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* feat(postgres): Add RuVector Postgres v2 implementation plan Complete specification for RuVector Postgres v2 with: Architecture: - PostgreSQL extension (pgrx) with hybrid architecture - SQL handles ACID/joins, RuVector engine handles vectors/graphs/learning - Backward compatible with pgvector SQL surface - Shared memory IPC with bounded contracts (64KB inline, 16MB shared) 4-Phase Implementation: - Phase 1: pgvector-compatible search (1a: function-based, 1b: Index AM) - Phase 2: Tiered storage with compression and exactness GUC - Phase 3: Graph engine with Cypher and SQL join keys - Phase 4: Dynamic mincut integrity gating (key differentiator) Key Technical Details: - lambda_cut: Minimum cut value via Stoer-Wagner (PRIMARY integrity metric) - lambda2: Algebraic connectivity (OPTIONAL drift signal) - DIFFERENT from mincut! - Contracted operational graph (~1000 nodes) - never compute on full similarity graph - Hysteresis model with consecutive samples and cooldown - Operation risk classification (Low/Medium/High) - MVCC visibility with incremental paging API - WAL replay with idempotency and LSN ordering - Partition map versioning and epoch fencing for cluster mode Files: - 00-overview.md: Architecture, consistency contract, benchmark spec - 01-sql-schema.md: SQL schema and types - 02-background-workers.md: IPC contract, mincut worker - 03-index-access-methods.md: Index AM specification - 04-integrity-events.md: Events, hysteresis, operation classes - 05-phase1-pgvector-compat.md: Phase 1a/1b incremental path - 06-phase2-tiered-storage.md: Tiered storage with GUC exactness - 07-phase3-graph-cypher.md: Graph engine with SQL joins - 08-phase4-integrity-control.md: Mincut gating with Stoer-Wagner - 09-migration-guide.md: Migration from pgvector - 10-consistency-replication.md: Consistency and replication model 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs(postgres): Rewrite v2 overview with compelling framing Replace technical executive summary with clear explanation of why RuVector matters: - From symptom monitoring to causal monitoring - Mincut as leading indicator, not metric - Algorithm becomes control signal (control plane, not analytics) - Failure mode class change: cascading → graceful degradation - Explainable operations via witness edges Key message: "We're not making vector search faster. We're making vector infrastructure survivable." 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * feat(postgres): Add hybrid search, multi-tenancy, and self-healing specs Three high-impact additions to RuVector Postgres v2: ## 11-hybrid-search.md - BM25 + Vector Fusion - Single query combines semantic and keyword search - Proper BM25 implementation (not just ts_rank) - Fusion algorithms: RRF (default), linear, learned - Integrity-aware degradation (stress → single branch) - Parallel branch execution - GUC configuration ## 12-multi-tenancy.md - First-Class Tenant Isolation - SET ruvector.tenant_id for transparent scoping - Isolation levels: shared, partition, dedicated - Automatic promotion based on vector count - Per-tenant integrity (stress in one doesn't affect others) - Per-tenant contracted graphs - Resource quotas and rate limiting - Fair scheduling (no noisy neighbors) - RLS integration for defense in depth ## 13-self-healing.md - Automated Remediation - Completes the control loop: sensor → actuator - Problem classification from witness edges: - Hotspot congestion - Centroid skew - Replication lag - Maintenance contention - Index fragmentation - Memory pressure - Built-in strategies: - Rebalance partitions - Pause maintenance jobs - Throttle ingestion - Scale read replicas (K8s) - Compact fragmented indexes - Safety: reversible actions, blast radius limits - Learning: outcome tracking, strategy weight updates - The key insight: "We built the sensor. Now we build the actuator." 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * feat(intelligence): Add self-learning intelligence layer with v3 features Comprehensive intelligence system for Claude Code hooks: Core Features (v2): - VectorMemory with @ruvector/core native HNSW (150x faster) - Hyperbolic distance (Poincaré ball) for hierarchical embeddings - ReasoningBank with Q-learning and pattern decay (7-day half-life) - Confidence Calibration tracking (predicted vs actual accuracy) - A/B Testing with 10% holdout for measuring intelligence lift - Feedback Loop for tracking suggestion follow-through - Active Learning for identifying uncertain states v3 Improvements: - Error Pattern Learning (Rust E0xxx, TypeScript TSxxxx, npm errors) - File Sequence Learning (tracks which files are edited together) - Test Suggestion Triggers (suggests cargo test after source edits) - Hive-Mind swarm coordination (11 agents, 38 edges) Pretrained from memory.db: - 7,697 commands processed - 4,023 vector memories - 117 Q-table states with decay metadata - 8,520 calibration samples Anti-overfitting measures: - Q-values capped at 0.8, floored at -0.5 - Decaying learning rate: 0.3/sqrt(count) - Pattern decay with timestamps 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix(intelligence): Fix Q-table lookups - learning now has real effect Three critical bugs were preventing the intelligence layer from using learned patterns: 1. State format mismatch: CLI used spaces ("editing rs in project") but Q-table used underscores ("edit_rs_in_project") - Fixed in cli.js: all states now use underscore format 2. stateKey() hyphen normalization: Function converted hyphens to underscores, but Q-table keys had hyphens (e.g. "ruvector-core") - Fixed regex: /[^a-z0-9-]+/g preserves hyphens 3. A/B testing control group: 10% random sessions ignored learning - Reduced holdout to 5% with persistent session assignment - Added INTELLIGENCE_MODE=treatment env override for development Result: Agent recommendations now show 80% confidence for Rust files using learned Q-values, instead of 0% with random selection. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix(hooks): Display intelligence guidance to Claude in foreground Critical fix: PreToolUse hooks were running in background (&) which meant Claude never saw the intelligence output. Now: - PreToolUse: Foreground execution (Claude sees guidance) - pre-edit: Shows recommended agent + confidence + similar edits - pre-command: Shows command patterns + suggestions - Added 3s timeout to prevent blocking - PostToolUse: Background execution (async learning) - post-edit: Records success/failure, learns patterns - post-command: Captures errors, updates Q-values - SessionStart: New hook shows learned patterns at session start - Displays pattern count, memory stats - Shows top 3 learned state-action pairs with Q-values Claude now receives self-learning guidance like: "🧠 Intelligence Analysis: 📁 ruvector-core/lib.rs 🤖 Recommended: rust-developer (80% confidence) 📚 3 similar past edits found" 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
719 lines
19 KiB
Markdown
719 lines
19 KiB
Markdown
# RuVector Postgres v2 - Multi-Tenancy Model
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## Why Multi-Tenancy Matters
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Every SaaS application needs tenant isolation. Without native support, teams build:
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- Separate databases per tenant (operational nightmare)
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- Manual partition schemes (error-prone)
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- Application-level filtering (security risk)
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RuVector provides **first-class multi-tenancy** with:
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- Tenant-isolated search (data never leaks)
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- Per-tenant integrity monitoring (one bad tenant doesn't sink others)
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- Efficient shared infrastructure (cost-effective)
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- Row-level security integration (PostgreSQL-native)
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---
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## Design Goals
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1. **Zero data leakage** — Tenant A never sees Tenant B's vectors
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2. **Per-tenant integrity** — Stress in one tenant doesn't affect others
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3. **Fair resource allocation** — No noisy neighbor problems
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4. **Transparent to queries** — SET tenant, then normal SQL
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5. **Efficient storage** — Shared indexes where safe, isolated where needed
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---
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## Architecture
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```
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+------------------------------------------------------------------+
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| Application |
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| SET ruvector.tenant_id = 'acme-corp'; |
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| SELECT * FROM embeddings ORDER BY vec <-> $q LIMIT 10; |
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+------------------------------------------------------------------+
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+------------------------------------------------------------------+
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| Tenant Context Layer |
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| - Validates tenant_id |
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| - Injects tenant filter into all operations |
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| - Routes to tenant-specific resources |
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+------------------------------------------------------------------+
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+---------------+---------------+
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| |
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+--------v--------+ +---------v---------+
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| Shared Index | | Tenant Indexes |
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| (small tenants)| | (large tenants) |
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+--------+--------+ +---------+---------+
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| |
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+---------------+---------------+
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+------------------------------------------------------------------+
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| Per-Tenant Integrity |
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| - Separate contracted graphs |
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| - Independent state machines |
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| - Isolated throttling policies |
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+------------------------------------------------------------------+
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```
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---
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## SQL Interface
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### Setting Tenant Context
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```sql
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-- Set tenant for session (required before any operation)
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SET ruvector.tenant_id = 'acme-corp';
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-- Or per-transaction
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BEGIN;
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SET LOCAL ruvector.tenant_id = 'acme-corp';
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-- ... operations ...
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COMMIT;
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-- Verify current tenant
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SELECT current_setting('ruvector.tenant_id');
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```
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### Tenant-Transparent Operations
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```sql
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-- Once tenant is set, all operations are automatically scoped
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SET ruvector.tenant_id = 'acme-corp';
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-- Insert only sees/affects acme-corp data
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INSERT INTO embeddings (content, vec) VALUES ('doc', $embedding);
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-- Search only returns acme-corp results
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SELECT * FROM embeddings ORDER BY vec <-> $query LIMIT 10;
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-- Delete only affects acme-corp
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DELETE FROM embeddings WHERE id = 123;
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```
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### Admin Operations (Cross-Tenant)
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```sql
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-- Superuser can query across tenants
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SET ruvector.tenant_id = '*'; -- Wildcard (admin only)
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-- View all tenants
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SELECT * FROM ruvector_tenants();
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-- View tenant stats
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SELECT * FROM ruvector_tenant_stats('acme-corp');
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-- Migrate tenant to dedicated index
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SELECT ruvector_tenant_isolate('acme-corp');
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```
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---
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## Schema Design
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### Tenant Registry
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```sql
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CREATE TABLE ruvector.tenants (
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id TEXT PRIMARY KEY,
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display_name TEXT,
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-- Resource limits
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max_vectors BIGINT DEFAULT 1000000,
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max_collections INTEGER DEFAULT 10,
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max_qps INTEGER DEFAULT 100,
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-- Isolation level
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isolation_level TEXT DEFAULT 'shared' CHECK (isolation_level IN (
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'shared', -- Shared index with tenant filter
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'partition', -- Dedicated partition in shared index
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'dedicated' -- Separate physical index
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)),
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-- Integrity settings
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integrity_enabled BOOLEAN DEFAULT true,
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integrity_policy_id INTEGER REFERENCES ruvector.integrity_policies(id),
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-- Metadata
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metadata JSONB DEFAULT '{}'::jsonb,
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created_at TIMESTAMPTZ DEFAULT NOW(),
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suspended_at TIMESTAMPTZ, -- Non-null = suspended
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-- Stats (updated by background worker)
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vector_count BIGINT DEFAULT 0,
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storage_bytes BIGINT DEFAULT 0,
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last_access TIMESTAMPTZ
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);
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CREATE INDEX idx_tenants_isolation ON ruvector.tenants(isolation_level);
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CREATE INDEX idx_tenants_suspended ON ruvector.tenants(suspended_at) WHERE suspended_at IS NOT NULL;
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```
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### Tenant-Aware Collections
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```sql
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-- Collections can be tenant-specific or shared
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CREATE TABLE ruvector.collections (
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id SERIAL PRIMARY KEY,
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name TEXT NOT NULL,
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tenant_id TEXT REFERENCES ruvector.tenants(id), -- NULL = shared
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-- ... other columns from 01-sql-schema.md ...
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UNIQUE (name, tenant_id) -- Same name allowed for different tenants
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);
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-- Tenant-scoped view
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CREATE VIEW ruvector.my_collections AS
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SELECT * FROM ruvector.collections
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WHERE tenant_id = current_setting('ruvector.tenant_id', true)
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OR tenant_id IS NULL; -- Shared collections visible to all
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```
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### Tenant Column in Data Tables
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```sql
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-- User tables include tenant_id column
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CREATE TABLE embeddings (
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id BIGSERIAL PRIMARY KEY,
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tenant_id TEXT NOT NULL DEFAULT current_setting('ruvector.tenant_id'),
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content TEXT,
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vec vector(1536),
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created_at TIMESTAMPTZ DEFAULT NOW(),
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CONSTRAINT fk_tenant FOREIGN KEY (tenant_id)
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REFERENCES ruvector.tenants(id) ON DELETE CASCADE
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);
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-- Partial index per tenant (for dedicated isolation)
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CREATE INDEX idx_embeddings_vec_tenant_acme
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ON embeddings USING ruhnsw (vec vector_cosine_ops)
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WHERE tenant_id = 'acme-corp';
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-- Or composite index for shared isolation
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CREATE INDEX idx_embeddings_vec_shared
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ON embeddings USING ruhnsw (vec vector_cosine_ops);
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-- Engine internally filters by tenant_id
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```
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---
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## Row-Level Security Integration
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### RLS Policies
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```sql
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-- Enable RLS on data tables
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ALTER TABLE embeddings ENABLE ROW LEVEL SECURITY;
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-- Tenant isolation policy
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CREATE POLICY tenant_isolation ON embeddings
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USING (tenant_id = current_setting('ruvector.tenant_id', true))
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WITH CHECK (tenant_id = current_setting('ruvector.tenant_id', true));
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-- Admin bypass policy
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CREATE POLICY admin_access ON embeddings
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FOR ALL
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TO ruvector_admin
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USING (true)
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WITH CHECK (true);
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```
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### Automatic Policy Creation
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```sql
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-- Helper function to set up RLS for a table
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CREATE FUNCTION ruvector_enable_tenant_rls(
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p_table_name TEXT,
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p_tenant_column TEXT DEFAULT 'tenant_id'
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) RETURNS void AS $$
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BEGIN
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-- Enable RLS
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EXECUTE format('ALTER TABLE %I ENABLE ROW LEVEL SECURITY', p_table_name);
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-- Create isolation policy
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EXECUTE format(
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'CREATE POLICY tenant_isolation ON %I
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USING (%I = current_setting(''ruvector.tenant_id'', true))
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WITH CHECK (%I = current_setting(''ruvector.tenant_id'', true))',
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p_table_name, p_tenant_column, p_tenant_column
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);
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-- Create admin bypass
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EXECUTE format(
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'CREATE POLICY admin_bypass ON %I FOR ALL TO ruvector_admin USING (true)',
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p_table_name
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);
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END;
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$$ LANGUAGE plpgsql;
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-- Usage
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SELECT ruvector_enable_tenant_rls('embeddings');
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SELECT ruvector_enable_tenant_rls('documents');
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```
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---
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## Isolation Levels
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### Shared (Default)
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All tenants share one index. Engine filters by tenant_id.
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```
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Pros:
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+ Most memory-efficient
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+ Fastest for small tenants
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+ Simple management
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Cons:
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- Some cross-tenant cache pollution
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- Shared integrity state
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Best for: < 100K vectors per tenant
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```
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### Partition
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Tenants get dedicated partitions within shared index structure.
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```
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Pros:
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+ Better cache isolation
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+ Per-partition integrity
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+ Easy promotion to dedicated
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Cons:
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- Some overhead per partition
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- Still shares top-level structure
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Best for: 100K - 10M vectors per tenant
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```
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### Dedicated
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Tenant gets completely separate physical index.
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```
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Pros:
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+ Complete isolation
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+ Independent scaling
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+ Custom index parameters
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Cons:
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- Higher memory overhead
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+ More management complexity
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Best for: > 10M vectors, enterprise tenants, compliance requirements
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```
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### Automatic Promotion
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```sql
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-- Configure auto-promotion thresholds
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SELECT ruvector_tenant_set_policy('{
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"auto_promote_to_partition": 100000, -- vectors
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"auto_promote_to_dedicated": 10000000,
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"check_interval": "1 hour"
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}'::jsonb);
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```
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```rust
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// Background worker checks and promotes
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pub fn check_tenant_promotion(tenant_id: &str) -> Option<IsolationLevel> {
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let stats = get_tenant_stats(tenant_id)?;
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let policy = get_promotion_policy()?;
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if stats.vector_count > policy.dedicated_threshold {
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Some(IsolationLevel::Dedicated)
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} else if stats.vector_count > policy.partition_threshold {
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Some(IsolationLevel::Partition)
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} else {
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None
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}
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}
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```
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---
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## Per-Tenant Integrity
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### Separate Contracted Graphs
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```sql
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-- Each tenant gets its own contracted graph
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CREATE TABLE ruvector.tenant_contracted_graph (
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tenant_id TEXT NOT NULL REFERENCES ruvector.tenants(id),
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collection_id INTEGER NOT NULL,
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node_type TEXT NOT NULL,
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node_id BIGINT NOT NULL,
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-- ... same as contracted_graph ...
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PRIMARY KEY (tenant_id, collection_id, node_type, node_id)
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);
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```
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### Independent State Machines
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```rust
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// Per-tenant integrity state
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pub struct TenantIntegrityState {
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tenant_id: String,
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state: IntegrityState,
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lambda_cut: f32,
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consecutive_samples: u32,
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last_transition: Instant,
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cooldown_until: Option<Instant>,
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}
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// Tenant stress doesn't affect other tenants
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pub fn check_tenant_gate(tenant_id: &str, operation: &str) -> GateResult {
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let state = get_tenant_integrity_state(tenant_id);
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apply_policy(state, operation)
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}
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```
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### Tenant-Specific Policies
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```sql
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-- Each tenant can have custom thresholds
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INSERT INTO ruvector.integrity_policies (tenant_id, name, threshold_high, threshold_low)
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VALUES
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('acme-corp', 'enterprise', 0.6, 0.3), -- Stricter
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('startup-xyz', 'standard', 0.4, 0.15); -- Default
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```
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---
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## Resource Quotas
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### Quota Enforcement
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```sql
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-- Quota table
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CREATE TABLE ruvector.tenant_quotas (
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tenant_id TEXT PRIMARY KEY REFERENCES ruvector.tenants(id),
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max_vectors BIGINT NOT NULL DEFAULT 1000000,
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max_storage_gb REAL NOT NULL DEFAULT 10.0,
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max_qps INTEGER NOT NULL DEFAULT 100,
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max_concurrent INTEGER NOT NULL DEFAULT 10,
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-- Current usage (updated by triggers/workers)
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current_vectors BIGINT DEFAULT 0,
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current_storage_gb REAL DEFAULT 0,
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-- Rate limiting state
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request_count INTEGER DEFAULT 0,
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window_start TIMESTAMPTZ DEFAULT NOW()
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);
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-- Check quota before insert
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CREATE FUNCTION ruvector_check_quota() RETURNS TRIGGER AS $$
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DECLARE
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v_quota RECORD;
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BEGIN
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SELECT * INTO v_quota
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FROM ruvector.tenant_quotas
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WHERE tenant_id = NEW.tenant_id;
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IF v_quota.current_vectors >= v_quota.max_vectors THEN
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RAISE EXCEPTION 'Tenant % has exceeded vector quota', NEW.tenant_id;
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END IF;
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RETURN NEW;
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END;
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$$ LANGUAGE plpgsql;
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CREATE TRIGGER check_quota_before_insert
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BEFORE INSERT ON embeddings
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FOR EACH ROW EXECUTE FUNCTION ruvector_check_quota();
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```
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### Rate Limiting
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```rust
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// Token bucket rate limiter per tenant
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pub struct TenantRateLimiter {
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buckets: DashMap<String, TokenBucket>,
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}
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impl TenantRateLimiter {
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pub fn check(&self, tenant_id: &str, tokens: u32) -> RateLimitResult {
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let bucket = self.buckets.entry(tenant_id.to_string())
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.or_insert_with(|| TokenBucket::new(
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get_tenant_qps_limit(tenant_id),
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));
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if bucket.try_acquire(tokens) {
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RateLimitResult::Allowed
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} else {
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RateLimitResult::Limited {
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retry_after_ms: bucket.time_to_refill(tokens),
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}
|
|
}
|
|
}
|
|
}
|
|
```
|
|
|
|
### Fair Scheduling
|
|
|
|
```rust
|
|
// Weighted fair queue for search requests
|
|
pub struct FairScheduler {
|
|
queues: HashMap<String, VecDeque<SearchRequest>>,
|
|
weights: HashMap<String, f32>, // Based on tier/quota
|
|
}
|
|
|
|
impl FairScheduler {
|
|
pub fn next(&mut self) -> Option<SearchRequest> {
|
|
// Weighted round-robin across tenants
|
|
// Prevents one tenant from monopolizing resources
|
|
let total_weight: f32 = self.weights.values().sum();
|
|
|
|
for (tenant_id, queue) in &mut self.queues {
|
|
let weight = self.weights.get(tenant_id).unwrap_or(&1.0);
|
|
let share = weight / total_weight;
|
|
|
|
// Probability of selecting this tenant's request
|
|
if rand::random::<f32>() < share {
|
|
if let Some(req) = queue.pop_front() {
|
|
return Some(req);
|
|
}
|
|
}
|
|
}
|
|
|
|
// Fallback: any available request
|
|
self.queues.values_mut()
|
|
.find_map(|q| q.pop_front())
|
|
}
|
|
}
|
|
```
|
|
|
|
---
|
|
|
|
## Tenant Lifecycle
|
|
|
|
### Create Tenant
|
|
|
|
```sql
|
|
SELECT ruvector_tenant_create('new-customer', '{
|
|
"display_name": "New Customer Inc.",
|
|
"max_vectors": 5000000,
|
|
"max_qps": 200,
|
|
"isolation_level": "shared",
|
|
"integrity_enabled": true
|
|
}'::jsonb);
|
|
```
|
|
|
|
### Suspend Tenant
|
|
|
|
```sql
|
|
-- Suspend (stops all operations, keeps data)
|
|
SELECT ruvector_tenant_suspend('bad-actor');
|
|
|
|
-- Resume
|
|
SELECT ruvector_tenant_resume('bad-actor');
|
|
```
|
|
|
|
### Delete Tenant
|
|
|
|
```sql
|
|
-- Soft delete (marks for cleanup)
|
|
SELECT ruvector_tenant_delete('churned-customer');
|
|
|
|
-- Hard delete (immediate, for compliance)
|
|
SELECT ruvector_tenant_delete('churned-customer', hard := true);
|
|
```
|
|
|
|
### Migrate Isolation Level
|
|
|
|
```sql
|
|
-- Promote to dedicated (online, no downtime)
|
|
SELECT ruvector_tenant_migrate('enterprise-customer', 'dedicated');
|
|
|
|
-- Status check
|
|
SELECT * FROM ruvector_tenant_migration_status('enterprise-customer');
|
|
```
|
|
|
|
---
|
|
|
|
## Shared Memory Layout
|
|
|
|
```rust
|
|
// Per-tenant state in shared memory
|
|
#[repr(C)]
|
|
pub struct TenantSharedState {
|
|
tenant_id_hash: u64, // Fast lookup key
|
|
integrity_state: u8, // 0=normal, 1=stress, 2=critical
|
|
lambda_cut: f32, // Current mincut value
|
|
request_count: AtomicU32, // For rate limiting
|
|
last_request_epoch: AtomicU64, // Rate limit window
|
|
flags: AtomicU32, // Suspended, migrating, etc.
|
|
}
|
|
|
|
// Tenant lookup table
|
|
pub struct TenantRegistry {
|
|
states: [TenantSharedState; MAX_TENANTS], // Fixed array in shmem
|
|
index: HashMap<String, usize>, // Heap-based lookup
|
|
}
|
|
```
|
|
|
|
---
|
|
|
|
## Monitoring
|
|
|
|
### Per-Tenant Metrics
|
|
|
|
```sql
|
|
-- Tenant dashboard
|
|
SELECT
|
|
t.id,
|
|
t.display_name,
|
|
t.isolation_level,
|
|
tq.current_vectors,
|
|
tq.max_vectors,
|
|
ROUND(100.0 * tq.current_vectors / tq.max_vectors, 1) AS usage_pct,
|
|
ts.integrity_state,
|
|
ts.lambda_cut,
|
|
ts.avg_search_latency_ms,
|
|
ts.searches_last_hour
|
|
FROM ruvector.tenants t
|
|
JOIN ruvector.tenant_quotas tq ON t.id = tq.tenant_id
|
|
JOIN ruvector.tenant_stats ts ON t.id = ts.tenant_id
|
|
ORDER BY tq.current_vectors DESC;
|
|
```
|
|
|
|
### Prometheus Metrics
|
|
|
|
```
|
|
# Per-tenant metrics
|
|
ruvector_tenant_vectors{tenant="acme-corp"} 1234567
|
|
ruvector_tenant_integrity_state{tenant="acme-corp"} 1
|
|
ruvector_tenant_lambda_cut{tenant="acme-corp"} 0.72
|
|
ruvector_tenant_search_latency_p99{tenant="acme-corp"} 15.2
|
|
ruvector_tenant_qps{tenant="acme-corp"} 45.3
|
|
ruvector_tenant_quota_usage{tenant="acme-corp",resource="vectors"} 0.62
|
|
```
|
|
|
|
---
|
|
|
|
## Security Considerations
|
|
|
|
### Tenant ID Validation
|
|
|
|
```rust
|
|
// Validate tenant_id before any operation
|
|
pub fn validate_tenant_context() -> Result<String, Error> {
|
|
let tenant_id = get_guc("ruvector.tenant_id")?;
|
|
|
|
// Check not empty
|
|
if tenant_id.is_empty() {
|
|
return Err(Error::NoTenantContext);
|
|
}
|
|
|
|
// Check tenant exists and not suspended
|
|
let tenant = get_tenant(&tenant_id)?;
|
|
if tenant.suspended_at.is_some() {
|
|
return Err(Error::TenantSuspended);
|
|
}
|
|
|
|
Ok(tenant_id)
|
|
}
|
|
```
|
|
|
|
### Audit Logging
|
|
|
|
```sql
|
|
-- Tenant operations audit log
|
|
CREATE TABLE ruvector.tenant_audit_log (
|
|
id BIGSERIAL PRIMARY KEY,
|
|
tenant_id TEXT NOT NULL,
|
|
operation TEXT NOT NULL, -- search, insert, delete, etc.
|
|
user_id TEXT, -- Application user
|
|
details JSONB,
|
|
ip_address INET,
|
|
created_at TIMESTAMPTZ DEFAULT NOW()
|
|
);
|
|
|
|
-- Enabled via GUC
|
|
SET ruvector.audit_enabled = true;
|
|
```
|
|
|
|
### Cross-Tenant Prevention
|
|
|
|
```rust
|
|
// Engine-level enforcement (defense in depth)
|
|
pub fn execute_search(request: &SearchRequest) -> Result<SearchResults, Error> {
|
|
let context_tenant = validate_tenant_context()?;
|
|
|
|
// Double-check request matches context
|
|
if let Some(req_tenant) = &request.tenant_id {
|
|
if req_tenant != &context_tenant {
|
|
// Log security event
|
|
log_security_event("tenant_mismatch", &context_tenant, req_tenant);
|
|
return Err(Error::TenantMismatch);
|
|
}
|
|
}
|
|
|
|
// Execute with tenant filter
|
|
execute_search_internal(request, &context_tenant)
|
|
}
|
|
```
|
|
|
|
---
|
|
|
|
## Testing Requirements
|
|
|
|
### Isolation Tests
|
|
- Tenant A cannot see Tenant B's data
|
|
- Tenant A's stress doesn't affect Tenant B's operations
|
|
- Suspended tenant cannot perform any operations
|
|
|
|
### Performance Tests
|
|
- Shared isolation: < 5% overhead vs single-tenant
|
|
- Dedicated isolation: equivalent to single-tenant
|
|
- Rate limiting adds < 1ms latency
|
|
|
|
### Scale Tests
|
|
- 1000+ tenants on shared infrastructure
|
|
- 100+ tenants with dedicated isolation
|
|
- Tenant migration under load
|
|
|
|
---
|
|
|
|
## Example: SaaS Application
|
|
|
|
```python
|
|
# Application code
|
|
class VectorService:
|
|
def __init__(self, db_pool):
|
|
self.pool = db_pool
|
|
|
|
def search(self, tenant_id: str, query_vec: list, k: int = 10):
|
|
with self.pool.connection() as conn:
|
|
# Set tenant context
|
|
conn.execute("SET ruvector.tenant_id = %s", [tenant_id])
|
|
|
|
# Search (automatically scoped to tenant)
|
|
results = conn.execute("""
|
|
SELECT id, content, vec <-> %s AS distance
|
|
FROM embeddings
|
|
ORDER BY vec <-> %s
|
|
LIMIT %s
|
|
""", [query_vec, query_vec, k])
|
|
|
|
return results.fetchall()
|
|
|
|
def insert(self, tenant_id: str, content: str, vec: list):
|
|
with self.pool.connection() as conn:
|
|
conn.execute("SET ruvector.tenant_id = %s", [tenant_id])
|
|
|
|
# Insert (tenant_id auto-populated from context)
|
|
conn.execute("""
|
|
INSERT INTO embeddings (content, vec)
|
|
VALUES (%s, %s)
|
|
""", [content, vec])
|
|
```
|