lakehouse/docs/PHASES.md

Phase Tracker

Phase 0: Bootstrap ✅

• Cargo workspace with all crate stubs compiling
• shared crate: error types, ObjectRef, DatasetId
• gateway with Axum: GET /health → 200
• tracing + tracing-subscriber wired in gateway
• justfile with build, test, run recipes
• docs committed to git

Phase 1: Storage + Catalog ✅

• storaged: object_store backend init (LocalFileSystem)
• storaged: Axum endpoints (PUT/GET/DELETE/LIST)
• shared/arrow_helpers.rs: RecordBatch ↔ Parquet + schema fingerprinting
• catalogd/registry.rs: in-memory index + manifest persistence
• catalogd service: POST/GET /datasets + by-name
• gateway routes wired

Phase 2: Query Engine ✅

• queryd: SessionContext + object_store config
• queryd: ListingTable from catalog ObjectRefs
• queryd service: POST /query/sql → JSON
• queryd → catalogd wiring
• gateway routes /query

Phase 3: AI Integration ✅

• Python sidecar: FastAPI + Ollama (embed/generate/rerank)
• Dockerfile for sidecar
• aibridge/client.rs: HTTP client
• aibridge service: Axum proxy endpoints
• Model config via env vars

Phase 4: Frontend ✅

• Dioxus scaffold, WASM build
• Ask tab: natural language → AI SQL → results
• Explore tab: dataset browser + AI summary
• SQL tab: raw DataFusion editor
• System tab: health checks for all services

Phase 5: Hardening ✅

• Proto definitions (lakehouse.proto)
• Internal gRPC: CatalogService on :3101
• OpenTelemetry tracing: stdout exporter
• Auth middleware: X-API-Key (toggleable)
• Config-driven startup: lakehouse.toml

Phase 6: Ingest Pipeline ✅

• CSV ingest with auto schema detection
• JSON ingest (array + newline-delimited, nested flattening)
• PDF text extraction (lopdf)
• Text/SMS file ingest
• Content hash dedup (SHA-256)
• POST /ingest/file multipart upload
• 12 unit tests

Phase 7: Vector Index + RAG ✅

• chunker: configurable size + overlap, sentence-boundary aware
• store: embeddings as Parquet (binary f32 vectors)
• search: brute-force cosine similarity
• rag: embed → search → retrieve → LLM answer with citations
• POST /vectors/index, /search, /rag
• Background job system with progress tracking
• Dual-pipeline supervisor with checkpointing + retry
• 100K embeddings: 177/sec on A4000, zero failures
• 6 unit tests

Phase 8: Hot Cache + Incremental Updates ✅

• MemTable hot cache: LRU, configurable max (16GB)
• POST /query/cache/pin, /cache/evict, GET /cache/stats
• Delta store: append-only delta Parquet files
• Merge-on-read: queries combine base + deltas
• Compaction: POST /query/compact
• Benchmarked: 9.8x speedup (1M rows: 942ms → 96ms)

Phase 8.5: Agent Workspaces ✅

• WorkspaceManager with daily/weekly/monthly/pinned tiers
• Saved searches, shortlists, activity logs per workspace
• Instant zero-copy handoff between agents
• Persistence to object storage, rebuild on startup

Phase 9: Event Journal ✅

• journald crate: append-only mutation log
• Event schema: entity, field, old/new value, actor, source, workspace
• In-memory buffer with auto-flush to Parquet
• GET /journal/history/{entity_id}, /recent, /stats
• POST /journal/event, /update, /flush

Phase 10: Rich Catalog v2 ✅

• DatasetManifest: description, owner, sensitivity, columns, lineage, freshness, tags
• PII auto-detection: email, phone, SSN, salary, address, medical
• Column-level metadata with sensitivity flags
• Lineage tracking: source_system → ingest_job → dataset
• PATCH /catalog/datasets/by-name/{name}/metadata
• Backward compatible (serde default)

Phase 11: Embedding Versioning ✅

• IndexRegistry: model_name, model_version, dimensions per index
• Index metadata persisted as JSON, rebuilt on startup
• GET /vectors/indexes — list all (filter by source/model)
• GET /vectors/indexes/{name} — metadata
• Background jobs auto-register metadata on completion

Phase 12: Tool Registry ✅

• 6 built-in staffing tools (search_candidates, get_candidate, revenue_by_client, recruiter_performance, cold_leads, open_jobs)
• Parameter validation + SQL template substitution
• Permission levels: read / write / admin
• Full audit trail per invocation
• GET /tools, GET /tools/{name}, POST /tools/{name}/call, GET /tools/audit

Phase 13: Security & Access Control ✅

• Role-based access: admin, recruiter, analyst, agent
• Field-level sensitivity enforcement
• Column masking determination per agent
• Query audit logging
• GET/POST /access/roles, GET /access/audit, POST /access/check

Phase 14: Schema Evolution ✅

• Schema diff detection (added, removed, type changed, renamed)
• Fuzzy rename detection (shared word parts)
• Auto-generated migration rules with confidence scores
• AI migration prompt builder for complex cases
• 5 unit tests

Phase 15+: Horizon

• HNSW vector index with iteration-friendly trial system (2026-04-16)
  • HnswStore.build_index_with_config — parameterized ef_construction, ef_search, seed
  • EmbeddingCache — pins 100K vectors in memory, shared across trials
  • harness::EvalSet — named query sets with brute-force ground truth
  • TrialJournal — append-only JSONL at _hnsw_trials/{index}.jsonl
  • Endpoints: /vectors/hnsw/trial, /hnsw/trials/{idx}, /hnsw/trials/{idx}/best?metric={recall|latency|pareto}, /hnsw/evals, /hnsw/evals/{name}/autogen, /hnsw/cache/stats
  • 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()
• 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.
• [~] Federated multi-bucket query — foundation complete 2026-04-16, see ADR-017
  • StorageConfig.buckets + rescue_bucket + profile_root config shape
  • SecretsProvider trait + FileSecretsProvider (reads /etc/lakehouse/secrets.toml, checks 0600 perms)
  • storaged::BucketRegistry — multi-backend, rescue-aware, reachability probes
  • storaged::error_journal::ErrorJournal — append-only JSONL at primary://_errors/bucket_errors.jsonl
  • Endpoints: GET /storage/buckets, GET /storage/errors, GET /storage/bucket-health
  • Bucket-aware I/O: PUT/GET /storage/buckets/{bucket}/objects/{*key} with rescue fallback + X-Lakehouse-Rescue-Used observability headers
  • Backward compat: empty [[storage.buckets]] synthesizes a primary from legacy root
  • 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
  • X-Lakehouse-Bucket header middleware on ingest endpoints (2026-04-16)
  • Catalog migration: POST /catalog/migrate-buckets stamps bucket = "primary" on legacy refs (12 renamed, 14 total now canonical)
  • queryd registers every bucket with DataFusion for cross-bucket SQL — verified with people_test (testing) × animals (primary) CROSS JOIN
  • Profile hot-load endpoints: bucket auto-provisioning on POST /vectors/profile/{id}/activate (2026-04-17)
  • vectord bucket-scoped paths: TrialJournal + PromotionRegistry resolve per-index via IndexMeta.bucket (2026-04-17)
  • Runtime bucket lifecycle: POST /storage/buckets (provision) + DELETE /storage/buckets/{name} (unregister, refuses primary/rescue) (2026-04-17)
  • ModelProfile.bucket field — per-profile artifact isolation (2026-04-17)
• Database connector ingest (Postgres first) — 2026-04-16
  • pg_stream::stream_table_to_parquet — ORDER BY + LIMIT/OFFSET pagination, configurable batch_size
  • parse_dsn — postgresql:// and postgres:// URL scheme, user/password/host/port/db
  • 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
  • Existing POST /ingest/postgres/import (structured config) preserved alongside
  • 4 DSN-parser unit tests + live end-to-end test against knowledge_base.team_runs (586 rows, 13 cols, 6 batches, 196ms)
• Phase B: Lance storage evaluation — 2026-04-16
  • crates/lance-bench standalone pilot (Lance 4.0) avoids DataFusion/Arrow version conflict with main stack
  • 8-dimension benchmark on resumes_100k_v2 — see docs/ADR-019-vector-storage.md for scorecard
  • 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.
• Phase E.2 — Compaction integrates tombstones (physical deletion) — 2026-04-16
  • delta::compact accepts tombstones: &[Tombstone] param, filters rows at merge time via arrow filter_record_batch
  • CompactResult gains tombstones_applied + rows_dropped_by_tombstones
  • 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
  • Snappy compression on output matches ingest defaults (avoids 3× size inflation on every compact)
  • TombstoneStore::clear drops all batch files for a dataset; called by queryd after successful compact
  • Query engine exposes catalog() accessor so service handler can reach the tombstone store
  • 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)
• Phase 16: Hot-swap generations + autotune agent — 2026-04-16
  • vectord::promotion::PromotionRegistry — per-index current config + history at _hnsw_promotions/{index}.json, cap 50 history entries
  • Endpoints: POST /vectors/hnsw/promote/{index}/{trial_id}, POST /vectors/hnsw/rollback/{index}, GET /vectors/hnsw/promoted/{index}
  • 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])
  • POST /vectors/hnsw/autotune — runs the full loop synchronously, journals every trial, auto-promotes winner
  • activate_profile uses promotion_registry.config_or(..., profile_default) so newly-promoted configs flow automatically into next activation
  • 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.
• Phase 17: Model profiles + scoped search — 2026-04-16
  • shared::types::ModelProfile — { id, ollama_name, description, bound_datasets, hnsw_config, embed_model, created_at, created_by }
  • shared::types::ProfileHnswConfig — mirror of vectord's HnswConfig to avoid cross-crate dep cycle (defaults ec=80 es=30 matching Phase 15 winner)
  • Registry::{put_profile, get_profile, list_profiles, delete_profile} persisted at _catalog/profiles/{id}.json, validates bindings exist (raw dataset OR AiView)
  • Endpoints: POST/GET /catalog/profiles, GET/DELETE /catalog/profiles/{id}
  • 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
  • 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
  • Fixed refresh to register new index metadata (was silently no-op for first-time indexes)
  • 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
• Phase E: Soft deletes (tombstones) — 2026-04-16
  • shared::types::Tombstone — { dataset, row_key_column, row_key_value, deleted_at, actor, reason }
  • 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)
  • 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)
  • Registry::add_tombstone / list_tombstones
  • Endpoint: POST /catalog/datasets/by-name/{name}/tombstone accepting {row_key_column, row_key_values[], actor, reason}; companion GET lists active tombstones
  • 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
  • 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
  • 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)
• Phase D: AI-safe views — 2026-04-16
  • shared::types::AiView — name, base_dataset, columns whitelist, optional row_filter, column_redactions
  • shared::types::Redaction — Null | Hash | Mask { keep_prefix, keep_suffix }
  • Registry::put_view / get_view / list_views / delete_view persisted to _catalog/views/{name}.json
  • queryd::context registers each view as a DataFusion view with the safe projection + filter + redactions baked into the SELECT
  • Endpoints: POST/GET /catalog/views, GET/DELETE /catalog/views/{name}
  • 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.
  • Capability surface — raw candidates still accessible by name; Phase 13 access control is the layer that enforces who can query what
• Phase C: Decoupled embedding refresh — 2026-04-16
  • DatasetManifest: last_embedded_at, embedding_stale_since, embedding_refresh_policy (Manual | OnAppend | Scheduled)
  • Registry::mark_embeddings_stale / clear_embeddings_stale / stale_datasets
  • Ingest paths (CSV pipeline + Postgres streaming) auto-mark-stale when writing to an already-embedded dataset
  • vectord::refresh::refresh_index — reads dataset, diffs doc_ids vs existing embeddings, embeds only new rows, writes combined index, clears stale
  • POST /vectors/refresh/{dataset} + GET /vectors/stale
  • Id columns accept Utf8, Int32, Int64
  • 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
• Phase 16.2/16.5: Background autotune agent + ingest-triggered re-trials — 2026-04-17
  • vectord::agent — ε-greedy proposer, rate-limited, cooldown-gated, tokio background task
  • Ingest paths push DatasetAppended triggers to agent queue
  • Endpoints: GET /vectors/agent/status, POST /vectors/agent/stop, POST /vectors/agent/enqueue/{idx}
  • [agent] config section in lakehouse.toml (enabled, cycle_interval, cooldown, min_recall, max_trials/hr)
  • 3 unit tests
• Phase 17 VRAM gate: Two-profile sequential swap — 2026-04-17
  • Sidecar: POST /admin/unload (keep_alive=0), POST /admin/preload, GET /admin/vram (nvidia-smi + Ollama /api/ps)
  • AiClient::unload_model / preload_model / vram_snapshot
  • VectorState.active_profile singleton — activate swaps models, deactivate unloads
  • Verified: staffing-recruiter (qwen2.5) ↔ docs-assistant (mistral) — only one model in VRAM at a time
• MySQL streaming connector — 2026-04-17
  • my_stream.rs mirrors pg_stream: DSN parsing, OFFSET pagination, Arrow type mapping, Parquet streaming
  • POST /ingest/mysql with PII detection, lineage, agent trigger
  • Verified end-to-end on live MariaDB (10 rows, 9 columns, round-tripped all types)
  • 6 DSN + type-mapping unit tests
• Phase 18 hybrid: vectord-lance production crate — 2026-04-17
  • Firewall crate (Arrow 57 / Lance 4, separate from main Arrow 55 / DF 47 stack)
  • Public API: migrate_from_parquet, build_index (IVF_PQ), search, get_by_doc_id, append, build_scalar_index, stats
  • lance_backend::LanceRegistry resolves bucket → URI per index
  • VectorBackend { Parquet | Lance } enum on ModelProfile + IndexMeta
  • 8 HTTP endpoints under /vectors/lance/* (migrate, index, search, doc, append, stats, scalar-index, recall)
  • Profile-driven routing: POST /vectors/profile/{id}/search auto-routes to Lance when profile.vector_backend=lance
  • Auto-migrate + auto-index on activation
  • Measured on real 100K × 768d: migrate 0.57s, IVF_PQ build 16.2s (14× faster than HNSW 230s), search 23ms, append 100 rows 3.3ms, doc_id fetch 3.5ms (with scalar btree)
  • IVF_PQ recall@10 = 0.805 (HNSW = 1.000) — measured via /vectors/lance/recall/{idx} harness
• Phase E.3: Scheduled ingest — 2026-04-17
  • ingestd::schedule module: ScheduleDef, ScheduleStore (JSON at _schedules/{id}.json), Scheduler tokio task
  • Supports MySQL + Postgres sources on interval triggers (Cron variant defined, parsing stubbed)
  • 6 CRUD endpoints under /ingest/schedules/* + run-now manual trigger
  • Full catalog integration: PII, lineage, mark-stale, agent trigger
  • 6 unit tests
• PDF OCR via Tesseract — 2026-04-17
  • Two-tier: lopdf text extraction → Tesseract 5.5 fallback for scanned/image PDFs
  • Extracts embedded XObject /Image streams, shells to tesseract --oem 3 --psm 6
  • Same schema (source_file, page_number, text_content) — downstream unchanged
• Fine-tuned domain models
• Multi-node query distribution

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52+ unit tests | 13 crates | 19 ADRs | 2.47M rows | 100K vectors | Hybrid Parquet+HNSW ⊕ Lance Latest: 2026-04-17 — 8 commits shipping Phase 16.2 through Phase 18