fenecdb and SQLite

SQLite is an embedded SQL database in one file; sqlite-vec adds vector search to it and FTS5 full-text search. fenecdb is an embedded document database with vector and full-text search built in, which also runs as a server over HTTP.

The facts about SQLite, sqlite-vec and FTS5 on this page were checked on 2026-10-02 against the pages each one links to. fenecdb is at version 0.1.8, and is alpha software.

Which to choose

Choose SQLite when

• You want the database that is already everywhere: SQLite is "likely used more than all other database engines combined" (sqlite.org), with bindings, ORMs and tools in every language.
• You want SQL with joins, and transactions you hold open across statements, with savepoints (transactions).
• Readers must never wait for the writer: in WAL mode they do not (WAL).
• You need a long track record: 100% branch coverage in its tests (testing), and an on-disk format kept backwards compatible, supported through 2050 (long-term support).
• Exact vector search over a modest table is enough, through sqlite-vec.

Choose fenecdb when

• You want an approximate vector index: HNSW with quantization and filtered search (The vector clause). sqlite-vec's approximate indexes are in its alpha releases.
• You search by meaning and by words in one query, fused by rank (Hybrid search).
• The page or app should stay in step with a server: a replica with live queries and offline writes (Sync).
• The same file should also be served to other languages over HTTP, with replicas (Server).

Feature by feature

Measured

SQLite against fenecdb in one process, 100 000 rows of 128 dimensions, SQLite with no vector extension: its rows are read back and scored by fenecdb's own code. We measured these on one laptop; the method is below.

Writers and readers at once, a row a write:

YCSB's core workloads over a million records, operations a second, the median of three runs (every cell):

• fenecdb writes a record again at its file's end on every update, where SQLite updates its page in place; the file compacts itself once half of it is dead, beside the reads. Before it did, C at one thread read 30.1 k after the updates, where SQLite read 279 k.
• SQLite opens faster and its readers never wait for a block of writes; fenecdb's do, while the block runs.
• Not measured yet: sqlite-vec, by brute force or with its alpha indexes; FTS5; and either in the browser.

make compare
make concurrency-bench

What the browser downloads

The WebAssembly engine alone, as each project publishes it, compressed the way a static file is served.

Moving from SQLite

fenec import reads a SQLite file directly, with no SQLite library installed, and makes a collection of a table.

fenec import data.sqlite --table docs --into articles \
    --vector embed:384 --index "embed@hnsw(cosine)" --dry-run

• --dry-run prints the schema it would make and writes nothing.
• Vectors stored as BLOBs of little-endian f32s become vector<N> with --vector.
• INTEGER PRIMARY KEY becomes the document id.

A search and its filter, in each:

-- sqlite-vec, year a metadata column of the vec0 table
SELECT rowid, distance FROM vec_articles
WHERE embed MATCH ?1 AND k = 10 AND year >= 2024;

get articles select title
  where year >= 2024
  near embed $1
  limit 10