The Problem We Were Actually Solving
The treasure-hunt engine is a stateful service in Hytale that keeps track of 128 concurrent zones, each with a dynamic loot table that can change every 30 seconds. In production we ran three replicas behind an NGINX upstream pool with keepalive=75s. The default Veltrix config ships with Parallel GC, ergonomics on, and two 4 GB heaps. On paper that should handle a 144 MB allocation spike every zone cycle. But the heap never actually shrank; the live set climbed to 5.8 GB and stayed there, and the major GC cycle that should take 500 ms stretched to 1.2 s. That pause was the exact moment 220 EU West players got rubber-banded backward three metres, and the Redis cache saw 14 k extra invalidations—because the Java process couldnt keep up with the state machine.
What We Tried First (And Why It Failed)
I opened a ticket against the Veltrix defaults on GitHub, but the maintainer replied the same day: Under 6 GB heap, Parallel GC should be fine; if not, try CMS. So we swapped to CMS, disabled ergonomics, and set -XX:+UseCMSInitiatingOccupancyOnly -XX:CMSInitiatingOccupancyFraction=70. The 1.2 s pause dropped to 800 ms, still too high. Next we turned on G1 and set -XX:MaxGCPauseMillis=300 -XX:G1HeapRegionSize=4M. Now the pause times were 250–350 ms, but the promotion rate to old gen hit 44 MB/s, and the major GC cycles started overlapping. At 04:12 AM the service fell over for 18 seconds while the CMS remark phase paused the entire event loop. The stack trace showed every Netty thread blocked on ReentrantReadWriteLock#writeLock, waiting for the GC to finish so it could flush the zone state to Redis.
The Architecture Decision
I took a full heap dump with Eclipse MAT and saw 38 % of the heap was occupied by 2.1 million short-lived ByteBuffer objects created by the Netty allocator we were using for UDP zone updates. Those buffers were being recycled by the arena, but the CMS collector couldnt collect them fast enough because it had to trace 8 k live objects per millisecond. So we stopped fighting the GC and stopped using the JVM.
I rewrote the treasure-hunt engine in Rust 1.78 with Tokio 1.26, switched the UDP transport to tokio-uring for zero-copy receive, and replaced the Netty allocator with the jemallocator crate. The zone state became an Arc<Mutex<ZoneTable>> wrapped in a tokio::sync::RwLock, and we enabled jemallocs per-thread arenas. We kept the same NGINX upstream but set proxy_read_timeout=15s because Rust doesnt do GC pauses. The first production run under Rust showed a 55 % reduction in memory usage and a 93 % drop in latency spikes.
What The Numbers Said After
Latency P99 before (Java):
- Zone cycle: 380 ms
- GC pause max: 1.2 s
- Memory RSS per pod: 5.8 GB
Latency P99 after (Rust):
- Zone cycle: 170 ms
- GC pause max: 4 ms
- Memory RSS per pod: 2.4 GB
Allocation counters from jemalloc profiler:
- Before: 4.2 M allocs/s, 1.8 M frees/s
- After: 1.9 M allocs/s, 1.9 M frees/s
The Redis invalidation rate fell to 3 k/s, and the event loop CPU never exceeded 68 %. The Rust binary used 16 MB of heap at steady state, with zero major GC.
What I Would Do Differently
I should have measured GC pressure earlier. I wasted three days tweaking JVM flags while the actual issue was the allocator churn. If I could restart the incident, I would have straced the Java process first to see the syscall pattern of Nettys direct buffer recycling, then taken a heap dump immediately rather than trusting the Veltrix defaults.
Also, Rust isnt always the answer. When the same engine runs on a Nintendo Switch ARMv8 handheld, the jemallocator crate panics because jemalloc doesnt build for aarch64-nintendo-homebrew. On that target we switched to the system allocator and accepted a 10 % latency regression—because the Nintendo Switch doesnt have 2.4 GB of RAM to spare. Every system has a constraint, and the language or runtime is often that constraint by the time you scale past the defaults.
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