**Fast, Memory-Efficient Hash Table in Java: Borrowing the Best Ideas**

One day, I stumbled upon SwissTable, a hash table design that left me squinting, grinning, and wondering why every naive linear-probing table I'd ever shipped was so slow. This post is my attempt to bring that same "why is this so fast?" feeling into Java.

**The SwissTable Project: A Deep Dive**

SwissTable is an open-addressing hash table design developed by Google's engineers and later implemented in Abseil. At its core, it still performs the usual hash-table operations: compute hash(key), pick a starting slot, and probe until you find your key or an empty slot. However, SwissTable separates metadata (tiny "control bytes") from actual key/value storage and uses these control bytes to avoid expensive key comparisons most of the time.

The control bytes act as a cheap "maybe" gate before paying for real key comparisons. Think of it like a tiny Bloom filter: if this feels like a compact, cache-friendly, and easy-to-compare control byte array, you're on the right track!

**Why SwissTable is Fast**

SwissTable's speed comes from doing comparisons wide: checking many control bytes in one go instead of looping byte-by-byte and branching constantly. This workload is perfect for SIMD (Single Instruction, Multiple Data) operations, which are great at load a small block, compare against a broadcasted value, get a bitmask of matches, and only then branch into "slow path" key comparisons.

**Java's Vector API: A Game-Changer**

The Java Vector API has been incubating to let Java express vector computations that reliably compile down to good SIMD instructions on supported CPUs. With this API, we can write code like "compare 16 bytes, produce a mask, act on set bits" in plain Java.

**Implementing SwissMap: The Java Version**

With the Vector API as our permission slip, I started implementing SwissMap, a SwissTable-inspired hash table for Java. We began with the core separation of a compact control array and separate key/value storage. The control bytes are the main character – if those stay hot in cache and the scan stays branch-light, we're good to go!

**Key Features of SwissMap**

* Higher Load Factor: SwissMap tracks tombstones (a marker for deleted slots) and triggers a same-capacity rehash when they cross a threshold. This rebuilds the control array, turning DELETED back into EMPTY and restoring short probe chains. * SIMD-Friendly Probing: We use SIMD operations to compare h2 against an entire group of control bytes and only touch keys for matching lanes. * Resizing without Redundant Checks: Resizing is treated as a pure move, making it a predictable linear pass over memory rather than a branchy series of full put() operations.

**Benchmark Results**

I used a simple JMH setup to stress high-load probing and pointer-heavy keys – the exact situation SwissTable-style designs are meant to handle. The results showed that SwissMap keeps competitive throughput against other open-addressing tables and stays close to JDK HashMap performance at high load factors.

**Future Work: SWAR-Style SwissTable Variant**

I'll be saving the full "how/why" (including the SWAR variant) for a future write-up. For now, let's keep experimenting and refining our understanding of these fast and memory-efficient hash tables!