AI-Powered Distributed Database Engine (APDDE) – Complete System Design

1. System Overview

• Purpose: Horizontally scalable, strongly consistent, multi-shard distributed database in modern C++ (C++20).
• Design Goals: Strong consistency (Raft), high availability, horizontal scalability, ACID transactions (MVCC), AI-driven optimization, hybrid data support, cloud-native deployment.

2. Core Architectural Layers

2.1 Client Layer

• Provides APIs (C++, Python, REST/gRPC)
• Transactional primitives (Begin, Put, Commit, Abort)
• Leader discovery, load balancing, retries

2.2 Front-End / Query Layer

• SQL parser and query planner
• AI-enhanced cost-based optimizer
• Transaction coordinator for multi-shard operations
• Cache manager with ML-guided eviction

2.3 Metadata & Routing Service

• Maintains cluster topology, shard placement, and schema information
• Metadata replication using Raft
• ShardMap and NodeRegistry structures

2.4 Consensus Layer (Raft)

• Leader election, term management
• Log replication and commit
• Snapshotting and log compaction
• Dynamic cluster membership
• Integration hooks for state machine application

2.5 Storage Engine

• MemTable (in-memory sorted map), WAL, SSTables
• Compaction, range tombstones, Bloom filters
• Columnar compression for analytics
• Checksum verification for integrity

2.6 Transaction & MVCC Layer

• Multi-Version Concurrency Control
• Snapshot isolation for reads
• Conflict detection and commit validation
• Two-Phase Commit (2PC) for distributed transactions

2.7 AI Engine Layer

• Query Optimizer AI for execution plan prediction
• Adaptive Indexer for automatic indexing
• Anomaly Detector for failure prediction
• Predictive Scaler for elastic scaling
• Self-Tuner for RL-based parameter adjustment
• Vector Engine for embedding storage and similarity search

2.8 Networking & RPC Layer

• gRPC or custom RPC over TCP with Protobuf or FlatBuffers
• Async I/O, connection pooling, batching
• Heartbeats, health checks, TLS encryption

2.9 Vector Indexing & AI-Native Data Layer

• HNSW, IVF, PQ indexes
• Hybrid queries combining structured + vector search
• GPU acceleration for vector similarity

2.10 Observability & Telemetry

• Metrics: Raft log lag, storage latency, query latency, cache hit ratio, AI inference time
• Prometheus exporter, OpenTelemetry tracing, Grafana dashboards

2.11 Security Layer

• TLS for RPC, RBAC, audit logging
• Encryption at rest (AES-256)
• Secure key management

2.12 Deployment & Scalability

• Kubernetes StatefulSets or bare-metal clusters
• Shard per pod with Raft replication
• Horizontal sharding and AI-driven replica scaling
• Rebalancing using consistent hashing or range split/merge
• Backup & recovery from snapshots and WAL

2.13 Testing & Verification

• Unit tests, fuzzing, chaos testing, simulation tests
• Load tests (YCSB, TPCC)
• AI model validation in shadow mode

2.14 Developer Ecosystem

• C++ and Python SDKs, CLI, Web UI dashboard
• REST Admin API, metrics, configuration management
• Docker Compose for local clusters
• CI/CD pipeline with GitHub Actions

3. End-to-End Data Flow (Write Path)

• Client writes → shard leader → WAL append → Raft replication → commit → MemTable + SSTable → snapshot

4. End-to-End AI Flow (Self-Tuning)

• Metrics collection → anomaly detection → RL-based self-tuning → AI optimization loop

5. Key Performance Targets

• Average latency <5 ms (single-shard)
• Cross-shard transaction latency <25 ms
• Throughput 100K ops/sec per shard
• Failover time <3 sec
• AI inference overhead <2% CPU
• Write amplification <2x

6. Future Extensions

• Full SQL optimizer with learned cost models
• Auto-indexing for vector and relational fields
• Integration with LLMs for semantic query generation
• Federated learning for multi-cluster tuning
• Cloud-native DBaaS with usage-based billing

7. Technology Stack Summary

• Language: C++20
• Build: CMake, Conan
• RPC: gRPC, Protobuf
• Consensus: Custom Raft
• Storage: RocksDB or custom LSM
• AI Inference: ONNX Runtime / LibTorch
• Vector Engine: FAISS, Hnswlib
• Monitoring: Prometheus + Grafana
• Tracing: OpenTelemetry
• Deployment: Kubernetes / Docker
• Testing: GoogleTest, RapidCheck

8. System Philosophy

• Autonomous, predictive, hybrid database combining human queries with AI-driven optimization and self-management.

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Dreams do come true -Beauttah K.