Distributed Systems & Storage

Architecture and design decisions behind a hand-built distributed LSM engine with HNSW vector search: from skip lists and SSTables to SWIM gossip, quorum coordination, and approximate nearest neighbors.

Storing HNSW vectors as regular LSM entries in Theseon — the encoding format, key layout, per-collection concurrency model, a graph connectivity bug, and binary snapshot persistence to avoid full rebuilds on restart.

Implementing the HNSW approximate nearest neighbor algorithm from the original paper in Go — the insert and search algorithms, neighbor selection heuristics, tombstone-aware traversal, and building an evaluation framework to prove it works.

How Theseon stores writes destined for dead nodes and replays them on recovery — the capacity accounting race, the iterator deadlock, and why skipping fsync is the right call for ephemeral data.

How Theseon's coordinator fans out operations to replicas, resolves conflicts by HLC timestamp, and repairs stale data in the background — plus the latency traps and test design bugs that emerged.

How Theseon detects node failures without a leader — implementing the SWIM gossip protocol from the paper, the bugs that emerged, and why liveness and data ownership must be decoupled.

How Theseon gained MVCC snapshot isolation and optimistic transactions — by separating merge from dedup and making versioning a first-class feature.

How the manifest, leveled compaction, block cache, and write batches turned Theseon into a self-maintaining storage engine.

How the storage foundation pieces are wired into a working engine, including internal key encoding and the merge iterator.

A deep dive into the bottom half of the Theseon stack: data structures and on-disk formats.

Architecture and design decisions behind a hand-built distributed LSM engine with HNSW vector search: from skip lists and SSTables to SWIM gossip, quorum coordination, and approximate nearest neighbors.