Standalone benchmark crate `crates/lance-bench` running Lance 4.0 against
our Parquet+HNSW at 100K × 768d (resumes_100k_v2) measured 8 dimensions.
Results (see docs/ADR-019-vector-storage.md for full scorecard):
Cold load: Parquet 0.17s vs Lance 0.13s (tie — not ≥2× threshold)
Disk size: 330.3 MB vs 330.4 MB (tie)
Search p50: 873us vs 2229us (Parquet 2.55× faster)
Search p95: 1413us vs 4998us (Parquet 3.54× faster)
Index build: 230s (ec=80) vs 16s (IVF_PQ) (Lance 14× faster)
Random access: 35ms (scan) vs 311us (Lance 112× faster)
Append 10K rows: full rewrite vs 0.08s/+31MB (Lance structural win)
Decision (ADR-019): hybrid, not migrate-or-reject.
- Parquet+HNSW stays primary — our HNSW at ec=80 es=30 recall=1.00 is
2.55× faster than Lance IVF_PQ at 100K in-RAM scale
- Lance joins as second backend per-profile for workloads where it wins
architecturally: random row access (RAG text fetch), append-heavy
pipelines (Phase C), hot-swap generations (Phase 16, 14× faster
builds), and indexes past the ~5M RAM ceiling
- Phase 17 ModelProfile gets vector_backend: Parquet | Lance field
- Ceiling table in PRD updated — 5M ceiling now says "switch to Lance"
instead of "migrate" since Lance runs alongside, not instead of
Isolation: lance-bench is a standalone workspace crate with its own dep
tree (Lance pulls DataFusion 52 + Arrow 57 incompatible with main stack
DataFusion 47 + Arrow 55). Kept off the critical path until API is
stable enough to promote into vectord::lance_store.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- POST /ingest/postgres/tables — list all tables in a database
- POST /ingest/postgres/import — import table → Parquet → catalog → queryable
- Auto type mapping: int2/4/8 → Int, float4/8 → Float64, bool → Boolean,
text/varchar/jsonb/timestamp → Utf8 (safe default per ADR-010)
- Auto PII detection + lineage on import
- Empty password support for trust auth
- Tested: imported lab_trials (40 rows, 10 cols) and threat_intel (20 rows, 30 cols)
from local knowledge_base Postgres database — immediately queryable
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- journald crate: immutable event log for every data mutation
- Events: entity_type, entity_id, field, action, old_value, new_value,
actor, source, workspace_id, timestamp
- In-memory buffer with configurable flush threshold (default 100 events)
- Flush writes events as Parquet to journal/ directory
- Query: GET /journal/history/{entity_id} — full history of any record
- Query: GET /journal/recent?limit=50 — latest events across all entities
- Convenience methods: record_insert, record_update, record_ingest
- Stats: GET /journal/stats — buffer size, persisted file count
- Manual flush: POST /journal/flush
- Per ADR-012: events are never modified or deleted
This is the single most important future-proofing decision.
Once history is lost, it's gone forever.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- ui: Dioxus WASM app with dataset sidebar, SQL editor (Ctrl+Enter), results table
- ui: dynamic API base URL (same-origin for nginx, port-based for local dev)
- gateway: CORS enabled for cross-origin requests
- nginx: lakehouse.devop.live proxies UI (:3300) + API (:3100) on same origin
- justfile: ui-build, ui-serve, sidecar, up commands added
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- queryd: SessionContext with custom URL scheme to avoid path doubling with LocalFileSystem
- queryd: ListingTable registration from catalog ObjectRefs with schema inference
- queryd: POST /query/sql returns JSON {columns, rows, row_count}
- queryd→catalogd wiring: reads all datasets, registers as named tables
- gateway: wires QueryEngine with shared store + registry
- e2e verified: SELECT *, WHERE/ORDER BY, COUNT/AVG all correct
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
