I Rewrote a Java Microservice in Rust — and Lost My Job
It started as a weekend experiment. It ended with a meeting invite titled: “Quick Sync — Org Update.” That’s the story of how I tried to be the “10x engineer” who optimized everything — and accidentally optimized myself out of employment. Let’s rewind a bit.
1. The Boring Java Service That Worked Just Fine We had this tiny Java Spring Boot service that handled about 15,000 requests per minute. Its job? Parse a payload, validate it, save a record, and respond with a 200. Nothing fancy. The original code looked like this: @RestController public class IngestController {
@PostMapping("/ingest")
public ResponseEntity<String> ingest(@RequestBody Payload payload) {
if (payload.getId() == null) {
return ResponseEntity.badRequest().body("Invalid payload");
}
repository.save(payload);
return ResponseEntity.ok("Saved");
}
} It was simple, reliable, and utterly uninteresting. Except for one thing: latency. On average, the endpoint took ~120ms per request. Our CTO wanted it below 80ms. So I thought — how hard could it be to beat Java with Rust?
2. The Rust Rewrite: My Brilliant Weekend Project I’d been playing with Rust for months — systems-level performance, zero-cost abstractions, ownership model, yada yada. So, one Friday night, I cloned the repo, opened a new folder named ingest-rs, and started rewriting. In Rust, the same thing looked lean and clean:
- [post("/ingest")]
async fn ingest(payload: web::Json<Payload>) -> impl Responder {
if payload.id.is_none() {
return HttpResponse::BadRequest().body("Invalid payload");
}
let saved = save_to_db(&payload).await;
match saved {
Ok(_) => HttpResponse::Ok().body("Saved"),
Err(_) => HttpResponse::InternalServerError().finish(),
}
} Compiled fast. Ran even faster. No garbage collector, no runtime overhead, no endless dependency hell. Or so I thought.
3. The Benchmark That Fooled Me I benchmarked it locally with wrk: $ wrk -t8 -c256 -d30s http://localhost:8080/ingest Results: | Metric | Java | Rust | | ----------- | ----- | ---- | | Avg Latency | 118ms | 46ms | | Req/sec | 15k | 37k | | Memory | 450MB | 92MB | I was ecstatic. Rust crushed it. I even sent a Slack message with “🚀 3x faster after rewrite!” (yeah, cringe). By Monday, I had a demo ready for the team.
4. The Deployment That Changed Everything We deployed it to staging. Everything seemed perfect. Until… the logs exploded. Turns out, the Rust version didn’t handle half-open TCP connections like the Java Netty stack did. Our load balancer was retrying requests, causing duplicate inserts. Then our database CPU shot up 80%. Then cache miss rate doubled. Then the message queue lagged by 10 minutes. My “optimized” microservice had just broken the production pipeline.
5. The Hidden Cost of Rewrites When we started investigating, we realized:
- The Java version used connection pooling tuned for our ORM.
- The Rust one? Opened a new DB connection per request (rookie mistake).
- The Java GC pauses we complained about? Barely 2ms under load.
- The Rust async executor I used (Tokio) was fine — until it spawned too many tasks for I/O-bound workloads.
And here’s the painful part: Nobody else on the team knew Rust. My manager asked me the worst possible question: “If this crashes in production at 3 AM, who can fix it besides you?” I didn’t have an answer.
6. The Diagram of My Downfall
┌────────────┐
│ Client │
└──────┬─────┘
│
HTTP Request
│
┌────────▼────────┐
│ Rust Microservice│
│ (Actix / Tokio) │
└────────┬────────┘
│
DB Conn 🔥 every request
│
┌────────▼────────┐
│ PostgreSQL DB │
└─────────────────┘
In short:
- I saved milliseconds.
- I lost maintainability.
- And I broke production.
7. The Meeting That “Quick Sync — Org Update” wasn’t about restructuring. It was about accountability. My manager wasn’t angry; he was tired. He said something that stuck with me: “We optimize for teams, not benchmarks.” Two weeks later, the Java service was rolled back. My Rust code was archived. And my name quietly disappeared from the service owner list.
8. The Lesson I Learned (the Hard Way) If you take away one thing from this story, let it be this: Don’t rewrite production systems in new languages just because benchmarks look sexy. Engineering isn’t about speed; it’s about sustainability. The right tool isn’t the fastest one — it’s the one your team can maintain, debug, and trust at 3 AM. Rust is amazing. But so is keeping your job.
9. The Benchmark Revisited (in Production Reality) After rollback, we profiled the original Java service properly: | Component | Time (ms) | Note | | --------------- | --------- | --------------- | | Request parsing | 5 | negligible | | Validation | 15 | custom logic | | DB I/O | 80 | main bottleneck | | Response | 5 | OK | Result: 78ms average latency — without rewriting a single line in Rust.
10. What I’d Do Differently Today If I faced the same problem again, I’d:
- Profile before rewriting. Never optimize blind.
- Prototype, don’t replace. Test new stacks in isolation first.
- Bring the team along. Teach, document, review.
- Measure total cost, not just performance. Hiring, tooling, on-call readiness — they all count.
- Remember: Fast code that no one can maintain is slow progress.
Hand-Drawn Style Architecture — The Real Fix
┌────────────┐
│ Clients │
└──────┬─────┘
│
┌──────▼──────┐
│ Spring Boot │
│ Ingest API │
└──────┬──────┘
│
┌───────────▼────────────┐
│ Async Queue / Redis │ ← Added buffering here
└───────────┬────────────┘
│
┌─────▼──────┐
│ PostgreSQL │
└────────────┘
Simple. Scalable. Boring. And that’s a good thing.
Final Thoughts If this story sounds like I’m bitter — I’m not. Rust is still my favorite language. But I learned that engineering is about trade-offs, not hero moves. My Rust rewrite made the service faster — but it made the team slower. And in a company, team speed > code speed. So yeah, I rewrote a Java microservice in Rust. And I lost my job. But I gained something better: a healthy respect for boring code that just works.