SynCache stores your cache directly in process memory — eliminating the network round-trip on every read. A lightweight broker keeps all your instances in sync, so you get the speed of local RAM with the consistency of a distributed system.
Leading caching solutions require a network call for every single cache operation. SynCache eliminates that entirely — reads come straight from the same heap your application runs in.
No TCP, no kernel syscalls, no context switches. Every remote cache GET forces your application to drop into kernel mode — the CPU saves registers, hands off to the TCP stack, waits for a network round-trip, and climbs back out. SynCache skips all of that: reads are a plain hash lookup inside your own process heap, entirely in user space. The CPU never leaves user mode, and there is no syscall to pay.
When any instance calls set(), every other instance gets the updated value. When it calls evict(), every other instance drops it. No pub/sub setup, no manual cache busting, no stale reads — consistency across your entire cluster happens automatically as a side effect of normal cache usage.
On Spring Boot, switching to SynCache is two lines in application.yaml — set type: syncache and add your token. Your existing @Cacheable, @CachePut, and @CacheEvict annotations work as-is. Every integration is built around each language's native idioms, so you adopt SynCache by adding a dependency, not rewriting your caching logic.
Many teams think the win of a remote cache is that it moves data from disk to memory. But leading cache solutions are already in-memory — that speed gap is already closed. The tax that remains is the TCP round-trip on every GET: your app drops into kernel mode to issue a syscall, the kernel TCP stack serializes a packet, it crosses the network, the cache server processes it, and the reply makes the same trip in reverse. At a million reads per second, those round-trips add up to seconds of pure overhead every hour — just waiting for I/O that carries data you could have read from local memory. SynCache removes that cost entirely on reads. Writes still sync through the broker so your cluster stays consistent — you just stop paying the per-read tax.
Every remote cache read has two costs. The industry spent a decade optimizing the smaller one.
Moving data from disk to RAM shrinks the storage slice — but that slice was never the bottleneck. Every remote cache still requires a TCP round-trip on every single read. Your app drops into kernel mode, the kernel TCP stack serializes a packet, it crosses the network, the cache server processes the request, and the reply travels back the same way. At a million reads per second that overhead accumulates into real seconds of pure I/O wait every hour — and it's there whether your cache server is Redis, Memcached, or anything else.
When the cache lives inside your process heap, a read is a hash table lookup — pure user-space, zero syscalls, zero network. The network slice doesn't shrink. It disappears entirely. And the further your cache server sits from your app in production, the wider the gap grows. SynCache's read path doesn't change at all: it's always just a heap lookup.
The benchmark above ran everything on localhost — zero physical network distance, which flatters the competition. In a real deployment, every remote cache read pays additional network overhead on top. SynCache's read path is unaffected: it's a heap lookup regardless of where the network is, because it never touches the network at all.
All benchmarks conducted using the TPC methodology in isolated Docker containers under identical conditions. 100,000 operations, 42-byte values, all engines on localhost. Reproducible on any machine.
Pick your language, follow the steps, then explore the full API reference and guides.
Follow the steps below to integrate SynCache into your project.
SYNCACHE_TOKEN
SynCache is distributed as pre-built binaries via GitHub Releases. CMake downloads and wires everything automatically — no manual download needed.
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