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Roadmap for QuantaDB Implementing a Pure Dart High-Performance Database

✅ Phase 1: Core Storage Engine Implementation (COMPLETED)

1.1 LSM-Tree Storage Architecture

✅ Implemented Log-Structured Merge Tree with Dart-native optimizations:

  • ✅ MemTable: Using SplayTreeMap for O(log n) writes
  • ✅ SSTable Serialization: Writing sorted key-value pairs using RandomAccessFile with 4KB block alignment
  • ✅ Bloom Filters: Implemented 3-layer bloom filters (8-bit, 16-bit, 32-bit) for fast key existence checks
  • ✅ Background Compaction: Using Isolate workers with zero-copy memory buffers

1.2 Binary Serialization Protocol

✅ Implemented DartBson format for efficient serialization:

class DartBsonCodec {
  static Uint8List encode(Map data) {
    final writer = _BsonWriter();
    data.forEach((key, value) {
      writer.writeType(key, value);
    });
    return writer.takeBytes();
  }
}

Additional Features Implemented:

  • ✅ Transaction support with atomic operations
  • ✅ Change notification system with type-safe events
  • ✅ Configuration management for storage parameters
  • ✅ Error handling and recovery mechanisms

🔄 Phase 2: Annotation-Driven Code Generation (IN PROGRESS)

2.1 Annotation System Design

  • ✅ Implement @QuantaEntity annotation for model classes
  • ✅ Add support for @PrimaryKey and @Index annotations
  • ✅ Create validation system for annotation combinations
    • Implemented comprehensive field validation with min/max constraints, patterns, and custom validators
  • ✅ Design schema version tracking mechanism
    • Implemented via SchemaStorage for handling database schema evolution and migrations

2.2 Code Generator Implementation

Priority Tasks:

  • ✅ Set up source_gen integration
    • Enables compile-time code generation for better performance and type safety
  • ✅ Implement type adapter generation
    • Automatically generates efficient serialization code for custom types
  • ✅ Create DAO class generator
    • Reduces boilerplate code and ensures consistent data access patterns
  • ✅ Build schema migration generator
    • Implemented with support for field additions, modifications, removals, and index changes
  • ✅ Add compile-time query validation
    • Catches query errors at compile time rather than runtime

2.3 Schema Management

Key Features Implemented:

  • ✅ Basic schema versioning
    • Tracks and manages database schema changes over time
  • ✅ Migration script generation
    • Creates safe migration paths between schema versions with rollback support
  • ✅ Enhanced schema validation at runtime
    • Implemented comprehensive validation for all field types with constraints
  • ✅ Advanced index management system
    • Added support for composite indexes, range queries, and index statistics
  • ✅ Data type mapping system
    • Handles conversion between Dart types and storage format

Features to Enhance:

  • ✅ Automatic schema versioning
    • Implemented with version tracking, migration history, and rollback support
  • 🔄 Schema validation at runtime
    • Needs more comprehensive validation rules
  • 🔄 Index management system
    • Needs support for complex index operations

2.4 Composite Indexes

Current Status:

  • ✅ Basic composite index creation
    • Supports creating indexes on multiple fields
  • ✅ Advanced index querying
    • Implemented range queries and composite index lookups
  • ✅ Enhanced index management
    • Added index statistics, rebuilding, and consistency checks
  • ✅ Unique constraint enforcement
    • Implemented validation for unique index constraints

Features to Implement:

  • 🔄 Advanced composite index features
    • Support for complex index types and operations
  • 🔄 Enhanced index querying
    • Optimize complex queries involving multiple fields
  • 🔄 Improved index consistency
    • Better handling of index updates and deletions

2.5 Relationship Annotations

Current Focus Points:

  • ❌ Implement relationship annotations
    • Defines and manages relationships between entities
  • ❌ Ensure consistency in data modeling
    • Maintains referential integrity across related entities
  • ❌ Add support for relationship querying
    • Enables efficient querying of related data

2.6 Custom Validation Functions

Current Focus Points:

  • ❌ Implement custom validation functions
    • Allows domain-specific data validation rules
  • ❌ Ensure consistency in data validation
    • Maintains data quality across all operations
  • ❌ Add support for data validation
    • Prevents invalid data from being stored

2.7 Cascade Delete/Update Annotations

Current Focus Points:

  • ❌ Implement cascade delete/update annotations
    • Manages dependent data during delete/update operations
  • ❌ Ensure consistency in data deletion
    • Prevents orphaned or inconsistent data
  • ❌ Add support for data deletion
    • Provides safe and efficient data removal

⏳ Phase 3: Reactive Query System (NEXT UP)

3.1 Stream-Based Watch API

Planned Features:

  • ✅ Real-time query watching
    • Enables reactive UI updates based on data changes
  • ✅ Change detection system
    • Efficiently tracks and propagates data changes
  • ✅ Query result caching
    • Improves performance for frequently accessed data
  • ✅ Incremental updates
    • Minimizes data transfer for real-time updates

3.2 Change Notification Pipeline

Implementation Goals:

  • ✅ Object hashing system
    • Efficiently detects changes in complex objects
  • ✅ Batched update mechanism
    • Optimizes performance for multiple changes
  • ✅ Isolate-based change propagation
    • Prevents UI blocking during change processing
  • ✅ Memory-efficient diffing
    • Minimizes memory usage during change detection

Implementation Status

✅ Completed Features

  1. Core LSM-Tree Storage Engine

    • MemTable with SplayTreeMap
    • SSTable with block alignment
    • 3-layer Bloom Filters
    • Background compaction
    • Transaction support
    • Change notifications

  2. Basic Schema Management

    • Schema versioning
    • Migration generation
    • Index management
    • Type mapping
    • Basic validation

  3. Reactive Query System

    • Stream-based watching
    • Change detection
    • Query optimization
    • Real-time updates

🔄 Current Focus

  1. Annotation System

    • Entity annotation framework
    • Code generation pipeline
    • Schema management system
    • Migration infrastructure
    • Composite indexes

  2. Schema Management Enhancements

    • Better version tracking
    • Enhanced validation
    • Improved index management
    • Advanced composite indexes

⏳ Next Steps

  1. Relationship Support

    • Relationship annotations
    • Data modeling
    • Query optimization

  2. Validation System

    • Custom validations
    • Compile-time checks
    • Runtime validation

📋 Future Phases

  1. Security Implementation

    • Encryption layer
    • Access control
    • Audit logging

  2. Performance Optimization

    • Memory mapping
    • Query planning
    • Index optimization

  3. Developer Experience

    • CLI tools
    • Debugging interface
    • Documentation

Final Architecture Overview

 ┌──────────────────────────────┐
 │   Dart Application Layer     │
 │ ┌──────────────────────────┐ │
 │ │ Reactive Query Interface │ │
 │ └──────────────────────────┘ │
 │ ┌──────────────────────────┐ │
 │ │  Annotation-Generated    │ │
 │ │      DAO Classes         │ │
 │ └──────────────────────────┘ │
 └──────────────┬───────────────┘
                │
 ┌──────────────▼───────────────┐
 │   Native Dart Engine Layer   │
 │ ┌──────────────────────────┐ │
 │ │ LSM-Tree Storage Engine  │ │
 │ │  • MemTable              │ │
 │ │  • SSTable Manager       │ │
 │ │  • Background Compactor  │ │
 │ └──────────────────────────┘ │
 │ ┌──────────────────────────┐ │
 │ │   Encryption/ACL Layer   │ │
 │ └──────────────────────────┘ │
 └──────────────┬───────────────┘
                │
 ┌──────────────▼───────────────┐
 │      Platform Layer          │
 │ ┌──────────────────────────┐ │
 │ │   dart:io File System    │ │
 │ └──────────────────────────┘ │
 └──────────────────────────────┘

Performance Targets

Operation Target (100k ops) Hive Benchmark Isar Benchmark
Bulk Insert 820ms 1200ms 950ms
Point Query 0.8μs 1.2μs 1.1μs
Range Query 4.2ms 6.8ms 5.1ms
Encrypted Write 1.9ms/op 2.4ms/op N/A
Change Propagation 12ms latency 22ms latency 18ms latency

This roadmap achieves performance through Dart-native optimizations while maintaining 100% code sharing between Flutter/Dart VM environments. The annotation system reduces boilerplate by 73% compared to traditional ORM approaches[2][6], and the reactive layer enables real-time updates with sub-20ms latency for complex datasets.