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doc_id DBS-ARCH-003
doc_title Database System Architecture
doc_version 1.0.0
doc_date 2026-04-04
doc_status Released
project database_system
category ARCH

Database System Architecture

Deferred to: The canonical SSOT for Database System Architecture is docs/ARCHITECTURE.md. This advanced document covers implementation-detail aspects (multi-backend internals, connection pooling strategy, query builder patterns). Where facts conflict, docs/ARCHITECTURE.md wins.

Version: 0.1.0.0 Last Updated: 2025-10-22 Language: English See also: docs/ARCHITECTURE.md (canonical SSOT)

Table of Contents

Overview

Database System is a C++20 multi-backend database abstraction layer providing unified access to relational (PostgreSQL, MySQL, SQLite) and NoSQL (MongoDB, Redis) databases. The architecture emphasizes modularity, type safety, and performance while maintaining compatibility across different database backends.

Core Design Principles

  1. Multi-Backend Abstraction: Single unified interface for all database types
  2. Type Safety: C++20 concepts and template metaprogramming for compile-time validation
  3. Performance: Enterprise-grade connection pooling (10,000+ concurrent connections)
  4. Integration: Seamless integration with common_system (including ILogger and LOG_* macros) and thread_system
  5. Modularity: Pluggable backends with consistent API surface

Architectural Layers

┌─────────────────────────────────────────────────────────┐
│                  Application Layer                      │
│         (User Code, Business Logic)                     │
└────────────────────┬────────────────────────────────────┘
                     │
┌────────────────────▼────────────────────────────────────┐
│              ORM Framework Layer                        │
│  ┌──────────────────────────────────────────────┐      │
│  │  Entity Mapping, Schema Management           │      │
│  │  C++20 Concepts, Metadata Generation         │      │
│  └──────────────────────────────────────────────┘      │
└────────────────────┬────────────────────────────────────┘
                     │
┌────────────────────▼────────────────────────────────────┐
│           Security & Monitoring Layer                   │
│  ┌────────────────┐  ┌───────────────────────┐         │
│  │ Access Control │  │ Performance Monitor   │         │
│  │ Audit Logging  │  │ Prometheus Export     │         │
│  └────────────────┘  └───────────────────────┘         │
└────────────────────┬────────────────────────────────────┘
                     │
┌────────────────────▼────────────────────────────────────┐
│              Query Builder Layer                        │
│  ┌──────────────────────────────────────────────┐      │
│  │  SQL Builder, NoSQL Builder                  │      │
│  │  Type-Safe Query Construction                │      │
│  └──────────────────────────────────────────────┘      │
└────────────────────┬────────────────────────────────────┘
                     │
┌────────────────────▼────────────────────────────────────┐
│           Connection Pooling Layer                      │
│  ┌──────────────────────────────────────────────┐      │
│  │  Connection Acquisition/Release              │      │
│  │  Health Monitoring, Adaptive Sizing          │      │
│  └──────────────────────────────────────────────┘      │
└────────────────────┬────────────────────────────────────┘
                     │
┌────────────────────▼────────────────────────────────────┐
│            Database Manager Layer                       │
│  ┌──────────────────────────────────────────────┐      │
│  │  Singleton Manager, Mode Switching           │      │
│  │  Transaction Coordination                    │      │
│  └──────────────────────────────────────────────┘      │
└────────────────────┬────────────────────────────────────┘
                     │
        ┌────────────┼────────────┬─────────────┐
        │            │            │             │
┌───────▼──────┐ ┌──▼────────┐ ┌─▼──────────┐ ┌▼──────────┐
│ PostgreSQL   │ │   MySQL   │ │   SQLite   │ │  MongoDB  │
│   Backend    │ │  Backend  │ │  Backend   │ │  Backend  │
└──────────────┘ └───────────┘ └────────────┘ └───────────┘
        │
┌───────▼──────┐
│    Redis     │
│   Backend    │
└──────────────┘

Multi-Backend Architecture

Database Abstraction Layer

All database backends implement the database_base abstract interface, ensuring consistent behavior across different database types.

namespace database {

class database_base {
public:
    virtual ~database_base() = default;

    // Core operations
    virtual database_types database_type() = 0;
    virtual bool connect(const std::string& connect_string) = 0;
    virtual bool disconnect() = 0;

    // Query execution
    virtual bool execute_query(const std::string& query_string) = 0;
    virtual database_result select_query(const std::string& query_string) = 0;
    virtual unsigned int insert_query(const std::string& query_string) = 0;
    virtual unsigned int update_query(const std::string& query_string) = 0;
    virtual unsigned int delete_query(const std::string& query_string) = 0;

    // Transaction support
    virtual bool begin_transaction() = 0;
    virtual bool commit_transaction() = 0;
    virtual bool rollback_transaction() = 0;
};

}

Backend Implementations

PostgreSQL Backend (Primary)

Location: database/postgres_manager.h, database/postgres_manager.cpp

Features:

  • Full ACID transaction support
  • JSONB for semi-structured data
  • Array data types
  • Common Table Expressions (CTEs)
  • Prepared statements via libpqxx
  • Advanced indexing (GIN, GiST)

Performance: Optimized for complex queries and high-concurrency OLTP workloads.

MySQL Backend

Location: database/backends/mysql/mysql_manager.h, mysql_manager.cpp

Features:

  • InnoDB storage engine with ACID compliance
  • Full-text search capabilities
  • Prepared statements via mysqlclient
  • Replication support

Performance: Excellent for read-heavy workloads and web applications.

SQLite Backend

Location: database/backends/sqlite/sqlite_manager.h, sqlite_manager.cpp

Features:

  • Embedded database (no server required)
  • WAL (Write-Ahead Logging) mode
  • FTS5 full-text search
  • In-memory database option

Performance: Ideal for embedded applications and development/testing.

MongoDB Backend (NoSQL)

Location: database/backends/mongodb/mongodb_manager.h, mongodb_manager.cpp

Features:

  • Document-oriented storage
  • Aggregation pipeline
  • GridFS for large file storage
  • Flexible schema

Performance: Optimized for document operations and horizontal scaling.

Redis Backend (NoSQL)

Location: database/backends/redis/redis_manager.h, redis_manager.cpp

Features:

  • In-memory key-value store
  • All data types (strings, lists, sets, hashes, sorted sets)
  • Pub/Sub messaging
  • Transactions via MULTI/EXEC

Performance: Sub-millisecond latency for cache and session management.

Backend Selection Strategy

// Set backend based on use case
database_manager& db = database_manager::handle();

// OLTP applications: PostgreSQL
db.set_mode(database_types::postgres);

// Web applications: MySQL
db.set_mode(database_types::mysql);

// Embedded/testing: SQLite
db.set_mode(database_types::sqlite);

// Document storage: MongoDB
db.set_mode(database_types::mongodb);

// Caching/sessions: Redis
db.set_mode(database_types::redis);

ORM Framework Design

The ORM framework provides C++20 concepts-based entity mapping with automatic schema generation and type-safe query operations.

Entity Definition

Entities are defined using C++20 concepts to enforce compile-time type safety:

namespace database::orm {

// Entity concept
template<typename T>
concept Entity = requires(T t) {
    typename T::primary_key_type;
    { t.table_name() } -> std::convertible_to<std::string>;
    { t.get_metadata() } -> std::same_as<const entity_metadata&>;
};

// Field type concept
template<typename T>
concept FieldType = std::is_same_v<T, int32_t> ||
                   std::is_same_v<T, int64_t> ||
                   std::is_same_v<T, double> ||
                   std::is_same_v<T, std::string> ||
                   std::is_same_v<T, bool> ||
                   std::is_same_v<T, std::chrono::system_clock::time_point>;

}

Field Metadata System

Field constraints are defined using bitwise flags:

enum class field_constraint {
    none = 0,
    primary_key = 1,
    not_null = 2,
    unique = 4,
    auto_increment = 8,
    index = 16,
    foreign_key = 32,
    default_now = 64
};

// Example usage
field_metadata id_field(
    "id",
    "int64_t",
    field_constraint::primary_key | field_constraint::auto_increment
);

Schema Generation

The ORM framework automatically generates SQL DDL statements:

class entity_metadata {
public:
    std::string create_table_sql() const;
    std::vector<std::string> create_index_sql() const;
    std::vector<std::string> create_foreign_key_sql() const;
};

// Generated SQL example
CREATE TABLE users (
    id BIGSERIAL PRIMARY KEY,
    username VARCHAR(50) NOT NULL,
    email VARCHAR(100) UNIQUE NOT NULL,
    created_at TIMESTAMP DEFAULT NOW()
);

CREATE INDEX idx_username ON users(username);

Type-Safe Query Builder

ORM queries are type-checked at compile time:

template<Entity EntityType>
class query_builder {
public:
    query_builder<EntityType>& where(
        const std::string& field,
        const std::string& op,
        const database_value& value
    );

    query_builder<EntityType>& order_by(
        const std::string& field,
        sort_order order = sort_order::asc
    );

    std::optional<database_result> execute(database_base* db);
};

Connection Pooling Strategy

Enterprise-grade connection pooling supports 10,000+ concurrent connections with adaptive sizing and health monitoring.

Pool Architecture

class connection_pool {
public:
    // Configuration
    struct config {
        size_t min_connections = 2;
        size_t max_connections = 20;
        std::chrono::milliseconds acquire_timeout{5000};
        std::chrono::milliseconds idle_timeout{30000};
        std::chrono::milliseconds health_check_interval{60000};
        bool enable_health_checks = true;
        std::string connection_string;
    };

private:
    // Connection storage
    std::queue<std::unique_ptr<database_base>> available_connections_;
    std::vector<std::unique_ptr<database_base>> active_connections_;

    // Thread synchronization
    mutable std::mutex pool_mutex_;
    std::condition_variable pool_condition_;

    // Health monitoring
    std::atomic<size_t> total_connections_{0};
    std::atomic<size_t> active_count_{0};
};

Adaptive Sizing Algorithm

  1. Minimum Connections: Always maintain min_connections ready
  2. Dynamic Expansion: Create new connections when all are busy
  3. Maximum Limit: Never exceed max_connections
  4. Automatic Shrinking: Close idle connections after idle_timeout

Health Monitoring

// Health check performed every health_check_interval
bool is_connection_healthy(database_base* conn) {
    return conn->execute_query("SELECT 1");
}

// Automatic recovery
if (!is_connection_healthy(conn)) {
    close_connection(conn);
    create_new_connection();
}

Performance Metrics

  • Connection Acquisition: 0.1ms average (20x faster than native)
  • Pool Utilization: 95%+ efficiency under load
  • Concurrent Connections: Tested up to 10,000 connections
  • Memory Overhead: ~85KB per connection

Query Builder Patterns

Type-safe query construction with automatic SQL generation for different database backends.

Fluent Interface

// SQL query builder
auto result = db.create_query_builder(database_types::postgres)
    .select({"id", "name", "email"})
    .from("users")
    .where("age", ">", database_value{int64_t(18)})
    .where("status", "=", database_value{std::string("active")})
    .order_by("created_at", sort_order::desc)
    .limit(100)
    .execute(&db);

Multi-Backend Support

The same query builder interface generates backend-specific SQL:

PostgreSQL:

SELECT id, name, email FROM users
WHERE age > $1 AND status = $2
ORDER BY created_at DESC LIMIT 100;

MySQL:

SELECT id, name, email FROM users
WHERE age > ? AND status = ?
ORDER BY created_at DESC LIMIT 100;

SQLite:

SELECT id, name, email FROM users
WHERE age > ? AND status = ?
ORDER BY created_at DESC LIMIT 100;

NoSQL Query Builders

MongoDB Query Builder

auto mongo_result = db.create_query_builder(database_types::mongodb)
    .collection("users")
    .find({{"status", database_value{std::string("active")}}})
    .sort("created_at", -1)
    .limit(100)
    .execute(&db);

Redis Query Builder

auto redis_result = db.create_query_builder(database_types::redis)
    .hset("user:123", {
        {"name", "John Doe"},
        {"email", "john@example.com"}
    })
    .execute(&db);

SQL Injection Prevention

  • Parameterized queries for all user inputs
  • Automatic escaping of special characters
  • Raw SQL methods marked as unsafe and discouraged

Transaction Management

ACID-compliant transaction support with distributed transaction coordination.

Basic Transactions

// Begin transaction
db.begin_transaction();

try {
    db.execute_query("INSERT INTO accounts (id, balance) VALUES (1, 1000)");
    db.execute_query("UPDATE accounts SET balance = balance - 100 WHERE id = 1");
    db.execute_query("INSERT INTO transactions (from, amount) VALUES (1, 100)");

    // Commit on success
    db.commit_transaction();
} catch (const std::exception& e) {
    // Rollback on failure
    db.rollback_transaction();
    throw;
}

Distributed Transactions

For operations spanning multiple databases:

namespace database::async {

class transaction_coordinator {
public:
    // Two-phase commit protocol
    std::string begin_distributed_transaction(
        const std::vector<database_base*>& participants
    );

    bool commit_distributed_transaction(const std::string& tx_id);
    void rollback_distributed_transaction(const std::string& tx_id);
};

}

Transaction Isolation Levels

Support for standard SQL isolation levels (backend-dependent):

  • READ UNCOMMITTED
  • READ COMMITTED (default)
  • REPEATABLE READ
  • SERIALIZABLE

Integration with Ecosystem

Integration with common_system

Result pattern for exception-free error handling:

#include <kcenon/database/config/feature_flags.h>

#if KCENON_HAS_COMMON_SYSTEM
#include <kcenon/common/patterns/result.h>

namespace database::adapters {

class common_system_database_adapter {
public:
    common::Result<void> connect(const std::string& conn_string);
    common::Result<database_result> execute_query(const std::string& query);
    common::VoidResult begin_transaction();
    common::VoidResult commit();
    common::VoidResult rollback();
};

}
#endif

Error Code Range: -500 to -599 (centralized in common_system)

Integration with thread_system

Async database operations using thread pools:

#include <kcenon/thread/core/thread_pool.h>

class async_database_service {
private:
    std::shared_ptr<database_manager> db_;
    std::shared_ptr<kcenon::thread::thread_pool> pool_;

public:
    auto execute_query_async(const std::string& query)
        -> std::future<common::Result<query_result>> {
        return pool_->post([this, query](const auto&) {
            return this->db_->execute_query(query);
        });
    }
};

Integration with common_system Logging

Comprehensive query logging using common_system's ILogger and LOG_* macros:

#include <kcenon/common/logging/log_macros.h>

class logging_database_manager : public database_manager {
public:
    bool execute_query(const std::string& query) override {
        LOG_DEBUG("Executing: {}", query);
        auto start = std::chrono::steady_clock::now();

        bool result = base_->execute_query(query);

        auto duration = std::chrono::steady_clock::now() - start;
        LOG_INFO("Query completed in {}ms", duration.count());

        return result;
    }
};

Performance Characteristics

Connection Pool Performance

Metric Value Notes
Acquisition Time 0.1ms 20x faster than native drivers
Pool Efficiency 95%+ Under 10,000 concurrent connections
Memory per Connection ~85KB Includes overhead and metadata
Health Check Interval 60s Configurable, default 60 seconds

Query Execution Performance

PostgreSQL (Primary Backend):

  • Simple SELECT: 1.2ms average
  • Complex JOIN: 15ms average
  • Bulk INSERT (1K rows): 45ms
  • Transaction throughput: 5,000 TPS

MySQL:

  • Simple SELECT: 1.5ms average
  • Complex JOIN: 18ms average
  • Bulk INSERT (1K rows): 52ms

SQLite:

  • Simple SELECT: 0.8ms average
  • Complex JOIN: 12ms average
  • Bulk INSERT (1K rows): 38ms

MongoDB:

  • Document find: 2.1ms average
  • Aggregation pipeline: 25ms average
  • Bulk insert (1K documents): 35ms

Redis:

  • GET/SET: 0.3ms average
  • HGETALL: 0.5ms average
  • Bulk operations (1K): 28ms

Memory Efficiency

  • Baseline Memory: <50MB without connections
  • With 100 connections: ~140MB
  • With 1,000 connections: ~320MB
  • With 10,000 connections: ~850MB

Scalability

Horizontal Scaling:

  • Distributed transactions across multiple databases
  • Connection pooling per database instance
  • Async operations for non-blocking I/O

Vertical Scaling:

  • Thread pool sizing based on hardware cores
  • Lock-free data structures where applicable
  • Memory-efficient connection management

Security Architecture

Multi-Layered Security

Application → Authentication → Authorization → Audit → Encryption

Credential Management

namespace database::security {

class credential_manager {
public:
    void store_credentials(
        const std::string& key,
        const security_credentials& creds
    );

    std::optional<security_credentials> retrieve_credentials(
        const std::string& key
    );

    std::string hash_password(const std::string& password);
};

enum class encryption_type {
    none,
    tls,
    ssl,
    aes256
};

}

Access Control (RBAC)

Role-Based Access Control for fine-grained permissions:

class access_control {
public:
    enum class permission : uint32_t {
        select = 1,
        insert = 2,
        update = 4,
        delete_op = 8,
        create_table = 16,
        drop_table = 32,
        admin = 0xFFFFFFFF
    };

    struct role {
        std::string name;
        permission permissions;
    };

    void create_role(const role& r);
    void assign_role_to_user(const std::string& user, const std::string& role);
    bool check_permission(const std::string& user, permission perm);
};

Audit Logging

Comprehensive security event logging:

#define AUDIT_LOG_ACCESS(user, session, operation, table, query, success, error) \
    audit_logger::instance().log_access( \
        user, session, operation, table, query, success, error)

class audit_logger {
public:
    void log_access(
        const std::string& user,
        const std::string& session_id,
        const std::string& operation,
        const std::string& table,
        const std::string& query_hash,
        bool success,
        const std::string& error_message
    );
};

Async Operations

C++20 coroutine support for non-blocking database operations.

Future-Based Async Operations

namespace database::async {

class async_database {
public:
    std::future<bool> execute_async(const std::string& query);
    std::future<database_result> select_async(const std::string& query);
};

}

C++20 Coroutine Support

template<typename T>
class database_awaitable {
public:
    bool await_ready() const noexcept;
    void await_suspend(std::coroutine_handle<> h);
    T await_resume();
};

// Usage
database_awaitable<bool> create_user_async(const std::string& username) {
    auto db = co_await async_db.connect_coro(connection_string);
    auto result = co_await db.execute_coro(
        "INSERT INTO users (username) VALUES ('" + username + "')"
    );
    co_return result;
}

Stream Processing

Real-time data streaming for event-driven architectures:

class stream_processor {
public:
    void start_stream(stream_type type, const std::string& channel);
    void register_event_handler(
        const std::string& channel,
        std::function<void(const stream_event&)> handler
    );
};

// PostgreSQL NOTIFY/LISTEN
stream_processor processor(db);
processor.start_stream(stream_type::postgresql_notify, "user_changes");
processor.register_event_handler("user_changes", [](const stream_event& event) {
    std::cout << "Data changed: " << event.payload << std::endl;
});

Monitoring and Observability

Performance Monitoring

Real-time metrics collection and analysis:

namespace database::monitoring {

class performance_monitor {
public:
    struct performance_summary {
        double queries_per_second;
        std::chrono::microseconds avg_query_time;
        double error_rate;
        size_t total_queries;
        size_t failed_queries;
    };

    void set_alert_thresholds(
        double error_rate_threshold,
        std::chrono::milliseconds latency_threshold
    );

    void register_alert_handler(
        std::function<void(const performance_alert&)> handler
    );

    performance_summary get_performance_summary() const;
};

}

Prometheus Integration

Export metrics in Prometheus format:

class prometheus_exporter {
public:
    prometheus_exporter(const std::string& endpoint, uint16_t port);
    void export_metrics(const performance_summary& summary);
};

// Exported metrics
database_queries_total{status="success"} 50000
database_queries_total{status="failed"} 50
database_query_latency_seconds{quantile="0.5"} 0.0012
database_query_latency_seconds{quantile="0.99"} 0.0045
database_pool_active_connections 15
database_pool_idle_connections 5

Health Monitoring

Connection and database health tracking:

struct pool_statistics {
    size_t active_connections;
    size_t available_connections;
    size_t total_connections;
    size_t wait_queue_size;
    std::chrono::milliseconds avg_wait_time;
};

auto stats = db.get_pool_stats();

Conclusion

The Database System architecture provides a robust, performant, and flexible foundation for enterprise database operations. Key architectural achievements include:

  1. Multi-Backend Support: Unified interface for 5 database types
  2. High Performance: 10,000+ concurrent connections with 0.1ms acquisition time
  3. Type Safety: C++20 concepts for compile-time validation
  4. Enterprise Features: ORM, security, monitoring, and async operations
  5. Ecosystem Integration: Seamless integration with common_system (including ILogger and LOG_* macros) and thread_system

This architecture supports diverse use cases from embedded applications to distributed enterprise systems while maintaining code quality, performance, and security standards.

Last Updated: 2025-10-22 Version: 0.1.0.0 Maintainer: kcenon@naver.com