clear. The moment you push scale beyond "normal," you reveal dozens of inefficiencies that rarely show up in happy-path benchmarks. And this doesn't just refer to hyper scale! Problems change at every tier of scale. From POC to startup. From startup to regional. From regional to global. Suddenly, small issues aren't small anymore, they're multiplied across thousands of functions, nodes, and containers processing millions of requests per second.
That's exactly what the Valkey team discovered while pushing cluster sizes past limits that were never originally intended. Instead of rewriting the system from scratch, they focused on identifying and removing friction one piece at a time. For example, early pub/sub workloads broadcast messages to every node in the cluster, regardless of who needed the data. Reducing that via sharded messaging meant drastically less traffic flowing through the cluster bus, which freed up capacity for the work that mattered.
Or the size and frequency of internal coordination messages. By making those packets smaller and more efficient, Valkey stopped wasting cycles on communication overhead, freeing up capacity for serving actual requests.
Individually, neither of these changes would get a developer on stage at a conference. Nobody ships a "lighter gossip protocol!" feature release. Stack those improvements long enough, and the system starts performing at a level that once felt impossible.
Innovation isn't about adding more. It's about taking away.
When you remove enough drag, the system suddenly feels like it's leaping forward… even though it got there one subtle change at a time.
Reliability is a force multiplier
A fast system under perfect conditions is just a prototype. The moment you introduce chaos, like what happens regularly in production, performance either collapses or compounds. Reliability determines which way it goes.
Just like removing friction creates capacity, improving reliability creates opportunity. In distributed systems, fragility multiplies just as quickly as performance. One node degrades, then another, and suddenly a perfectly healthy cluster wastes capacity on recovery instead of serving requests. Failover becomes a performance bottleneck waiting to happen if handled poorly.
Back to the small wins with Valkey – when multiple primaries failed simultaneously (like during an Availability Zone outage), the system would struggle to elect new leaders because candidate requests flooded in all at once. Leadership battles caused downstream stalls that made the whole cluster feel unstable under pressure. The solution was introducing strategic delays to election behavior, allowing replicas to take over cleanly and confidently without overwhelming the system.
But that's the pattern – resilience opens up throughput. A cluster that can route around failure without spiking CPU or tail latency stays fast when users need it most. In Valkey's case, the additional throughput in unfavorable situations kept cycles free to handle more requests per second. Another blip on the release notes that continued to add up to the breakthrough.
Reliability amplifies every other improvement in the system. It creates breathing room. It enables aggressive scaling. It gives teams the confidence to grow beyond today's limits. When recovery becomes routine and uneventful, progress accelerates.
Scaling is an operational behavior
Contrary to what you might think, scaling isn't a hardware problem. It's a behavior problem. If changing scale feels risky, disruptive, or expensive, teams simply don't do it – even when they need to. They take the safe route: over-provisioning up front and praying they don't get surprised later.
There's nothing innovative about survival mode.
Real scalability emerges when the cost of adjusting capacity falls so low that it becomes a non-event (that's one of the reasons people like serverless so much). The faster and safer adding nodes, redistributing data, or shifting workload patterns feel, the more frequently teams perform them. And the more frequently teams perform them, the closer infrastructure can track real-world demand without waste, stress, and cost.
Once again, the Valkey team learned this firsthand. Since Redis 1.0, data migration has been a scary operation. Moving slots between nodes meant risking performance hits, stalled operations, or complicated (and error prone) manual intervention. So people avoided it, and clusters either stayed small or stayed oversized. Innovation would stall because operating it at its potential felt like walking a tightrope.
Atomic slot migration changed the behavior. Now, moving data around a cluster is predictable, controlled, and boring – exactly what operators need when managing production systems. This feature opened up capabilities that already existed but weren't safe to reach for.
When scaling becomes effortless, teams stop treating it like a last resort and start doing it as a reflex. Progress begins to compound. Capacity and confidence reinforce one another. And suddenly, what once felt like a milestone becomes just another Tuesday.
Breakthroughs happen gradually… then suddenly
From the outside, breakthroughs look like a leap. One day the headline appears. The graph turns vertical. Everyone wonders how a system could possibly jump from "pretty good" to "borderline unbelievable" in such a short time.
But from the inside, it looks like months, sometimes years, of sanding down rough edges. Fixing the tiny things that don't feel worthy of a celebration. Optimizing behavior that only shows up in profiling. Eliminating friction one pull request at a time.
That's exactly what happened with Valkey. A billion RPS was never the goal, but it was the receipt of countless invisible improvements. The milestone only looks magical because we didn't see the work it took to make it boring.
And this isn't unique to distributed systems or caching engines. It's universal. Every major engineering accomplishment is built from the moments where someone looked at a sharp corner and decided it didn't have to stay sharp.
Progress compounds. Confidence compounds. Capability compounds. Breakthroughs happen gradually… then suddenly.
So if you're in the middle of the grind, whether you're shipping incremental changes, cleaning up edge cases, or improving reliability and operability in small ways, take a breath of solace. You're stacking. You're creating the conditions where something extraordinary is going to look effortless.
The day is coming when the big announcement arrives, the system holds steady, and people assume it was easy. But you'll know better. You'll know it was earned one small improvement at a time.
Happy coding!
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