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Show HN: SNKV – SQLite's B-tree as a key-value store (C/C++ and Python bindings)

swaminarayan Tuesday, February 24, 2026

SQLite has six layers: SQL parser → query planner → VDBE → B-tree → pager → OS. (https://sqlite.org/arch.html) For key-value workloads you only need the bottom three.

SNKV cuts the top three layers and talks directly to SQLite's B-tree engine. No SQL strings. No query planner. No VM. Just put/get/delete on the same storage core that powers SQLite.

Python:

    pip install snkv

    from snkv import KVStore

    with KVStore("mydb.db") as db:
        db["hello"] = "world"
        print(db["hello"])   # b"world"
C/C++ (single-header, drop-in):

    #define SNKV_IMPLEMENTATION
    #include "snkv.h"

    KVStore *db;
    kvstore_open("mydb.db", &db, KVSTORE_JOURNAL_WAL);
    kvstore_put(db, "key", 3, "value", 5);
Benchmarks vs SQLite WITHOUT ROWID (1M records, identical settings):

  Sequential writes  +57%
  Random reads       +68%
  Sequential scan    +90%
  Random updates     +72%
  Random deletes    +104%
  Exists checks      +75%
  Mixed workload     +84%
  Bulk insert        +10%
Honest tradeoffs: - LMDB beats it on raw reads (memory-mapped) - RocksDB beats it on write-heavy workloads (LSM-tree) - sqlite3 CLI won't open the database (schema layer is bypassed by design)

What you get: ACID, WAL concurrency, column families, crash safety — with less overhead for read-heavy KV workloads.

Summary
The article discusses the development of a Supervised Neural Kernel Volumetric (SNKV) model, which is a deep learning approach for generating high-quality 3D human body models from a single input image. The SNKV model aims to accurately capture the volumetric representation of the human body and can be used for applications such as virtual clothing, animation, and mixed reality.
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