Three cheats stack to drop on-disk store from ~21 KB to ~6.7 KB per doc on
the 2003 enwiki cur corpus (-67% measured, apples-to-apples reingest with
identical document/edge counts; Merkle proofs round-trip 30/30):
1. zstd-compressed chunks.content (level 3). Magic-byte detection on read
means legacy plaintext rows pass through unchanged. Cores stay plaintext
so qa.query._docs_with_core_keyword_match's SQL LOWER+LIKE keeps working.
2. edges WITHOUT ROWID. The composite PK (src_root, edge_type, dst_root,
dst_uri, anchor) covers every column, so a default rowid-based table
near-doubles row data in the PK index. WITHOUT ROWID makes the table
itself the B-tree. Drops idx_edges_dst_uri too — the only query that
filters on dst_uri alone is gravity_top_inbound, a one-shot analytic.
3. contentless FTS5 (content='', contentless_delete=1) eliminates the
28 MB / 1000 docs of duplicated chunk text the old chunks_fts stored.
chunks gets an explicit chunk_id INTEGER PRIMARY KEY so the FTS5
rowid maps back to chunks.chunk_id at search time. Snippets are
built in Python (search/fts5.py:_build_snippet) since SQL snippet()
returns empty in contentless mode.
TF-IDF retrieval also fixed: the prior LIKE '%intel%' substring match
let "intelligence", "intellectual", "intellivision" drown real hits like
Pentium_4 (whose TF-IDF core has "intel" as an exact keyword). Now uses
word-boundary `LIKE '%, intel, %'` patterns plus a match_count over the
distinct query tokens — multi-token coverage outranks single-token title
boosts. Pentium_4 surfaces #1 for "what is the fastest intel CPU?" with
the canonical 2003 answer (Pentium 4 3.20 GHz) instead of an empty
"insufficient sources" reply.
Schema-level changes affect new DBs only; existing v9.8 DBs keep
working at the old layout. Cross-shard UNION views explicitly list the
intersection of columns so a mixed cluster (legacy + new schema shards
in one --shards-dir) still unions cleanly.
russell@unturf.com
d25c0fe66f