David Snelling
3e8b9aacc8
Major architectural improvements and critical bug fixes: ## COW Always-On Architecture - Removed cowEnabled flag from BaseStorage (COW cannot be disabled) - Eliminated marker file system (checkClearMarker, createClearMarker) - Simplified all code paths to assume COW is always enabled - COW automatically re-initializes after clear() operations ## Critical Bug Fix: Cloud Storage clear() - Fixed GCS clear() using correct paths (branches/ instead of entities/nouns/) - Fixed S3Compatible clear() path structure - Fixed R2 clear() implementation - Fixed Azure, FileSystem, OPFS, Memory clear() COW flag handling - clear() now deletes: branches/, _cow/, _system/ - Result: Cloud buckets can now be fully cleared (previously impossible) ## Container Memory Detection - Auto-detect Docker/K8s/Cloud Run memory limits (cgroup v1/v2) - Smart memory allocation (75% graph data, 25% query operations) - Environment variable support (CLOUD_RUN_MEMORY, MEMORY_LIMIT) - Production-grade containerized deployment support ## CommitLog streamHistory Feature - Added streamable commit history with pagination - Efficient memory usage for large commit histories - Support for branch filtering and time ranges ## Comprehensive Storage Documentation - Complete v5.11.0 file structure reference - Detailed path construction algorithms - 8 common storage scenarios with examples - Type-first storage, sharding, COW architecture explained - Public docs: docs/architecture/data-storage-architecture.md (1063 lines) ## Files Modified (14 files) - All 8 storage adapters (GCS, S3, R2, Azure, FS, OPFS, Memory, Historical) - BaseStorage core architecture - CommitLog with streaming - Brainy memory configuration - Parameter validation with container detection - Storage architecture documentation ## Breaking Changes NONE - COW was already enabled by default. This removes the ability to disable it. ## Migration No action required. Upgrade and clear() will work correctly on cloud storage. ## Impact - Users can now clear cloud storage buckets completely - No more corrupted buckets after clear() operations - Container deployments automatically optimize memory allocation - COW is mandatory and always enabled (safer, simpler) v5.11.0 - Production ready
1458 lines
44 KiB
Markdown
1458 lines
44 KiB
Markdown
# Brainy Data Storage Architecture (v5.11.0)
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**Complete file structure reference for all storage backends**
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This document explains how Brainy stores, indexes, and scales data across all storage backends (GCS, S3, R2, Azure, filesystem, OPFS, memory).
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---
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## Table of Contents
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1. [Complete File Structure](#1-complete-file-structure)
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2. [What Gets Stored](#2-what-gets-stored)
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3. [The 4 Indexes](#3-the-4-indexes)
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4. [Sharding Strategy](#4-sharding-strategy)
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5. [COW (Copy-on-Write) Architecture](#5-cow-copy-on-write-architecture)
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6. [Type-First Storage](#6-type-first-storage)
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7. [VFS (Virtual File System)](#7-vfs-virtual-file-system)
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8. [Storage Backend Mapping](#8-storage-backend-mapping)
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9. [Performance Characteristics](#9-performance-characteristics)
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---
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## 1. Complete File Structure
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### v5.11.0 Full Directory Tree
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```
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brainy-data/ # Root directory (or bucket name for cloud)
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│
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├── branches/ # Branch-scoped storage (v5.4.0+, COW always-on)
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│ ├── main/ # Main branch (default)
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│ │ └── entities/
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│ │ ├── nouns/
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│ │ │ ├── Character/ # Type-first: entities organized by type
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│ │ │ │ ├── vectors/
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│ │ │ │ │ ├── 00/ # UUID-based sharding (256 shards)
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│ │ │ │ │ │ ├── 001234...uuid.json # HNSW vector + connections
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│ │ │ │ │ │ └── 00abcd...uuid.json
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│ │ │ │ │ ├── 01/ ... fe/
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│ │ │ │ │ └── ff/
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│ │ │ │ └── metadata/
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│ │ │ │ ├── 00/
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│ │ │ │ │ ├── 001234...uuid.json # Business metadata only
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│ │ │ │ │ └── 00abcd...uuid.json
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│ │ │ │ ├── 01/ ... fe/
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│ │ │ │ └── ff/
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│ │ │ │
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│ │ │ ├── Place/ # Another type
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│ │ │ │ ├── vectors/
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│ │ │ │ │ ├── 00/ ... ff/
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│ │ │ │ └── metadata/
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│ │ │ │ ├── 00/ ... ff/
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│ │ │ │
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│ │ │ ├── Concept/
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│ │ │ ├── Organization/
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│ │ │ ├── Event/
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│ │ │ └── [42 total noun types]
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│ │ │
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│ │ └── verbs/
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│ │ ├── Knows/ # Type-first for relationships too
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│ │ │ ├── vectors/
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│ │ │ │ ├── 00/ ... ff/
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│ │ │ └── metadata/
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│ │ │ ├── 00/ ... ff/
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│ │ │
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│ │ ├── LocatedIn/
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│ │ ├── WorksFor/
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│ │ └── [127 total verb types]
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│ │
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│ ├── feature-branch-1/ # Git-like feature branches
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│ │ └── entities/
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│ │ └── [same structure as main]
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│ │
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│ └── user-workspace-alice/ # User-specific branches
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│ └── entities/
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│ └── [same structure as main]
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│
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├── _cow/ # Copy-on-Write version control
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│ ├── commits/ # Git-like commit objects
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│ │ ├── 00/
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│ │ │ ├── 00a1b2c3...sha256.json # Commit metadata
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│ │ │ └── 00d4e5f6...sha256.json
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│ │ ├── 01/ ... fe/
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│ │ └── ff/
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│ │
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│ ├── trees/ # Directory snapshots
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│ │ ├── 00/
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│ │ │ ├── 00123456...sha256.json # Tree object (directory listing)
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│ │ │ └── 00789abc...sha256.json
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│ │ ├── 01/ ... fe/
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│ │ └── ff/
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│ │
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│ ├── blobs/ # Content-addressable data storage
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│ │ ├── 00/
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│ │ │ ├── 00abcdef...sha256.bin # Deduplicated data blobs
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│ │ │ └── 00fedcba...sha256.bin
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│ │ ├── 01/ ... fe/
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│ │ └── ff/
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│ │
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│ └── refs/ # Branch pointers (not sharded)
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│ ├── heads/
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│ │ ├── main.json # Points to latest commit on main
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│ │ ├── feature-branch-1.json
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│ │ └── user-workspace-alice.json
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│ │
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│ └── tags/ # Version tags
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│ ├── v1.0.0.json
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│ └── stable.json
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│
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└── _system/ # System metadata (not sharded)
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├── statistics.json # Global statistics
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├── counts.json # Entity/verb counts by type
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│
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├── hnsw/ # HNSW index metadata
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│ ├── system.json # Entry point, max level
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│ └── nodes/
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│ ├── 00/
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│ │ └── 001234...uuid.json # Per-node HNSW data
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│ ├── 01/ ... fe/
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│ └── ff/
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│
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├── metadata_indexes/ # Field indexes for filtering
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│ ├── __metadata_field_index__status.json
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│ ├── __metadata_field_index__category.json
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│ ├── __metadata_sorted_index__createdAt.json
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│ └── [dynamic based on metadata fields]
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│
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└── vfs/ # Virtual File System (v5.0+)
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├── root/ # VFS root directory
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│ ├── 00000000-0000-0000-0000-000000000000.json # Root dir entity
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│ └── files/
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│ ├── 12345678-...uuid.json # File entities
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│ └── 87654321-...uuid.json # Folder entities
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│
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└── metadata/ # VFS-specific metadata
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└── registry.json # VFS entity registry
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```
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---
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## 2. What Gets Stored
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### 2.1 Entities (Nouns) - Split into 2 Files
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Each entity is stored as **2 separate files** for optimal performance.
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#### Vector File
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**Location**: `branches/{branch}/entities/nouns/{type}/vectors/{shard}/{uuid}.json`
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```json
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{
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"id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
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"vector": [0.1, 0.2, 0.3, ...], // 384-dimensional embedding
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"connections": { // HNSW graph connections
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"0": ["uuid1", "uuid2"], // Layer 0 neighbors
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"1": ["uuid3", "uuid4"] // Layer 1 neighbors
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},
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"level": 2 // HNSW max level for this node
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}
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```
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**Purpose**: HNSW graph navigation for semantic search
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**Size**: ~4KB per entity (384 dims × 4 bytes × 2.6 overhead)
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**Scale**: Millions of entities
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#### Metadata File
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**Location**: `branches/{branch}/entities/nouns/{type}/metadata/{shard}/{uuid}.json`
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```json
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{
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"type": "Character",
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"name": "Alice",
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"age": 30,
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"occupation": "Software Engineer",
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"location": "San Francisco",
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"createdAt": 1699564234567,
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"customField": "custom value",
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"_vfs": { // VFS metadata (if applicable)
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"path": "/documents/alice.txt",
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"parentId": "parent-uuid",
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"isDirectory": false,
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"size": 1024
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}
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}
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```
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**Purpose**: Business data and filtering
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**Size**: ~1-10KB per entity (varies by metadata complexity)
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**Scale**: Millions of entities
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---
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### 2.2 Relationships (Verbs) - Split into 2 Files
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Each relationship is also stored as **2 separate files**.
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#### Vector File
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**Location**: `branches/{branch}/entities/verbs/{type}/vectors/{shard}/{uuid}.json`
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```json
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{
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"id": "7b2f5e3c-8d4a-4f1e-9c2b-5a6d7e8f9a0b",
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"vector": [0.5, 0.3, 0.7, ...], // Relationship embedding
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"connections": {
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"0": ["verb-uuid1", "verb-uuid2"] // Verb-to-verb HNSW connections
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},
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"level": 1
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}
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```
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**Purpose**: Relationship similarity for semantic graph queries
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**Size**: ~2KB per relationship
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**Scale**: Millions of relationships
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#### Metadata File
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**Location**: `branches/{branch}/entities/verbs/{type}/metadata/{shard}/{uuid}.json`
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```json
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{
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"sourceId": "user-uuid", // Source entity
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"targetId": "product-uuid", // Target entity
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"type": "Purchased", // Verb type
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"weight": 1.0,
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"timestamp": 1699564234567,
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"metadata": {
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"amount": 99.99,
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"quantity": 2,
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"paymentMethod": "credit_card"
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}
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}
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```
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**Purpose**: Graph structure (edges) and relationship data
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**Size**: ~500 bytes per relationship
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**Scale**: Millions of relationships
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---
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### 2.3 COW (Copy-on-Write) Data
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#### Commit Objects
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**Location**: `_cow/commits/{shard}/{sha256}.json`
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```json
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{
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"tree": "tree-sha256-hash", // Root tree snapshot
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"parent": "parent-commit-sha256", // Previous commit (null for first)
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"author": "user@example.com",
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"timestamp": 1699564234567,
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"message": "Add new characters",
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"branch": "main"
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}
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```
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**Purpose**: Git-like version history
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**Size**: ~300 bytes per commit
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**Scale**: Thousands of commits
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#### Tree Objects
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**Location**: `_cow/trees/{shard}/{sha256}.json`
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```json
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{
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"entries": [
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{
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"type": "tree",
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"name": "entities/nouns/Character",
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"hash": "subtree-sha256-hash"
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},
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{
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"type": "blob",
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"name": "entities/nouns/Character/vectors/00/001234...uuid.json",
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"hash": "blob-sha256-hash"
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}
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]
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}
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```
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**Purpose**: Directory snapshots (like git trees)
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**Size**: ~1-50KB per tree (varies by directory size)
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**Scale**: Thousands of trees
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#### Blob Objects
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**Location**: `_cow/blobs/{shard}/{sha256}.bin`
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```
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[Binary data - deduplicated content]
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```
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**Purpose**: Content-addressable storage (deduplication)
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**Size**: Varies (1KB - 1MB typical)
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**Scale**: Millions of blobs
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**Compression**: Optional zstd compression for >4KB blobs
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#### Refs (Branch Pointers)
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**Location**: `_cow/refs/heads/{branch}.json`
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```json
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{
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"commit": "latest-commit-sha256-hash",
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"updated": 1699564234567
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}
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```
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**Purpose**: Branch head tracking (like git refs)
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**Size**: ~100 bytes per ref
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**Scale**: Dozens to hundreds of branches
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---
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### 2.4 System Metadata
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Unlike entities and relationships, system metadata consists of **index files** that enable fast lookups without scanning millions of entities.
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**Purpose**: Fast filtering and range queries
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**Scale**: 50-200 files total (NOT per-entity!)
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**Location**: `_system/` (not sharded, not branched)
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#### Statistics
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**Location**: `_system/statistics.json`
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```json
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{
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"nounCount": {
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"Character": 50000,
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"Place": 30000,
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"Concept": 20000
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},
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"verbCount": {
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"Knows": 100000,
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"LocatedIn": 75000
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},
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"metadataCount": {
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"Character": 50000,
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"Place": 30000
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},
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"hnswIndexSize": 204800,
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"totalNodes": 100000,
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"totalEdges": 175000,
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"lastUpdated": "2025-11-18T..."
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}
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```
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**Purpose**: Global statistics for monitoring and optimization
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#### Counts (Entity Type Counts)
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**Location**: `_system/counts.json`
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```json
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{
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"nouns": {
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"Character": 50000,
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"Place": 30000,
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"Concept": 20000,
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"Organization": 15000
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},
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"verbs": {
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"Knows": 100000,
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"LocatedIn": 75000,
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"WorksFor": 50000
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},
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"total": {
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"nouns": 115000,
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"verbs": 225000
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},
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"lastUpdated": 1699564234567
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}
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```
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**Purpose**: Fast entity/verb counts by type without scanning storage
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#### HNSW System Metadata
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**Location**: `_system/hnsw/system.json`
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```json
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{
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"entryPointId": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
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"maxLevel": 4,
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"totalNodes": 100000,
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"lastUpdated": 1699564234567
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}
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```
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**Purpose**: HNSW index entry point and global parameters
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#### HNSW Node Data
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**Location**: `_system/hnsw/nodes/{shard}/{uuid}.json`
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```json
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{
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"id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
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"level": 2,
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"connections": {
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"0": ["uuid1", "uuid2", "uuid3"], // Layer 0 neighbors
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"1": ["uuid4", "uuid5"], // Layer 1 neighbors
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"2": ["uuid6"] // Layer 2 neighbors
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}
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}
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```
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**Purpose**: Per-node HNSW graph connections (persisted for fast rebuild)
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#### Field Indexes (Hash Indexes)
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**Location**: `_system/metadata_indexes/__metadata_field_index__{field}.json`
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```json
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{
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"values": {
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"active": 80000, // Count of entities with status=active
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"pending": 15000,
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"deleted": 5000
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},
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"lastUpdated": 1699564234567
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}
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```
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**Purpose**: Fast exact-match filtering without scanning all entities
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#### Sorted Indexes (Range Queries)
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**Location**: `_system/metadata_indexes/__metadata_sorted_index__{field}.json`
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```json
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{
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"values": [
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[1704067200000, ["uuid1", "uuid2", "uuid3"]], // Jan 1, 2024
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[1704153600000, ["uuid4", "uuid5"]], // Jan 2, 2024
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[1704240000000, ["uuid6"]] // Jan 3, 2024
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],
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"fieldType": "number"
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}
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```
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**Purpose**: Fast range queries (e.g., "created after Jan 1, 2024")
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---
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## 2.5 Path Construction Algorithm
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Understanding how Brainy constructs storage paths is critical for debugging and optimization.
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### Path Construction Steps
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**For an entity (noun)**:
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```typescript
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// Given:
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const entityId = "3fa85f64-5717-4562-b3fc-2c963f66afa6"
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const entityType = "Character"
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const branch = "main"
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// Step 1: Extract shard from UUID (first 2 hex characters)
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const shard = entityId.substring(0, 2) // "3f"
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// Step 2: Construct vector path
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const vectorPath = `branches/${branch}/entities/nouns/${entityType}/vectors/${shard}/${entityId}.json`
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// Result: "branches/main/entities/nouns/Character/vectors/3f/3fa85f64-5717-4562-b3fc-2c963f66afa6.json"
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// Step 3: Construct metadata path
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const metadataPath = `branches/${branch}/entities/nouns/${entityType}/metadata/${shard}/${entityId}.json`
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// Result: "branches/main/entities/nouns/Character/metadata/3f/3fa85f64-5717-4562-b3fc-2c963f66afa6.json"
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```
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**For a relationship (verb)**:
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```typescript
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// Given:
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const verbId = "7b2f5e3c-8d4a-4f1e-9c2b-5a6d7e8f9a0b"
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const verbType = "Knows"
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const branch = "main"
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// Step 1: Extract shard
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const shard = verbId.substring(0, 2) // "7b"
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// Step 2: Construct paths
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const vectorPath = `branches/${branch}/entities/verbs/${verbType}/vectors/${shard}/${verbId}.json`
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const metadataPath = `branches/${branch}/entities/verbs/${verbType}/metadata/${shard}/${verbId}.json`
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```
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**For COW objects**:
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```typescript
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// Commits, trees, blobs use content hash as filename
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const commitHash = "00a1b2c3d4e5f6789abcdef0123456789abcdef0123456789abcdef012345678"
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const shard = commitHash.substring(0, 2) // "00"
|
||
const commitPath = `_cow/commits/${shard}/${commitHash}.json`
|
||
|
||
// Refs don't use sharding
|
||
const branchRefPath = `_cow/refs/heads/${branch}.json`
|
||
const tagRefPath = `_cow/refs/tags/${tagName}.json`
|
||
```
|
||
|
||
**For system files**:
|
||
```typescript
|
||
// System files never use sharding or branching
|
||
const statsPath = `_system/statistics.json`
|
||
const countsPath = `_system/counts.json`
|
||
const hnswSystemPath = `_system/hnsw/system.json`
|
||
const fieldIndexPath = `_system/metadata_indexes/__metadata_field_index__${fieldName}.json`
|
||
|
||
// HNSW node data IS sharded (entity UUID-based)
|
||
const hnswNodePath = `_system/hnsw/nodes/${shard}/${entityId}.json`
|
||
```
|
||
|
||
### Path Patterns Summary
|
||
|
||
| Data Type | Path Pattern | Sharded? | Branched? |
|
||
|-----------|--------------|----------|-----------|
|
||
| **Noun vector** | `branches/{branch}/entities/nouns/{type}/vectors/{shard}/{uuid}.json` | ✅ Yes (UUID) | ✅ Yes |
|
||
| **Noun metadata** | `branches/{branch}/entities/nouns/{type}/metadata/{shard}/{uuid}.json` | ✅ Yes (UUID) | ✅ Yes |
|
||
| **Verb vector** | `branches/{branch}/entities/verbs/{type}/vectors/{shard}/{uuid}.json` | ✅ Yes (UUID) | ✅ Yes |
|
||
| **Verb metadata** | `branches/{branch}/entities/verbs/{type}/metadata/{shard}/{uuid}.json` | ✅ Yes (UUID) | ✅ Yes |
|
||
| **COW commit** | `_cow/commits/{shard}/{sha256}.json` | ✅ Yes (SHA) | ❌ No |
|
||
| **COW tree** | `_cow/trees/{shard}/{sha256}.json` | ✅ Yes (SHA) | ❌ No |
|
||
| **COW blob** | `_cow/blobs/{shard}/{sha256}.bin` | ✅ Yes (SHA) | ❌ No |
|
||
| **COW ref** | `_cow/refs/heads/{branch}.json` | ❌ No | ❌ No |
|
||
| **Statistics** | `_system/statistics.json` | ❌ No | ❌ No |
|
||
| **Counts** | `_system/counts.json` | ❌ No | ❌ No |
|
||
| **HNSW system** | `_system/hnsw/system.json` | ❌ No | ❌ No |
|
||
| **HNSW node** | `_system/hnsw/nodes/{shard}/{uuid}.json` | ✅ Yes (UUID) | ❌ No |
|
||
| **Field index** | `_system/metadata_indexes/__metadata_field_index__{field}.json` | ❌ No | ❌ No |
|
||
|
||
### Key Principles
|
||
|
||
1. **Shard Extraction**: Always use first 2 hex characters of UUID/SHA-256
|
||
2. **Type-First**: Type comes before shard in entity paths
|
||
3. **Branch Isolation**: Only entity data uses branches/
|
||
4. **System Isolation**: System files never use sharding or branching (except HNSW nodes)
|
||
5. **Content-Addressable**: COW uses SHA-256 hash as filename
|
||
|
||
---
|
||
|
||
## 3. The 4 Indexes
|
||
|
||
Brainy uses four complementary index systems for different query patterns.
|
||
|
||
### 3.1 HNSW Vector Index (In-Memory with Lazy Loading)
|
||
|
||
**Purpose**: Semantic similarity search
|
||
**Location**: RAM (rebuilt from storage on startup)
|
||
**Data Structure**: Hierarchical graph of vector connections
|
||
|
||
**How It Works**:
|
||
1. Loads `branches/{branch}/entities/nouns/{type}/vectors/**/*.json` files
|
||
2. Builds HNSW graph structure in memory
|
||
3. Enables O(log n) approximate nearest neighbor search
|
||
4. Vectors loaded on-demand in lazy mode (zero configuration)
|
||
|
||
**Performance**:
|
||
- Build time: 1-5 seconds per 100K entities
|
||
- Query time: 1-10ms for k=10 results (standard mode)
|
||
- Query time: 2-15ms for k=10 results (lazy mode, with cache)
|
||
- Memory (standard): ~200MB per 100K entities
|
||
- Memory (lazy): ~15-33MB per 100K entities (5-10x less!)
|
||
|
||
**Automatic Lazy Mode** (v3.36.0+): Enables automatically when vectors don't fit in UnifiedCache
|
||
|
||
---
|
||
|
||
### 3.2 Type-Aware Index (Path-Based)
|
||
|
||
**Purpose**: Fast type filtering and organization
|
||
**Location**: Derived from filesystem paths (no separate storage)
|
||
**Data Structure**: Directory tree organized by type
|
||
|
||
**How It Works**:
|
||
```typescript
|
||
// Find all Characters
|
||
const characters = await brain.getNouns({ type: 'Character' })
|
||
// Scans only: branches/main/entities/nouns/Character/**/*.json
|
||
// Skips: all other type directories
|
||
```
|
||
|
||
**Performance**:
|
||
- Type filtering: O(type_count) instead of O(total_entities)
|
||
- 42x faster for queries filtered by type (42 noun types total)
|
||
- Zero storage overhead (uses filesystem structure)
|
||
|
||
---
|
||
|
||
### 3.3 Graph Adjacency Index (In-Memory, LSM-Tree)
|
||
|
||
**Purpose**: Navigate relationships (graph queries)
|
||
**Location**: RAM (rebuilt from storage on startup)
|
||
**Data Structure**: Bidirectional LSM-tree mappings
|
||
|
||
```typescript
|
||
{
|
||
sourceToTargets: Map<string, Set<string>>, // "user-uuid" → ["product1", "product2"]
|
||
targetToSources: Map<string, Set<string>> // "product1" → ["user1", "user2"]
|
||
}
|
||
```
|
||
|
||
**Example Query**:
|
||
```typescript
|
||
// Find all products purchased by a user
|
||
const verbs = await brain.getVerbsBySource("user-uuid")
|
||
|
||
// Find all users who purchased a product
|
||
const verbs = await brain.getVerbsByTarget("product-uuid")
|
||
```
|
||
|
||
**Performance**:
|
||
- Build time: 0.5-2 seconds per 100K relationships
|
||
- Query time: <1ms (O(1) lookup)
|
||
- Memory: ~100MB per 100K relationships
|
||
|
||
---
|
||
|
||
### 3.4 Metadata Field Indexes (On-Disk)
|
||
|
||
**Purpose**: Filter by business fields without loading all entities
|
||
**Location**: `_system/metadata_indexes/`
|
||
**Data Structure**: Field → Value → IDs mapping
|
||
|
||
**Example Query**:
|
||
```typescript
|
||
// Find all active users
|
||
const users = await brain.getNouns({
|
||
filter: { metadata: { status: 'active' } }
|
||
})
|
||
// Uses: _system/metadata_indexes/__metadata_field_index__status.json
|
||
// Returns: ~1000 IDs in 5ms (vs scanning 1M entities)
|
||
```
|
||
|
||
**Performance**:
|
||
- Exact match: O(1) hash lookup
|
||
- Range query: O(log n) binary search (sorted indexes)
|
||
- Filter time: 5-50ms for 1M entities
|
||
|
||
---
|
||
|
||
## 4. Sharding Strategy
|
||
|
||
### 4.1 Why Shard?
|
||
|
||
**Cloud Storage Limitations**:
|
||
- GCS/S3: Listing 100K files in one directory = 10-30 seconds
|
||
- GCS/S3: Max recommended files per directory = 1,000-10,000
|
||
- Network: Parallel operations faster than sequential
|
||
|
||
**Solution**: Split into 256 shards = ~3,900 files per shard at 1M scale
|
||
|
||
---
|
||
|
||
### 4.2 How Sharding Works
|
||
|
||
**Algorithm**: Extract first 2 hex characters from UUID
|
||
|
||
```
|
||
UUID: 3fa85f64-5717-4562-b3fc-2c963f66afa6
|
||
^^
|
||
Shard: 3f
|
||
```
|
||
|
||
**Properties**:
|
||
- **Deterministic**: Same UUID always maps to same shard
|
||
- **Uniform**: UUIDs distribute evenly across shards
|
||
- **Predictable**: Easy to compute, no randomness
|
||
- **Efficient**: Simple string operation (O(1))
|
||
|
||
**Shard Distribution (1M entities)**:
|
||
```
|
||
Shard 00: ~3,900 entities
|
||
Shard 01: ~3,900 entities
|
||
...
|
||
Shard fe: ~3,900 entities
|
||
Shard ff: ~3,900 entities
|
||
Total: 256 shards × 3,900 = ~1,000,000 entities
|
||
```
|
||
|
||
---
|
||
|
||
### 4.3 What Gets Sharded vs. Not Sharded
|
||
|
||
| Data Type | Sharded? | Path Pattern |
|
||
|-----------|----------|--------------|
|
||
| **Noun vectors** | ✅ Yes | `branches/{branch}/entities/nouns/{type}/vectors/{shard}/{uuid}.json` |
|
||
| **Noun metadata** | ✅ Yes | `branches/{branch}/entities/nouns/{type}/metadata/{shard}/{uuid}.json` |
|
||
| **Verb vectors** | ✅ Yes | `branches/{branch}/entities/verbs/{type}/vectors/{shard}/{uuid}.json` |
|
||
| **Verb metadata** | ✅ Yes | `branches/{branch}/entities/verbs/{type}/metadata/{shard}/{uuid}.json` |
|
||
| **COW commits** | ✅ Yes | `_cow/commits/{shard}/{sha256}.json` |
|
||
| **COW trees** | ✅ Yes | `_cow/trees/{shard}/{sha256}.json` |
|
||
| **COW blobs** | ✅ Yes | `_cow/blobs/{shard}/{sha256}.bin` |
|
||
| **COW refs** | ❌ No | `_cow/refs/heads/{branch}.json` |
|
||
| **System metadata** | ❌ No | `_system/statistics.json` |
|
||
| **Indexes** | ❌ No | `_system/metadata_indexes/*.json` |
|
||
|
||
**Key Principle**: Shard by **UUID** (entity IDs, commit hashes), not by type or field.
|
||
|
||
---
|
||
|
||
### 4.4 Performance Impact
|
||
|
||
**Without Sharding (1M entities)**:
|
||
```
|
||
List directory: 30 seconds
|
||
Find entity: 30 seconds (must list first)
|
||
Delete entity: 30 seconds (must list first)
|
||
```
|
||
|
||
**With Sharding (1M entities across 256 shards)**:
|
||
```
|
||
List directory: 120ms (only ~3,900 files)
|
||
Find entity: 150ms (list shard + download)
|
||
Delete entity: 150ms (list shard + delete)
|
||
```
|
||
|
||
**Speedup**: 200x faster for large datasets
|
||
|
||
---
|
||
|
||
## 5. COW (Copy-on-Write) Architecture
|
||
|
||
### 5.1 What is COW?
|
||
|
||
COW is Brainy's **git-like versioning system** that enables:
|
||
- ✅ **Time-travel queries** (query data as it existed at any point in time)
|
||
- ✅ **Instant branches** (create lightweight branches in milliseconds)
|
||
- ✅ **Efficient forks** (zero-copy duplication via lazy COW)
|
||
- ✅ **Deduplication** (identical data stored only once)
|
||
- ✅ **Version history** (full audit trail of all changes)
|
||
|
||
**Status**: ALWAYS ENABLED (v5.11.0+) - cannot be disabled
|
||
|
||
---
|
||
|
||
### 5.2 COW Directory Structure
|
||
|
||
```
|
||
_cow/
|
||
├── commits/ # Commit objects (version history)
|
||
├── trees/ # Directory snapshots
|
||
├── blobs/ # Content-addressable data storage
|
||
└── refs/ # Branch pointers
|
||
├── heads/ # Branch heads (main, feature branches)
|
||
└── tags/ # Version tags (v1.0.0, stable, etc.)
|
||
```
|
||
|
||
---
|
||
|
||
### 5.3 How COW Works
|
||
|
||
**When you add data**:
|
||
1. Data written to `branches/main/entities/nouns/Character/...`
|
||
2. Commit object created in `_cow/commits/{sha}/`
|
||
3. Tree objects created for directory structure
|
||
4. Blobs created for content (deduplicated by SHA-256)
|
||
5. `_cow/refs/heads/main.json` updated to point to new commit
|
||
|
||
**When you query `brain.asOf(timestamp)`**:
|
||
1. Find commit at specified timestamp
|
||
2. Load tree from commit
|
||
3. Lazy-load entities from historical tree structure
|
||
4. Return read-only view (no writes allowed)
|
||
|
||
**When you create a branch**:
|
||
1. Copy `_cow/refs/heads/main.json` → `_cow/refs/heads/feature.json`
|
||
2. Create `branches/feature/` directory (initially empty)
|
||
3. Lazy COW: Only modified files copied, rest shared with main
|
||
4. Result: Instant branch creation (milliseconds)
|
||
|
||
---
|
||
|
||
### 5.4 Deduplication
|
||
|
||
**Content-addressable storage** means identical data is stored only once:
|
||
|
||
```
|
||
// Two entities with identical vector data
|
||
Entity A: vector = [0.1, 0.2, 0.3, ...] → SHA-256 = abc123...
|
||
Entity B: vector = [0.1, 0.2, 0.3, ...] → SHA-256 = abc123... (same!)
|
||
|
||
// Only ONE blob stored:
|
||
_cow/blobs/ab/abc123...sha256.bin (used by both entities)
|
||
```
|
||
|
||
**Deduplication savings**:
|
||
- Typical: 10-30% storage reduction
|
||
- Forks/branches: 70-90% reduction (shared data not duplicated)
|
||
- Identical imports: 95%+ reduction
|
||
|
||
---
|
||
|
||
## 6. Type-First Storage
|
||
|
||
### 6.1 What is Type-First?
|
||
|
||
**Type-first storage** organizes entities by their **semantic type** before sharding by UUID.
|
||
|
||
**Old structure** (pre-v5.4.0):
|
||
```
|
||
entities/nouns/vectors/00/001234...uuid.json # What type? Unknown until you read it!
|
||
```
|
||
|
||
**Type-first structure** (v5.4.0+):
|
||
```
|
||
branches/main/entities/nouns/Character/vectors/00/001234...uuid.json # Type visible in path!
|
||
```
|
||
|
||
---
|
||
|
||
### 6.2 Benefits of Type-First
|
||
|
||
**1. Fast Type Filtering**
|
||
```typescript
|
||
// Find all Characters
|
||
const characters = await brain.getNouns({ type: 'Character' })
|
||
// Scans only: branches/main/entities/nouns/Character/**
|
||
// Skips: Place, Concept, Organization, etc. (41 other types)
|
||
```
|
||
|
||
**2. Efficient Storage Scans**
|
||
- List all Characters: O(character_count) instead of O(total_entities)
|
||
- 42x faster for type-filtered queries (42 noun types total)
|
||
|
||
**3. Clear Data Organization**
|
||
- Each type has dedicated directory
|
||
- Easy to backup/restore specific types
|
||
- Clear separation of concerns
|
||
|
||
---
|
||
|
||
### 6.3 Type-First Path Structure
|
||
|
||
```
|
||
branches/{branch}/entities/nouns/{type}/vectors/{shard}/{uuid}.json
|
||
branches/{branch}/entities/nouns/{type}/metadata/{shard}/{uuid}.json
|
||
branches/{branch}/entities/verbs/{type}/vectors/{shard}/{uuid}.json
|
||
branches/{branch}/entities/verbs/{type}/metadata/{shard}/{uuid}.json
|
||
```
|
||
|
||
**Breakdown**:
|
||
- `branches/{branch}`: Branch isolation (main, feature branches, user workspaces)
|
||
- `entities/nouns` or `entities/verbs`: Entity vs. relationship
|
||
- `{type}`: Semantic type (Character, Place, Knows, LocatedIn, etc.)
|
||
- `vectors` or `metadata`: Vector vs. metadata split
|
||
- `{shard}`: UUID-based shard (00-ff, 256 total)
|
||
- `{uuid}.json`: Individual entity file
|
||
|
||
---
|
||
|
||
### 6.4 Supported Types
|
||
|
||
**42 Noun Types**:
|
||
- Person, Organization, Location, Thing, Concept, Event, Agent, Organism, Substance, Quality, TimeInterval, Function, Proposition, Document, Media, File, Message, Collection, Dataset, Product, Service, Task, Project, Process, State, Role, Language, Currency, Measurement, Hypothesis, Experiment, Contract, Regulation, Interface, Resource, Custom, SocialGroup, Institution, Norm, InformationContent, InformationBearer, Relationship
|
||
|
||
**127 Verb Types**:
|
||
- Knows, LocatedIn, WorksFor, HasProperty, Contains, PartOf, CausedBy, PrecededBy, FollowedBy, etc.
|
||
- See [noun-verb-taxonomy.md](./noun-verb-taxonomy.md) for complete list
|
||
|
||
---
|
||
|
||
## 7. VFS (Virtual File System)
|
||
|
||
### 7.1 What is VFS?
|
||
|
||
**VFS** lets you store traditional file/folder hierarchies in Brainy's graph database.
|
||
|
||
**Example**:
|
||
```
|
||
/documents/
|
||
├── reports/
|
||
│ ├── Q1.pdf (stored as entity)
|
||
│ └── Q2.pdf (stored as entity)
|
||
└── notes/
|
||
└── meeting.txt (stored as entity)
|
||
```
|
||
|
||
Each file/folder is a **regular Brainy entity** with special VFS metadata.
|
||
|
||
---
|
||
|
||
### 7.2 VFS Storage Structure
|
||
|
||
**File Entity**:
|
||
```json
|
||
// branches/main/entities/nouns/File/metadata/12/123456...uuid.json
|
||
{
|
||
"type": "File",
|
||
"name": "Q1.pdf",
|
||
"_vfs": {
|
||
"path": "/documents/reports/Q1.pdf",
|
||
"parentId": "parent-directory-uuid",
|
||
"isDirectory": false,
|
||
"size": 102400,
|
||
"mimeType": "application/pdf",
|
||
"createdAt": 1699564234567,
|
||
"modifiedAt": 1699564234567
|
||
},
|
||
// Regular metadata fields can coexist
|
||
"author": "Alice",
|
||
"department": "Finance"
|
||
}
|
||
```
|
||
|
||
**Directory Entity**:
|
||
```json
|
||
// branches/main/entities/nouns/Collection/metadata/ab/abcdef...uuid.json
|
||
{
|
||
"type": "Collection",
|
||
"name": "reports",
|
||
"_vfs": {
|
||
"path": "/documents/reports",
|
||
"parentId": "documents-directory-uuid",
|
||
"isDirectory": true,
|
||
"childrenIds": ["Q1-uuid", "Q2-uuid"]
|
||
}
|
||
}
|
||
```
|
||
|
||
**Root Directory** (special fixed UUID):
|
||
```json
|
||
// branches/main/entities/nouns/Collection/metadata/00/00000000-0000-0000-0000-000000000000.json
|
||
{
|
||
"type": "Collection",
|
||
"name": "root",
|
||
"_vfs": {
|
||
"path": "/",
|
||
"parentId": null,
|
||
"isDirectory": true,
|
||
"childrenIds": ["documents-uuid", "projects-uuid"]
|
||
}
|
||
}
|
||
```
|
||
|
||
---
|
||
|
||
### 7.3 VFS + Triple Intelligence
|
||
|
||
VFS files can use **Triple Intelligence** for semantic extraction:
|
||
|
||
```typescript
|
||
// Upload PDF
|
||
const fileId = await brain.vfs.uploadFile('/documents/report.pdf', pdfBuffer)
|
||
|
||
// Triple Intelligence extracts:
|
||
// - Entities: People, organizations, locations mentioned
|
||
// - Relationships: Who works where, who knows who
|
||
// - Concepts: Key themes and topics
|
||
|
||
// Query semantically
|
||
const related = await brain.find('financial projections for Q2')
|
||
// Returns: report.pdf + extracted entities + relationships
|
||
```
|
||
|
||
**Storage**: Extracted entities stored as regular entities in type-first structure, linked to file via relationships.
|
||
|
||
---
|
||
|
||
## 8. Storage Backend Mapping
|
||
|
||
### 8.1 All Backends Use Same Structure
|
||
|
||
**Filesystem** (local):
|
||
```
|
||
/path/to/brainy-data/
|
||
├── branches/main/entities/nouns/Character/vectors/00/001234...uuid.json
|
||
├── _cow/commits/00/00a1b2c3...sha256.json
|
||
└── _system/statistics.json
|
||
```
|
||
|
||
**Google Cloud Storage** (GCS):
|
||
```
|
||
gs://my-bucket/
|
||
├── branches/main/entities/nouns/Character/vectors/00/001234...uuid.json
|
||
├── _cow/commits/00/00a1b2c3...sha256.json
|
||
└── _system/statistics.json
|
||
```
|
||
|
||
**AWS S3** / **MinIO** / **DigitalOcean Spaces**:
|
||
```
|
||
s3://my-bucket/
|
||
├── branches/main/entities/nouns/Character/vectors/00/001234...uuid.json
|
||
├── _cow/commits/00/00a1b2c3...sha256.json
|
||
└── _system/statistics.json
|
||
```
|
||
|
||
**Cloudflare R2**:
|
||
```
|
||
r2://my-bucket/
|
||
├── branches/main/entities/nouns/Character/vectors/00/001234...uuid.json
|
||
├── _cow/commits/00/00a1b2c3...sha256.json
|
||
└── _system/statistics.json
|
||
```
|
||
|
||
**Azure Blob Storage**:
|
||
```
|
||
azure://my-container/
|
||
├── branches/main/entities/nouns/Character/vectors/00/001234...uuid.json
|
||
├── _cow/commits/00/00a1b2c3...sha256.json
|
||
└── _system/statistics.json
|
||
```
|
||
|
||
**OPFS** (browser):
|
||
```
|
||
opfs://root/brainy/
|
||
├── branches/main/entities/nouns/Character/vectors/00/001234...uuid.json
|
||
├── _cow/commits/00/00a1b2c3...sha256.json
|
||
└── _system/statistics.json
|
||
```
|
||
|
||
**Memory Storage** (in-memory):
|
||
- Uses same path structure
|
||
- Stored in `Map<string, any>`
|
||
- Key = full path (e.g., "branches/main/entities/nouns/Character/vectors/00/001234...uuid.json")
|
||
|
||
---
|
||
|
||
### 8.2 Backend-Specific Optimizations
|
||
|
||
**Cloud Storage (GCS, S3, R2, Azure)**:
|
||
- Lifecycle policies for automatic archival (96% cost savings)
|
||
- Intelligent-Tiering (S3) or Autoclass (GCS) for access-pattern optimization
|
||
- Batch operations (1000 objects per request for S3)
|
||
- Parallel uploads/downloads
|
||
|
||
**Filesystem**:
|
||
- Optional gzip compression (60-80% space savings)
|
||
- Direct file I/O (fastest for local)
|
||
- Atomic writes with rename
|
||
|
||
**OPFS**:
|
||
- Quota monitoring (browser storage limits)
|
||
- Persistent storage (survives page refresh)
|
||
- Worker-based I/O (non-blocking)
|
||
|
||
**Memory**:
|
||
- No I/O overhead (instant access)
|
||
- No persistence (data lost on restart)
|
||
- Ideal for testing and development
|
||
|
||
---
|
||
|
||
## 9. Performance Characteristics
|
||
|
||
### 9.1 File Count (1M Entities Example)
|
||
|
||
| Directory | File Count | Size per File | Total Size |
|
||
|-----------|-----------|---------------|------------|
|
||
| `branches/main/entities/nouns/*/vectors/**` | 1,000,000 | ~4KB | ~4GB |
|
||
| `branches/main/entities/nouns/*/metadata/**` | 1,000,000 | ~2KB | ~2GB |
|
||
| `branches/main/entities/verbs/*/vectors/**` | 1,000,000 | ~2KB | ~2GB |
|
||
| `branches/main/entities/verbs/*/metadata/**` | 1,000,000 | ~500B | ~500MB |
|
||
| `_cow/commits/**` | ~10,000 | ~300B | ~3MB |
|
||
| `_cow/trees/**` | ~50,000 | ~5KB | ~250MB |
|
||
| `_cow/blobs/**` | ~2,000,000 | ~2KB | ~4GB |
|
||
| `_cow/refs/**` | ~50 | ~100B | ~5KB |
|
||
| `_system/**` | ~100 | ~1-500KB | ~10MB |
|
||
| **Total** | **~5,060,150** | | **~12.8GB** |
|
||
|
||
**With deduplication**: ~8.5-10GB (30-40% savings from blob deduplication)
|
||
|
||
---
|
||
|
||
### 9.2 Read Performance
|
||
|
||
| Operation | No Sharding | With Sharding | Improvement |
|
||
|-----------|-------------|---------------|-------------|
|
||
| Get entity by ID | 15-30s | 100-150ms | **200x faster** |
|
||
| List all entities | 30-60s | 30-60s | Same |
|
||
| Filter by metadata | 10-30s | 5-50ms | **100-600x faster** (via indexes) |
|
||
| Semantic search | N/A | 1-10ms | N/A (requires HNSW) |
|
||
| Type filtering | 30-60s | 120-200ms | **150-500x faster** (type-first) |
|
||
| Graph query (getVerbsBySource) | O(total_verbs) | <1ms | **O(1) via index** |
|
||
|
||
---
|
||
|
||
### 9.3 Write Performance
|
||
|
||
| Operation | No Sharding | With Sharding | Improvement |
|
||
|-----------|-------------|---------------|-------------|
|
||
| Add entity | 15-30s | 100-150ms | **200x faster** |
|
||
| Update entity | 15-30s | 100-150ms | **200x faster** |
|
||
| Delete entity | 15-30s | 100-150ms | **200x faster** |
|
||
| Batch insert (1000) | 4-8 hours | 2-3 minutes | **120x faster** |
|
||
| Create branch | N/A | 100-200ms | Instant (COW) |
|
||
| Commit changes | N/A | 500-1000ms | Automatic (COW) |
|
||
|
||
---
|
||
|
||
### 9.4 Scale Limits
|
||
|
||
| Storage Backend | Max Entities (No Optimization) | Max Entities (Full Optimization) |
|
||
|----------------|-------------------------------|----------------------------------|
|
||
| GCS | ~10,000 | **10M+** |
|
||
| S3 | ~10,000 | **10M+** |
|
||
| R2 | ~10,000 | **10M+** |
|
||
| Azure | ~10,000 | **10M+** |
|
||
| Filesystem | ~100,000 | **10M+** |
|
||
| OPFS | ~50,000 | **1M+** (browser limits) |
|
||
| Memory | Limited by RAM | Limited by RAM |
|
||
|
||
**Full optimization** = Sharding + Type-first + COW + Lifecycle policies + Lazy mode
|
||
|
||
---
|
||
|
||
### 9.5 Memory Usage
|
||
|
||
| Component | Standard Mode | Lazy Mode | Savings |
|
||
|-----------|---------------|-----------|---------|
|
||
| **HNSW Index (100K entities)** | 149MB | 15-33MB | 5-10x |
|
||
| **Graph Index (100K verbs)** | 100MB | 100MB | N/A |
|
||
| **Metadata Indexes** | 10-50MB | 10-50MB | N/A |
|
||
| **UnifiedCache** | 2GB | 2GB | N/A |
|
||
| **Total (100K entities)** | ~2.3GB | ~2.2GB | Minimal |
|
||
| **Total (1M entities)** | ~3.5GB | ~2.3GB | **34% less** |
|
||
| **Total (10M entities)** | ~15GB | ~3.0GB | **80% less** |
|
||
|
||
**Lazy mode activates automatically** when vectors exceed available cache.
|
||
|
||
---
|
||
|
||
## 10. Best Practices
|
||
|
||
### 10.1 Data Organization
|
||
|
||
✅ **Do**:
|
||
- Use UUIDs for all entities and relationships
|
||
- Let Brainy handle sharding automatically (type-first + UUID sharding)
|
||
- Use metadata indexes for filtering
|
||
- Enable lifecycle policies for cloud storage (96% cost savings)
|
||
- Use batch operations for bulk deletions
|
||
- Enable compression for FileSystem storage (60-80% space savings)
|
||
- Create branches for experimentation (instant, zero-cost)
|
||
|
||
❌ **Don't**:
|
||
- Try to organize files manually
|
||
- Assume file paths are predictable (use IDs, not paths)
|
||
- Store large binary data in metadata (use blob storage or VFS)
|
||
- Disable COW (can't be disabled in v5.11.0+, always enabled)
|
||
- Forget to monitor OPFS quota in browser applications
|
||
|
||
---
|
||
|
||
### 10.2 clear() Operation
|
||
|
||
**What clear() deletes** (v5.11.0+):
|
||
|
||
✅ Deletes:
|
||
- `branches/` → ALL entity data (all types, all shards, all branches, all forks)
|
||
- `_cow/` → ALL version control (commits, trees, blobs, refs)
|
||
- `_system/` → ALL indexes (statistics, HNSW, metadata)
|
||
|
||
✅ Resets:
|
||
- COW managers (refManager, blobStorage, commitLog) → `undefined`
|
||
- Entity counts → 0
|
||
- Statistics cache → `null`
|
||
|
||
✅ Behavior:
|
||
- COW **auto-reinitializes** on next operation (can't be disabled)
|
||
- Branches recreated automatically when new data added
|
||
- clean slate for fresh start
|
||
|
||
**Example**:
|
||
```typescript
|
||
await brain.storage.clear() // ✅ Deletes ALL data correctly (v5.11.0+)
|
||
await brain.add({ data: 'Alice', type: 'person' }) // ✅ COW reinitializes automatically
|
||
```
|
||
|
||
---
|
||
|
||
### 10.3 Querying
|
||
|
||
✅ **Do**:
|
||
- Use type filtering for known types: `brain.getNouns({ type: 'Character' })`
|
||
- Use metadata filters when possible: `brain.getNouns({ filter: { metadata: { status: 'active' } } })`
|
||
- Limit result sets with pagination: `brain.getNouns({ limit: 100, offset: 0 })`
|
||
- Use semantic search for similarity queries: `brain.find('concept similar to...')`
|
||
- Use graph queries for relationships: `brain.getVerbsBySource(userId)`
|
||
|
||
❌ **Don't**:
|
||
- Load all entities into memory: `const all = await brain.getNouns()` (use pagination!)
|
||
- Filter in application code (use metadata indexes instead)
|
||
- Scan all entities for simple queries (use indexes)
|
||
|
||
---
|
||
|
||
## 10.4 Common Storage Scenarios
|
||
|
||
Understanding how Brainy's storage architecture handles common scenarios.
|
||
|
||
### Scenario 1: Adding an Entity
|
||
|
||
**User code**:
|
||
```typescript
|
||
await brain.add({ data: 'Alice', type: 'person' })
|
||
```
|
||
|
||
**What happens in storage**:
|
||
```
|
||
1. Generate UUID: "3fa85f64-5717-4562-b3fc-2c963f66afa6"
|
||
2. Compute vector embedding: [0.1, 0.2, 0.3, ...]
|
||
3. Extract shard: "3f"
|
||
4. Write vector file:
|
||
→ branches/main/entities/nouns/person/vectors/3f/3fa85f64-5717-4562-b3fc-2c963f66afa6.json
|
||
5. Write metadata file:
|
||
→ branches/main/entities/nouns/person/metadata/3f/3fa85f64-5717-4562-b3fc-2c963f66afa6.json
|
||
6. Create COW commit:
|
||
→ _cow/commits/00/00a1b2c3...sha256.json
|
||
7. Create COW tree (directory snapshot):
|
||
→ _cow/trees/ab/abcdef12...sha256.json
|
||
8. Create COW blobs (content-addressable):
|
||
→ _cow/blobs/3f/3fa85f64...sha256.bin (vector data)
|
||
→ _cow/blobs/7b/7b2f5e3c...sha256.bin (metadata)
|
||
9. Update branch ref:
|
||
→ _cow/refs/heads/main.json (points to new commit)
|
||
10. Update statistics:
|
||
→ _system/statistics.json (increment person count)
|
||
11. Update HNSW index (in-memory):
|
||
→ Connect to nearest neighbors
|
||
12. Update graph index (in-memory):
|
||
→ Add to adjacency maps
|
||
```
|
||
|
||
**Files created**: 5-7 files (2 entity files + 1 commit + 1 tree + 2-3 blobs + 1 ref update)
|
||
|
||
---
|
||
|
||
### Scenario 2: Querying by Type
|
||
|
||
**User code**:
|
||
```typescript
|
||
const characters = await brain.getNouns({ type: 'Character', limit: 100 })
|
||
```
|
||
|
||
**What happens in storage**:
|
||
```
|
||
1. Type-first optimization:
|
||
→ Scan only: branches/main/entities/nouns/Character/**
|
||
→ Skip all other types (41 other type directories)
|
||
2. List all shards in parallel:
|
||
→ branches/main/entities/nouns/Character/metadata/00/
|
||
→ branches/main/entities/nouns/Character/metadata/01/
|
||
→ ... (256 parallel operations)
|
||
3. Read first 100 metadata files
|
||
4. Return results (no vector load needed for listing)
|
||
```
|
||
|
||
**Performance**: 120-200ms for 100K entities (vs 30-60s without type-first)
|
||
|
||
---
|
||
|
||
### Scenario 3: Semantic Search
|
||
|
||
**User code**:
|
||
```typescript
|
||
const results = await brain.find('medieval castle', { k: 10 })
|
||
```
|
||
|
||
**What happens in storage**:
|
||
```
|
||
1. Compute query vector: [0.1, 0.2, 0.3, ...]
|
||
2. Use HNSW index (in-memory):
|
||
→ Navigate graph from entry point
|
||
→ Find 10 nearest neighbors (1-10ms)
|
||
3. Load vectors from cache or storage:
|
||
→ Standard mode: All vectors already in memory
|
||
→ Lazy mode: Load missing vectors from storage
|
||
branches/main/entities/nouns/Place/vectors/3f/3fa85f64...uuid.json
|
||
4. Return results with metadata
|
||
```
|
||
|
||
**Performance**: 1-10ms (standard mode), 2-15ms (lazy mode with cache)
|
||
|
||
---
|
||
|
||
### Scenario 4: Creating a Branch
|
||
|
||
**User code**:
|
||
```typescript
|
||
await brain.branch.create('feature-experiment')
|
||
```
|
||
|
||
**What happens in storage**:
|
||
```
|
||
1. Copy ref (instant):
|
||
_cow/refs/heads/main.json → _cow/refs/heads/feature-experiment.json
|
||
2. Create branch directory (empty initially):
|
||
branches/feature-experiment/
|
||
3. NO data copying (lazy COW):
|
||
→ All data shared with main branch
|
||
→ Only modified entities copied on write
|
||
4. Result: Branch created in 100-200ms
|
||
```
|
||
|
||
**Storage overhead**: ~100 bytes (just the ref file)
|
||
**Data duplication**: 0% (shared with main until modified)
|
||
|
||
---
|
||
|
||
### Scenario 5: Time-Travel Query
|
||
|
||
**User code**:
|
||
```typescript
|
||
const yesterday = await brain.asOf(Date.now() - 86400000)
|
||
const historicalData = await yesterday.getNouns({ type: 'Character' })
|
||
```
|
||
|
||
**What happens in storage**:
|
||
```
|
||
1. Find commit at timestamp:
|
||
→ Search _cow/commits/** for timestamp match
|
||
2. Load commit object:
|
||
→ _cow/commits/00/00a1b2c3...sha256.json
|
||
3. Load tree from commit:
|
||
→ _cow/trees/ab/abcdef12...sha256.json
|
||
4. Lazy-load entities from historical tree:
|
||
→ Read blob hashes from tree
|
||
→ Load blobs: _cow/blobs/3f/3fa85f64...sha256.bin
|
||
→ Reconstruct entities from historical state
|
||
5. Return read-only view (writes blocked)
|
||
```
|
||
|
||
**Performance**: 500-1000ms for first query (loads commit tree), 100-200ms for subsequent queries (cached)
|
||
|
||
---
|
||
|
||
### Scenario 6: Clearing Storage
|
||
|
||
**User code**:
|
||
```typescript
|
||
await brain.storage.clear()
|
||
```
|
||
|
||
**What happens in storage** (v5.11.0+):
|
||
```
|
||
1. Delete all entity data:
|
||
→ Remove: branches/ (entire directory)
|
||
→ Result: ALL types, ALL shards, ALL branches deleted
|
||
2. Delete all version control:
|
||
→ Remove: _cow/ (entire directory)
|
||
→ Result: ALL commits, trees, blobs, refs deleted
|
||
3. Delete all indexes:
|
||
→ Remove: _system/ (entire directory)
|
||
→ Result: Statistics, HNSW, metadata indexes deleted
|
||
4. Reset COW managers in memory:
|
||
→ refManager = undefined
|
||
→ blobStorage = undefined
|
||
→ commitLog = undefined
|
||
5. Reset counters:
|
||
→ totalNounCount = 0
|
||
→ totalVerbCount = 0
|
||
6. Next operation auto-reinitializes COW:
|
||
→ COW managers recreate automatically
|
||
→ Fresh branches/main/ created
|
||
→ New _cow/ initialized
|
||
```
|
||
|
||
**Storage after clear()**: Empty (all data deleted)
|
||
**COW status**: Always enabled (auto-reinitializes)
|
||
|
||
---
|
||
|
||
### Scenario 7: Cold Start (Index Rebuild)
|
||
|
||
**User code**:
|
||
```typescript
|
||
const brain = new Brainy({ storage: existingStorage })
|
||
await brain.init()
|
||
```
|
||
|
||
**What happens in storage**:
|
||
```
|
||
1. Check for persisted indexes:
|
||
→ Load: _system/hnsw/system.json (entry point, max level)
|
||
→ Load: _system/hnsw/nodes/** (graph connections)
|
||
→ Load: _system/statistics.json (entity counts)
|
||
2. Decide standard vs lazy mode:
|
||
→ Check: entityCount × vectorSize vs. available cache
|
||
→ Auto-enable lazy mode if needed
|
||
3. Rebuild HNSW index:
|
||
→ Standard mode: Load all vectors into memory
|
||
→ Lazy mode: Load only graph structure (~24 bytes/node)
|
||
4. Rebuild Graph Adjacency index:
|
||
→ Load: branches/main/entities/verbs/*/metadata/** (all verbs)
|
||
→ Build: sourceToTargets and targetToSources maps
|
||
5. Load Metadata indexes:
|
||
→ Read: _system/metadata_indexes/** (on-demand)
|
||
6. Ready for queries (1-5 seconds for 100K entities)
|
||
```
|
||
|
||
**Performance**:
|
||
- 100K entities: 1-5 seconds
|
||
- 1M entities: 10-30 seconds
|
||
- 10M entities: 1-3 minutes
|
||
|
||
---
|
||
|
||
### Scenario 8: Bulk Import
|
||
|
||
**User code**:
|
||
```typescript
|
||
await brain.addBatch([
|
||
{ data: 'Alice', type: 'person' },
|
||
{ data: 'Bob', type: 'person' },
|
||
// ... 10,000 more
|
||
])
|
||
```
|
||
|
||
**What happens in storage**:
|
||
```
|
||
1. Batch vector computation (parallel)
|
||
2. Batch shard distribution:
|
||
→ 10,000 entities → ~39 entities per shard (256 shards)
|
||
3. Parallel writes to storage:
|
||
→ 256 shards written in parallel
|
||
→ Each shard: ~39 files written
|
||
4. Single COW commit for entire batch:
|
||
→ 1 commit object
|
||
→ 1 tree object (or tree fan-out for large trees)
|
||
→ 10,000+ blobs (deduplicated)
|
||
5. Update indexes in batch:
|
||
→ HNSW: Batch insert (optimized)
|
||
→ Graph: Batch update
|
||
→ Metadata: Batch index update
|
||
```
|
||
|
||
**Performance**: 2-3 minutes for 10,000 entities (vs 4-8 hours without batching)
|
||
|
||
---
|
||
|
||
## 11. Summary
|
||
|
||
**Complete Storage Structure**:
|
||
- **3 storage layers**: branches/ (data), _cow/ (versions), _system/ (indexes)
|
||
- **2 files per entity**: vector + metadata (optimized I/O)
|
||
- **4 indexes**: HNSW (semantic), Type-Aware (filtering), Graph (relationships), Metadata (fields)
|
||
- **256 shards**: UUID-based (uniform distribution)
|
||
- **42 noun types + 127 verb types**: Type-first organization
|
||
- **Git-like COW**: Branches, commits, trees, blobs, refs
|
||
- **VFS support**: Traditional file/folder hierarchies
|
||
|
||
**Scalability**:
|
||
- Sharding: 200x faster for cloud storage
|
||
- Type-first: 42x faster for type filtering
|
||
- Lazy mode: 5-10x less memory for large datasets
|
||
- COW: Instant branches, efficient forks
|
||
- Deduplication: 30-90% storage savings
|
||
|
||
**Production Features**:
|
||
- Lifecycle policies (96% cost savings on cloud storage)
|
||
- Batch operations (efficient API usage)
|
||
- Compression (60-80% space savings on filesystem)
|
||
- Quota monitoring (OPFS browser limits)
|
||
- Auto-reinitialization (COW always-on, can't be broken)
|
||
|
||
---
|
||
|
||
## Next Steps
|
||
|
||
- [Storage Adapters](./storage-architecture.md) - Configure cloud storage backends
|
||
- [VFS Guide](../vfs/README.md) - Use Virtual File System features
|
||
- [Triple Intelligence](../vfs/TRIPLE_INTELLIGENCE.md) - Semantic file extraction
|
||
- [Scaling Guide](../SCALING.md) - Handle 10M+ entities
|
||
- [Performance Tuning](../PERFORMANCE.md) - Optimize for your use case
|
||
|
||
---
|
||
|
||
**Version**: v5.11.0
|
||
**Last Updated**: 2025-11-18
|
||
**Key Features**: COW always-on, type-first storage, 4-index architecture, VFS support, billion-scale optimization
|