Most databases separate their write path and read path with layers of abstraction — a write-ahead log (WAL) for durability, a buffer pool for caching, and separate index structures for querying. Each layer adds latency and memory overhead. GTSDB takes a fundamentally different approach: the WAL is the database.
Instead of maintaining a separate buffer pool and periodically flushing pages to disk, GTSDB appends every write directly to a per-key append-only file. There is no double-writing — data goes straight from the network socket to the WAL and into a ring buffer cache. This eliminates the memory amplification that comes from maintaining both a write buffer and a read cache. The ring buffer, configured per-key with up to 10,000 slots, serves recent reads directly from memory without any disk access. For a typical IoT workload where the latest readings matter most, virtually every read hits the cache.
But the real performance breakthrough is in how GTSDB handles the read path. Traditional databases serialize query results into verbose JSON, with each data point carrying repeated field names like "timestamp" and "value". At 5,000 points per query, this adds up to hundreds of kilobytes of redundant text. GTSDB introduces an optional binary protocol: each data point becomes a fixed 16-byte record — an 8-byte timestamp followed by an 8-byte IEEE 754 float. No parsing, no field name repetition, no reflection-based serialization overhead. The server writes raw bytes straight to the TCP socket; the client reads them back with zero allocation. On a multi-key read of 25,000 points, this alone takes the response time from 7 milliseconds down to 260 microseconds.
The JSON path is no slouch either. GTSDB uses Velox, a Go JSON library backed by a native C VM, which outperforms the standard library by an order of magnitude and even beats SIMD-based alternatives like Sonic. For writes and non-bulk reads where JSON remains the default, Velox handles marshaling in nanoseconds per operation.
Under the hood, a dirty-key async flusher ensures that only modified keys trigger disk syncs, avoiding the blanket fsync storms that plague append-only databases. When data does go to disk, Facebook's Gorilla time-series compression reduces storage by nearly 30x compared to raw JSON, making disk space a non-issue even on constrained edge devices.
The architecture scales down as well as it scales up. At idle, GTSDB uses about 6 MB of memory — less than a single browser tab. It ships as a single statically-linked binary with no external dependencies. Deploy it on a Raspberry Pi, a cloud VM, or a Windows server; the behavior is identical. This is what makes GTSDB uniquely suited for IoT: it does not ask you to choose between durability, speed, and footprint. It gives you all three.
