SetrixDB: a set engine in Go — exact set intersection over IDs (and where it loses)

“Given an ID, is it in this list?” and “which IDs are in both lists at the same time?” These look like

textbook exercises. But when those lists hold millions or billions of elements and must answer in

microseconds — in a faceted filter, a permission check, a pre-filter of candidates for an LLM — the

answer stops being trivial.

This article is about one specific primitive: an exact set engine over uint64 IDs, with measured,

reproducible numbers — and a dedicated section on where it loses to an established library. It is

not about replacing databases; it is about an operation that usually gets left open.

A lot of modern software spends its time crossing lists of identifiers:

In all of these, what matters is exact presence and exact intersection over IDs — not

payloads. Generic structures (map, joins, sorted scans) solve it — just not optimally: they carry

pointers, indirections and comparisons you don't need when the data is the number.

The core choice: always work with uint64 IDs. A set is a pile of uint64; an intersection is an

AND. Everything is arithmetic.

Before a set can exist, I need dense, collision-free identifiers. My first keygen was a positional hash — a simple arithmetic formula. It collided badly: on 200k short alphanumeric tokens,

"Oa" and "0b" landed on the same ID. The fix was implementing a Minimal Perfect Hash Function (CHD v2, from scratch):

If this article has one takeaway, it is this: measure the collision rate on the real corpus is the step almost everyone skips — and it changes the whole architecture.

uint64 ID.AND runs in vpandq + vpopcntq) via cgo, with __builtin_cpu_supports) and a scalar fallback — the same binary runs anywhere. 1/(N+1) of the IDs).

Environment (all measurements): reference server — 2 vCPU AMD EPYC (Zen4, AVX-512), 3.8 GB RAM, Go 1.22 (+ gcc for cgo). Date: 09/2026. | Structure | memory | speed | exact? |

|---|---|---|---|
| `map[uint64]` (Go) | 22.3 B/key | 133.3M ops/s | yes | 

| SetrixDB (MPHF CHD v2) | 0.5 B/key (structure) | ~118 ns/lookup | yes | | Bloom filter (1% false positive) | 1.2 B/key | 23.6M ops/s | no |

Speed parity with map, at 2.2× less memory — and exact, unlike a probabilistic filter.

| Strategy | dense IDs ( `denso32` ) | random 64-bit IDs ( `aleat64` ) | 
|---|---|---|

| Sorted merge (SetrixDB) | 9.2 ms | 11.3 ms | | Roaring (compressed bitmap) | 148 µs | 523 ms | | Hash join ( map ) | 91.6 ms | 94.9 ms |

| Bitset `AND` (pure Go) | 29 µs | — | 
| **Bitset `AND` (AVX-512)** | **6 µs** | — | 

Let me be explicit, because a comparison without context is misleading:

universe/8). That's where aleat64 case, Roaring took 523 ms — but that's because it was designed for a different regime. The point is not "I always win"; it's So where does it win? In the opposite regime: a dense universe that fits in RAM, large sets,

exact intersection on the hot path. That's exactly what a real-data test showed ↓

I ran three public datasets and checked every result externally (sort + comm).

1,067,371 real sale lines (UK, 2009–2011). Query "United Kingdom AND Q4/2011 AND price ≥

5" → 22,701 rows in 823 µs. Independent check: 22,701. Identical.

enwiki-latest-all-titles-in-ns0: 19,264,252 titles. "multi-word AND starts with s" → 1,408,399 in 9.5 ms; "multi-word AND United" → 38,602 in 8.0 ms. Verified: identical.

25,000,095 real ratings; derived facets (genre, decade, score). Sets with 10.9M and 12.4M

members. Three queries, all externally verified:

And here is the number I like most — because it is about picking the right representation. On the

same 25M-ID universe, with sets of tens of millions:

| Path | memory/set | latency (A∩B) | 
|---|---|---|

| Sorted list merge | 87.7 MB | 80.4 ms |

| Dense bitset (AVX-512) | 2 MB | 227 µs | Same exact result, ~350× faster and ~43× smaller. When the universe is dense and fits in memory,

the bitset isn't just the fastest — it's the most economical too.

It IS an embeddable set engine, in Go, that answers exact presence and exact intersection

over uint64 IDs, with a SIMD kernel, sharding and a cluster mode. It coexists with your current

database: your data stays where it is; SetrixDB sits beside it as an index/pre-filter.

It is NOT a relational, columnar, NoSQL or vector database. It doesn't do SQL, joins or

similarity. And — importantly — it stores sets of IDs, not payloads.

v0.1.0). "Why not just use CRoaring/Roaring?"

Because Roaring is excellent — and it is the right answer when the universe doesn't fit in RAM or is

very sparse. SetrixDB targets another point: native Go, embeddable, dense universe that fits in RAM, with MPHF in the keygen and sharding/cluster built in. If your case is Roaring's case, use

"Why not a map/Bloom filter?"

A map stores pointers and is ~44× fatter per key (22.3 vs 0.5 B/key here). Bloom is smaller but it

errs (1% false positive) — in permissions, erring toward "can see" is unacceptable.

"Does MPHF handle insert/delete?"

No. It's built for a set. Mutable loads require a rebuild (or the sparse mode). It's a conscious

trade for O(1) lookup at ~4 bits/key. "What about Go's GC on the hot path?"

The bitsets are contiguous []uint64, allocated once; the hot loop doesn't allocate. For DMA (NPU) there's UnsafePtr + pinning — with the caveat of keeping the buffer alive.

"Isn't this just 'bitset with AVX-512'?"

Partly, yes — and that's fine: bitset + SIMD is a solid, well-known base. What the project adds is the

package: a collision-free keygen, adaptive representation (dense/sparse/hybrid), sharding/cluster,

and the "stored sets" mode (only the name travels over the network).

SetrixDB is open source (Apache-2.0). If the next wave isn't about storing more, but about

deciding faster — and if set operations deserve a dedicated, exact, vectorized engine beside what

you already use — come test it.

Code, reproducible benchmarks and a quickstart: https://github.com/setrixdb/setrixdb Run the benchmarks, open an issue, and tell me where the numbers don't add up.

SetrixDB — the arithmetic set engine.

Sets. In microseconds. On any chip. Beside your database.

License: Apache-2.0 · Copyright 2026 SetrixDB.