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.
162 lines
5 KiB
Python
162 lines
5 KiB
Python
"""Tests for transparent zstd compression of chunk content."""
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from __future__ import annotations
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import sqlite3
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from aborist.compress import (
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_MIN_COMPRESS_BYTES,
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_ZSTD_MAGIC,
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is_compressed,
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pack_chunk,
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unpack_chunk,
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)
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from aborist.ingest import ingest_source
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from aborist.sources.wikipedia_xml import WikipediaXmlDump
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from aborist.store import connect
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def test_pack_passes_short_text_through_uncompressed():
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short = "hello world"
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packed = pack_chunk(short)
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assert packed == short
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assert isinstance(packed, str)
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assert not is_compressed(packed)
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def test_pack_compresses_long_text():
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long = "Wikipedia article body. " * 200 # ~5 KB, well above threshold
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packed = pack_chunk(long)
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assert isinstance(packed, bytes)
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assert is_compressed(packed)
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assert len(packed) < len(long.encode("utf-8"))
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def test_pack_falls_back_to_plaintext_when_incompressible():
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# Already-random bytes hex'd as text — no entropy left for zstd.
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import os
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incompressible = os.urandom(2048).hex()
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packed = pack_chunk(incompressible)
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# Implementation may keep plaintext if compression doesn't shrink.
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if isinstance(packed, bytes):
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assert len(packed) < len(incompressible.encode("utf-8"))
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else:
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assert packed == incompressible
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def test_unpack_round_trip_compressed():
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text = "Some readable text here. " * 100
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packed = pack_chunk(text)
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assert is_compressed(packed)
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assert unpack_chunk(packed) == text
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def test_unpack_passes_legacy_str_through():
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# Old DBs have plaintext str in chunks.content. Must keep working.
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legacy = "old-format plaintext row"
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assert unpack_chunk(legacy) == legacy
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def test_unpack_handles_none_for_cold_tier():
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assert unpack_chunk(None) is None
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def test_unpack_handles_plain_utf8_bytes_without_magic():
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raw = "no zstd here, just bytes".encode("utf-8")
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assert not is_compressed(raw)
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assert unpack_chunk(raw) == "no zstd here, just bytes"
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def test_unpack_raises_on_unknown_type():
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import pytest
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with pytest.raises(TypeError):
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unpack_chunk(12345)
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def test_zstd_magic_is_correct_rfc8478_value():
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# Sanity: zstd frame magic per RFC 8478 §3.1.1.
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assert _ZSTD_MAGIC == b"\x28\xb5\x2f\xfd"
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def test_min_compress_threshold_is_reasonable():
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# Threshold below which the helper passes plaintext through.
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# 64 bytes is plenty small; covers TF-IDF cores (~50-200 bytes) but
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# would trigger compression for anything article-shaped.
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assert _MIN_COMPRESS_BYTES <= 256
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def test_ingest_writes_compressed_for_large_chunks(tmp_path):
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"""End-to-end: ingest a doc with a long body, verify chunks.content
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is stored as compressed bytes (not plaintext)."""
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# Synthesize a fixture XML with one big article body.
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big_body = " ".join(f"sentence{i}" for i in range(2000)) # ~18 KB
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xml = f"""<?xml version="1.0" encoding="UTF-8"?>
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<mediawiki xmlns="http://www.mediawiki.org/xml/export-0.10/" version="0.10">
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<page>
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<title>Big Article</title>
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<ns>0</ns>
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<id>1</id>
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<revision>
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<id>1</id>
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<timestamp>2010-01-01T00:00:00Z</timestamp>
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<text xml:space="preserve">{big_body}</text>
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</revision>
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</page>
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</mediawiki>"""
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fixture = tmp_path / "wp.xml"
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fixture.write_text(xml, encoding="utf-8")
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db_path = tmp_path / "aborist.db"
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conn = connect(db_path)
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try:
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ingest_source(conn, WikipediaXmlDump(fixture))
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finally:
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conn.close()
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# Re-open with raw sqlite3 to inspect cell types directly.
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raw = sqlite3.connect(db_path)
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row = raw.execute("SELECT content FROM chunks LIMIT 1").fetchone()
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raw.close()
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assert row is not None
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content = row[0]
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# Stored as bytes (BLOB cell), zstd-framed.
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assert isinstance(content, bytes)
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assert is_compressed(content)
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# Round-trips back to the original text.
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assert unpack_chunk(content).startswith("sentence0")
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def test_ingest_then_search_round_trip_finds_compressed_doc(tmp_path):
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"""FTS5 still indexes plaintext; chunks.content stays compressed; reads
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that go through unpack_chunk reconstruct the original. The whole
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pipeline keeps working."""
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from aborist.search import FTS5Backend
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big_body = "merkle providence wikipedia anarchism " * 200 # ~7.4 KB
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xml = f"""<?xml version="1.0" encoding="UTF-8"?>
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<mediawiki xmlns="http://www.mediawiki.org/xml/export-0.10/" version="0.10">
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<page>
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<title>Topic</title>
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<ns>0</ns>
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<id>1</id>
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<revision>
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<id>1</id>
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<timestamp>2010-01-01T00:00:00Z</timestamp>
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<text xml:space="preserve">{big_body}</text>
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</revision>
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</page>
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</mediawiki>"""
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fixture = tmp_path / "wp.xml"
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fixture.write_text(xml, encoding="utf-8")
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db_path = tmp_path / "aborist.db"
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conn = connect(db_path)
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try:
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ingest_source(conn, WikipediaXmlDump(fixture))
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backend = FTS5Backend(conn)
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hits = backend.search("merkle providence")
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finally:
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conn.close()
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assert hits, "FTS5 should find the compressed doc via its plaintext index"
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assert hits[0].title == "Topic"
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