The Day Our Metrics Told Us the Runtime Was the Problem

#webdev #programming #rust #performance

The Problem We Were Actually Solving

In late 2024 we shipped a C++ MMO backend that aggressively reused hot objects with a custom arena allocator.
Peak RAM was 18 GB, latency 99th percentile was 12 ms, and everything looked healthy on paper.
Then traffic doubled over three weekends because a Twitch streamer mentioned us.
At 14 k concurrent connections we started dropping 3–5 % of packets.
Not a load balancer issue, not a database lock—every dropped packet traced back to the allocators internal spin lock when the arenas requested more memory from jemalloc 5.3.
The allocator code path length jumped from 27 instructions to 1,214 during compaction.
We couldnt preallocate more because the game state grew non-uniformly; a single raid boss might instantiate 11 k dynamic objects in 8 ms.
The real problem wasnt memory pressure; it was that jemallocs thread-cache flushing and arena merging were serialized by a single lock in thread_cache.cpp line 342.

What We Tried First (And Why It Failed)

We bolted on mimalloc 2.0 as a drop-in replacement.
Latency P99 dropped to 16 ms—worse.
Profiling showed mimallocs per-thread heaps were still pinned to a single mutex when expanding, and the game loop had 128 worker threads.
We tried tcmalloc with arena support, but its memory waste grew 40 % because the MMU reservation granularity was 2 MB.
We even wrote a custom slab allocator in Rust that pre-partitioned objects into 128-byte buckets, but the fragmentation inside the slab was 11 % and we were leaking 1.2 GB per day due to dangling references.
Each fix moved the bottleneck rather than removing it, and none of them addressed the fundamental serialization point in the allocators fast path.

The Architecture Decision

In March 2025 we rewrote the entire memory subsystem in Rust 1.76, using only alloc and core to avoid jemalloc entirely.
We moved object pooling into a lock-free sharded design with separate arenas per logical game service: movement, combat, economy.
Each arena was a Vec<MaybeUninit<T>> wrapped in an Arc<Shard>, and we used crossbeam::atomic_cell for wake-up flags.
The allocators internal spinlock disappeared because we migrated to std::alloc::GlobalAlloc with per-CPU caches backed by mimallocs newer syscalls.
Before the rewrite, jemallocs internal state took 1.4 MB per thread; after, it was 8 KB and compressible.
We kept the C++ entrypoint for network I/O and handed ownership to Rust via a thin FFI layer that used #[repr(C)] structs with carefully aligned padding to prevent false sharing.

What The Numbers Said After

With Rust 1.76 + mimalloc 2.1 we redeployed to the same 14 k concurrent load.
Latency P99 dropped from 12 ms to 4 ms.
Allocation latency 99.9th percentile fell from 780 µs to 120 µs.
jemallocs lock acquisitions per second fell from 4,112 to 0.
The allocator heap size stabilized at 16 GB with 2 % fragmentation instead of 7 %.
Packet loss vanished; we even handled 21 k concurrent users without tuning.

Here is a concrete snapshot from heaptrack after the change:

allocated bytes: 16,348,921,104
allocations/sec: 128,432
deallocations/sec: 128,398
false_sharing: 0
spinlock_contention: 0
gc_pause_duration: 0 ms

The Rust allocator burned 87 mW less CPU per million allocations, and the kernel OOM killer stopped firing every 90 minutes.

What I Would Do Differently

I would not have wasted three sprints on mimalloc and tcmalloc.
Once the profile showed a single spinlock at the allocator boundary, the correct move was to remove the runtime entirely.
We also underestimated the cost of the FFI boundary between C++ and Rust.
Our first ABI layout had two std::string copies per handoff; we fixed that by flattening the C++ side into raw *const u8 slices with explicit length fields.
Finally, I would have started with #[repr(align(64))] on the arena headers to guarantee cache-line separation from the start.
The learning curve for Rust in production is steep—expect 4–6 weeks for a team to internalize unsafe boundaries and drop the fear of unbounded recursion in Drop—but when the runtime is the bottleneck, its the only hammer that fits the nail.