4e1c400f5d
Phase E gave us soft-delete at query time (tombstones hide rows via a
DataFusion filter view). This completes the invariant: after compact,
tombstoned rows are PHYSICALLY absent from the parquet on disk.
delta::compact changes:
- Signature adds tombstones: &[Tombstone]
- After merging base + deltas, apply_tombstone_filter builds a
BooleanArray keep-mask per batch (True where row_key_value is NOT
in the tombstone set) and applies arrow::compute::filter_record_batch
- Supports Utf8, Int32, Int64 key columns (matches refresh.rs coverage
for pg- and csv-derived schemas)
- CompactResult gains tombstones_applied + rows_dropped_by_tombstones
- Caller clears tombstone store on success
Critical correctness fix surfaced during E2E testing:
The original Phase 8 compact concatenated N independent Parquet byte
streams from record_batch_to_parquet() — each with its own footer.
Parquet readers only see the FIRST footer's data; the rest is invisible.
Latent since Phase 8 shipped; triggered by tombstone-filtering produc-
ing multiple batches. Corrupted candidates.parquet on first test run
(restored from UI fixture copy — good argument for test data in repo).
Fix:
- Single ArrowWriter per compaction, writes every batch into one
properly-footered Parquet
- Snappy compression to match ingest defaults (otherwise rewrite
inflated file 3× — 10.5MB → 34MB — because no compression was set)
- Verify-before-swap: parse written buf back to confirm row count
matches expected; refuses to overwrite base_key if verification fails
- Write to {base_key}.compact-{ts}.tmp first, then to base_key; delete
temp; only then delete delta files. Any error along the way leaves
the original base intact.
TombstoneStore::clear(dataset) drops all tombstone batch files and
evicts the per-dataset AppendLog from cache. Called after successful
compact.
QueryEngine::catalog() accessor exposes the Registry so queryd
handlers can reach the tombstone store without routing through gateway
state.
E2E on candidates (100K rows, 15 cols):
- Baseline: 10.59 MB, 100000 rows
- Tombstone CAND-000001/2/3 (soft-delete): 99997 visible, 100000 raw
- Compact: tombstones_applied=3, rows_dropped=3, final_rows=99997
- Post: 10.72 MB (Snappy), valid parquet (1 row_group), 99997 rows
- Restart: persists, tombstones list empty, __raw__candidates also
99997 (the 3 IDs are physically gone from disk)
PRD invariant close: deletion is now actually deletion, not just
masking. GDPR erasure request → tombstone + schedule compact → data
gone.
Deferred:
- Compact-all-datasets cron (currently manual per-dataset via
POST /query/compact)
- Compaction of tombstone batch files themselves (they grow at
flush_threshold=1 per tombstone; TombstoneStore::compact exists
but not auto-called)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
216 lines
16 KiB
Markdown
216 lines
16 KiB
Markdown
# Phase Tracker
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## Phase 0: Bootstrap ✅
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- [x] Cargo workspace with all crate stubs compiling
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- [x] `shared` crate: error types, ObjectRef, DatasetId
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- [x] `gateway` with Axum: GET /health → 200
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- [x] tracing + tracing-subscriber wired in gateway
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- [x] justfile with build, test, run recipes
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- [x] docs committed to git
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## Phase 1: Storage + Catalog ✅
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- [x] storaged: object_store backend init (LocalFileSystem)
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- [x] storaged: Axum endpoints (PUT/GET/DELETE/LIST)
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- [x] shared/arrow_helpers.rs: RecordBatch ↔ Parquet + schema fingerprinting
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- [x] catalogd/registry.rs: in-memory index + manifest persistence
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- [x] catalogd service: POST/GET /datasets + by-name
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- [x] gateway routes wired
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## Phase 2: Query Engine ✅
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- [x] queryd: SessionContext + object_store config
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- [x] queryd: ListingTable from catalog ObjectRefs
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- [x] queryd service: POST /query/sql → JSON
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- [x] queryd → catalogd wiring
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- [x] gateway routes /query
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## Phase 3: AI Integration ✅
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- [x] Python sidecar: FastAPI + Ollama (embed/generate/rerank)
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- [x] Dockerfile for sidecar
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- [x] aibridge/client.rs: HTTP client
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- [x] aibridge service: Axum proxy endpoints
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- [x] Model config via env vars
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## Phase 4: Frontend ✅
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- [x] Dioxus scaffold, WASM build
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- [x] Ask tab: natural language → AI SQL → results
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- [x] Explore tab: dataset browser + AI summary
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- [x] SQL tab: raw DataFusion editor
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- [x] System tab: health checks for all services
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## Phase 5: Hardening ✅
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- [x] Proto definitions (lakehouse.proto)
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- [x] Internal gRPC: CatalogService on :3101
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- [x] OpenTelemetry tracing: stdout exporter
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- [x] Auth middleware: X-API-Key (toggleable)
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- [x] Config-driven startup: lakehouse.toml
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## Phase 6: Ingest Pipeline ✅
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- [x] CSV ingest with auto schema detection
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- [x] JSON ingest (array + newline-delimited, nested flattening)
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- [x] PDF text extraction (lopdf)
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- [x] Text/SMS file ingest
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- [x] Content hash dedup (SHA-256)
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- [x] POST /ingest/file multipart upload
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- [x] 12 unit tests
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## Phase 7: Vector Index + RAG ✅
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- [x] chunker: configurable size + overlap, sentence-boundary aware
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- [x] store: embeddings as Parquet (binary f32 vectors)
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- [x] search: brute-force cosine similarity
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- [x] rag: embed → search → retrieve → LLM answer with citations
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- [x] POST /vectors/index, /search, /rag
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- [x] Background job system with progress tracking
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- [x] Dual-pipeline supervisor with checkpointing + retry
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- [x] 100K embeddings: 177/sec on A4000, zero failures
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- [x] 6 unit tests
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## Phase 8: Hot Cache + Incremental Updates ✅
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- [x] MemTable hot cache: LRU, configurable max (16GB)
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- [x] POST /query/cache/pin, /cache/evict, GET /cache/stats
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- [x] Delta store: append-only delta Parquet files
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- [x] Merge-on-read: queries combine base + deltas
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- [x] Compaction: POST /query/compact
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- [x] Benchmarked: 9.8x speedup (1M rows: 942ms → 96ms)
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## Phase 8.5: Agent Workspaces ✅
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- [x] WorkspaceManager with daily/weekly/monthly/pinned tiers
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- [x] Saved searches, shortlists, activity logs per workspace
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- [x] Instant zero-copy handoff between agents
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- [x] Persistence to object storage, rebuild on startup
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## Phase 9: Event Journal ✅
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- [x] journald crate: append-only mutation log
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- [x] Event schema: entity, field, old/new value, actor, source, workspace
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- [x] In-memory buffer with auto-flush to Parquet
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- [x] GET /journal/history/{entity_id}, /recent, /stats
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- [x] POST /journal/event, /update, /flush
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## Phase 10: Rich Catalog v2 ✅
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- [x] DatasetManifest: description, owner, sensitivity, columns, lineage, freshness, tags
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- [x] PII auto-detection: email, phone, SSN, salary, address, medical
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- [x] Column-level metadata with sensitivity flags
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- [x] Lineage tracking: source_system → ingest_job → dataset
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- [x] PATCH /catalog/datasets/by-name/{name}/metadata
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- [x] Backward compatible (serde default)
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## Phase 11: Embedding Versioning ✅
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- [x] IndexRegistry: model_name, model_version, dimensions per index
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- [x] Index metadata persisted as JSON, rebuilt on startup
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- [x] GET /vectors/indexes — list all (filter by source/model)
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- [x] GET /vectors/indexes/{name} — metadata
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- [x] Background jobs auto-register metadata on completion
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## Phase 12: Tool Registry ✅
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- [x] 6 built-in staffing tools (search_candidates, get_candidate, revenue_by_client, recruiter_performance, cold_leads, open_jobs)
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- [x] Parameter validation + SQL template substitution
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- [x] Permission levels: read / write / admin
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- [x] Full audit trail per invocation
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- [x] GET /tools, GET /tools/{name}, POST /tools/{name}/call, GET /tools/audit
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## Phase 13: Security & Access Control ✅
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- [x] Role-based access: admin, recruiter, analyst, agent
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- [x] Field-level sensitivity enforcement
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- [x] Column masking determination per agent
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- [x] Query audit logging
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- [x] GET/POST /access/roles, GET /access/audit, POST /access/check
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## Phase 14: Schema Evolution ✅
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- [x] Schema diff detection (added, removed, type changed, renamed)
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- [x] Fuzzy rename detection (shared word parts)
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- [x] Auto-generated migration rules with confidence scores
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- [x] AI migration prompt builder for complex cases
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- [x] 5 unit tests
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## Phase 15+: Horizon
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- [x] HNSW vector index with iteration-friendly trial system (2026-04-16)
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- `HnswStore.build_index_with_config` — parameterized ef_construction, ef_search, seed
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- `EmbeddingCache` — pins 100K vectors in memory, shared across trials
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- `harness::EvalSet` — named query sets with brute-force ground truth
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- `TrialJournal` — append-only JSONL at `_hnsw_trials/{index}.jsonl`
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- Endpoints: `/vectors/hnsw/trial`, `/hnsw/trials/{idx}`, `/hnsw/trials/{idx}/best?metric={recall|latency|pareto}`, `/hnsw/evals`, `/hnsw/evals/{name}/autogen`, `/hnsw/cache/stats`
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- Measured on 100K resumes: **brute-force 44-54ms → HNSW 509us-1830us**, recall 0.92-1.00 depending on `ef_construction`. Sweet spot: ec=80 es=30 → p50=873us recall=1.00 — locked in as `HnswConfig::default()`
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- [x] Catalog manifest repair — `POST /catalog/resync-missing` restores row_count and columns from parquet footers (2026-04-16). All 7 staffing tables recovered to PRD-matching 2.47M rows.
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- [~] Federated multi-bucket query — **foundation complete 2026-04-16**, see ADR-017
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- [x] `StorageConfig.buckets` + `rescue_bucket` + `profile_root` config shape
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- [x] `SecretsProvider` trait + `FileSecretsProvider` (reads /etc/lakehouse/secrets.toml, checks 0600 perms)
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- [x] `storaged::BucketRegistry` — multi-backend, rescue-aware, reachability probes
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- [x] `storaged::error_journal::ErrorJournal` — append-only JSONL at `primary://_errors/bucket_errors.jsonl`
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- [x] Endpoints: `GET /storage/buckets`, `GET /storage/errors`, `GET /storage/bucket-health`
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- [x] Bucket-aware I/O: `PUT/GET /storage/buckets/{bucket}/objects/{*key}` with rescue fallback + `X-Lakehouse-Rescue-Used` observability headers
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- [x] Backward compat: empty `[[storage.buckets]]` synthesizes a `primary` from legacy `root`
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- [x] Three-bucket test (primary + rescue + testing) verified: normal reads, rescue fallback with headers, hard-fail missing, write to unknown bucket 503, error journal + health summary
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- [x] `X-Lakehouse-Bucket` header middleware on ingest endpoints (2026-04-16)
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- [x] Catalog migration: `POST /catalog/migrate-buckets` stamps `bucket = "primary"` on legacy refs (12 renamed, 14 total now canonical)
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- [x] `queryd` registers every bucket with DataFusion for cross-bucket SQL — verified with people_test (testing) × animals (primary) CROSS JOIN
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- [ ] Profile hot-load endpoints: `POST /profile/{user}/activate|deactivate` (deferred to Phase 17)
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- [ ] `vectord` bucket-scoped paths (trial journals, eval sets per-bucket) (deferred to Phase 17)
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- [x] Database connector ingest (Postgres first) — 2026-04-16
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- `pg_stream::stream_table_to_parquet` — ORDER BY + LIMIT/OFFSET pagination, configurable batch_size
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- `parse_dsn` — postgresql:// and postgres:// URL scheme, user/password/host/port/db
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- `POST /ingest/db` endpoint: `{dsn, table, dataset_name?, batch_size?, order_by?, limit?}` → streams to Parquet, registers in catalog with PII detection + redacted-password lineage
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- Existing `POST /ingest/postgres/import` (structured config) preserved alongside
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- 4 DSN-parser unit tests + live end-to-end test against `knowledge_base.team_runs` (586 rows, 13 cols, 6 batches, 196ms)
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- [x] Phase B: Lance storage evaluation — 2026-04-16
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- `crates/lance-bench` standalone pilot (Lance 4.0) avoids DataFusion/Arrow version conflict with main stack
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- 8-dimension benchmark on resumes_100k_v2 — see docs/ADR-019-vector-storage.md for scorecard
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- Decision: hybrid architecture. Parquet+HNSW stays primary (2.55× faster search at 100K in-RAM). Lance added as per-profile second backend for random access (112× faster), append (0.08s vs full rewrite), hot-swap (14× faster index builds), and scale past 5M RAM ceiling.
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- [x] Phase E.2 — Compaction integrates tombstones (physical deletion) — 2026-04-16
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- `delta::compact` accepts `tombstones: &[Tombstone]` param, filters rows at merge time via arrow `filter_record_batch`
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- CompactResult gains `tombstones_applied` + `rows_dropped_by_tombstones`
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- Atomic write: ArrowWriter → single Parquet file (fixes latent bug where concatenated Parquet byte streams produced garbage — footer-only-first-segment visible), verify-parse before overwrite, temp_key staging, delete delta files AFTER base write succeeds
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- Snappy compression on output matches ingest defaults (avoids 3× size inflation on every compact)
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- `TombstoneStore::clear` drops all batch files for a dataset; called by queryd after successful compact
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- Query engine exposes `catalog()` accessor so service handler can reach the tombstone store
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- E2E verified on candidates (100K rows): tombstone 3 IDs → compact → 99,997 rows physically in parquet, tombstones empty, IDs gone from `__raw__candidates` too; file size 10.59 MB → 10.72 MB (proportional to data, not inflated)
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- [x] Phase 16: Hot-swap generations + autotune agent — 2026-04-16
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- `vectord::promotion::PromotionRegistry` — per-index current config + history at `_hnsw_promotions/{index}.json`, cap 50 history entries
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- Endpoints: `POST /vectors/hnsw/promote/{index}/{trial_id}`, `POST /vectors/hnsw/rollback/{index}`, `GET /vectors/hnsw/promoted/{index}`
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- `vectord::autotune::run_autotune` — grid of trials (configurable or default 5 configs), Pareto winner selection (max recall, then min p50), min_recall safety gate (default 0.9), config bounds (ec ∈ [10,400], es ∈ [10,200])
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- `POST /vectors/hnsw/autotune` — runs the full loop synchronously, journals every trial, auto-promotes winner
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- `activate_profile` uses `promotion_registry.config_or(..., profile_default)` so newly-promoted configs flow automatically into next activation
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- End-to-end: autogen harness for threat_intel_v1 (10 queries), autotune ran 5 trials (all recall=1.00, p50 64-68us), promoted ec=20 es=30 at recall=1.0 p50=64us as winner. Manual promote of ec=80 es=30 pushed autotune pick onto history. Rollback restored autotune winner. Second rollback cleared to None. Re-promote + restart verified persistence. Activation after promotion logged "building HNSW ef_construction=80 ef_search=30 seed=42" — config flowed through correctly.
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- [x] Phase 17: Model profiles + scoped search — 2026-04-16
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- `shared::types::ModelProfile` — { id, ollama_name, description, bound_datasets, hnsw_config, embed_model, created_at, created_by }
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- `shared::types::ProfileHnswConfig` — mirror of vectord's HnswConfig to avoid cross-crate dep cycle (defaults ec=80 es=30 matching Phase 15 winner)
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- `Registry::{put_profile, get_profile, list_profiles, delete_profile}` persisted at `_catalog/profiles/{id}.json`, validates bindings exist (raw dataset OR AiView)
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- Endpoints: `POST/GET /catalog/profiles`, `GET/DELETE /catalog/profiles/{id}`
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- `POST /vectors/profile/{id}/activate` — warms EmbeddingCache + builds HNSW with profile's config for every bound dataset's vector index; reports warmed indexes + failures + duration
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- `POST /vectors/profile/{id}/search` — rejects 403 if requested index's source isn't in profile.bound_datasets; falls through to HNSW if warm, brute-force otherwise
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- Fixed refresh to register new index metadata (was silently no-op for first-time indexes)
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- End-to-end: security-analyst profile bound to threat_intel → activate warms 54 vectors in 156ms → within-scope HNSW search works (0.625 score); out-of-scope search for candidates returns 403 with allowed bindings listed
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- [x] Phase E: Soft deletes (tombstones) — 2026-04-16
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- `shared::types::Tombstone` — { dataset, row_key_column, row_key_value, deleted_at, actor, reason }
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- `catalogd::tombstones::TombstoneStore` per-dataset append-log at `_catalog/tombstones/{dataset}/`, flush_threshold=1 + explicit flush so every tombstone is durable on return (compliance requirement)
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- All tombstones for a dataset must share the same `row_key_column` (validated at write — query filter is built as a single WHERE NOT IN clause)
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- `Registry::add_tombstone / list_tombstones`
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- Endpoint: `POST /catalog/datasets/by-name/{name}/tombstone` accepting `{row_key_column, row_key_values[], actor, reason}`; companion `GET` lists active tombstones
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- `queryd::context::build_context` wraps tombstoned tables: raw goes to `__raw__{name}`, public name becomes a DataFusion view with `WHERE CAST(col AS VARCHAR) NOT IN (...)` filter
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- End-to-end on candidates: tombstone 3 IDs, COUNT drops 100,000 → 99,997, specific WHERE returns empty, AiView candidates_safe transitively excludes them too, restart preserves all tombstones
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- Limits / not in MVP: physical compaction (Phase 8 doesn't yet read tombstones during merge); journal integration (tombstones don't yet emit Phase 9 mutation events — covered by audit fields on the tombstone itself)
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- [x] Phase D: AI-safe views — 2026-04-16
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- `shared::types::AiView` — name, base_dataset, columns whitelist, optional row_filter, column_redactions
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- `shared::types::Redaction` — Null | Hash | Mask { keep_prefix, keep_suffix }
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- `Registry::put_view / get_view / list_views / delete_view` persisted to `_catalog/views/{name}.json`
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- `queryd::context` registers each view as a DataFusion view with the safe projection + filter + redactions baked into the SELECT
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- Endpoints: `POST/GET /catalog/views`, `GET/DELETE /catalog/views/{name}`
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- End-to-end on candidates: `candidates_safe` view exposes 8 of 15 columns, masks `candidate_id` (CAN******01), filters out `status='blocked'`. `SELECT * FROM candidates_safe` returns whitelist only; `SELECT email FROM candidates_safe` fails. View survives restart.
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- Capability surface — raw `candidates` still accessible by name; Phase 13 access control is the layer that enforces who can query what
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- [x] Phase C: Decoupled embedding refresh — 2026-04-16
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- `DatasetManifest`: `last_embedded_at`, `embedding_stale_since`, `embedding_refresh_policy` (Manual | OnAppend | Scheduled)
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- `Registry::mark_embeddings_stale` / `clear_embeddings_stale` / `stale_datasets`
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- Ingest paths (CSV pipeline + Postgres streaming) auto-mark-stale when writing to an already-embedded dataset
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- `vectord::refresh::refresh_index` — reads dataset, diffs doc_ids vs existing embeddings, embeds only new rows, writes combined index, clears stale
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- `POST /vectors/refresh/{dataset}` + `GET /vectors/stale`
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- Id columns accept `Utf8`, `Int32`, `Int64`
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- End-to-end on threat_intel: initial 20-row embed 2.1s; re-ingest to 54 rows auto-marks stale; delta refresh embeds only 34 new in 970ms (6× faster than full re-embed); stale cleared
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- [ ] Database connector ingest (Postgres/MySQL)
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- [ ] PDF OCR (Tesseract)
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- [ ] Scheduled ingest (cron)
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- [ ] Fine-tuned domain models
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- [ ] Multi-node query distribution
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---
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**30 unit tests | 11 crates | 16 ADRs | 2.47M rows | 100K vectors | All built 2026-03-27**
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**HNSW trial system: 2026-04-16**
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