High-Performance Database Engine

Creating a high-performance database application in C++ involves a range of tasks, including efficient data structures, shared and optimized memory management, safe message and network communication, persistent storage, and so much more. This section examines how to get started.

Libraries
Sample Database Engine using Containers
Optimize Memory Allocation
Integrate Shared Memory
Create a Thread-safe Queue for Inter-thread Communication
Add Serialization to Persistently Store the Database
See Also

Libraries

Here are some Boost libraries that might be useful when planning and building your database app:

Boost.Container : Provides STL-compatible containers, including stable vector, flat set/map and more. The containers provided by this library can offer performance benefits over their standard library equivalents, making them a good fit for a high-performance database application.
Boost.Pool : This library is used for simple, fast memory allocation and can improve efficiency in some scenarios by managing memory in chunks.
Boost.Interprocess : This library allows for shared memory communication and synchronization between processes. In a database context, this can be useful for inter-process communication (IPC) and shared memory databases.
Boost.Lockfree : Provides lock-free data structures which could be useful in multi-threaded database applications where you want to avoid locking overhead.
Boost.Serialization : If you need to serialize objects for storage, Boost.Serialization can be a useful tool. However, be aware that for many database applications, more specialized serialization formats (like Protocol Buffers, Thrift, etc.) might be more appropriate.
Boost.Asio : Provides a consistent asynchronous model using a modern C++ approach for network and low-level I/O programming. It supports a variety of network protocols, which could be helpful if your database needs to communicate over a network.
Boost.Thread : Provides a portable interface for multithreading, which can be crucial when creating a high-performance database that can handle multiple queries concurrently.
Boost.Fiber : Allows you to write code that works with fibers, which are user-space threads that can be used to write concurrent code. This can be useful in situations where you have many tasks that need to run concurrently but are I/O-bound rather than CPU-bound.
Boost.Polygon or Boost.Geometry : For storing and querying spatial data, these libraries can provide the necessary data types and algorithms.
Boost.Filesystem : Provides a portable way of querying and manipulating paths, files, and directories.

Sample Database Engine using Containers

A database engine requires efficient data structures for handling indexes, caches, and storage layouts. The Boost.Container library provides drop-in replacements for standard containers like std::vector, std::map, and std::unordered_map, but optimized for memory efficiency and performance.

In the following sample code, we will use in-memory indexing as the basis of a database engine. The Boost.Container flat_map feature is used to store a sorted index for quick lookups, and the stable_vector feature to store persistent records with stable pointers. The sample demonstrates inserting and retrieving records efficiently.

#include <boost/container/flat_map.hpp>
#include <boost/container/stable_vector.hpp>
#include <iostream>
#include <string>

// Define a Simple Database Table Structure
struct Record {
    int id;             // Primary Key
    std::string name;   // Represents record data

    Record(int id, std::string name) : id(id), name(std::move(name)) {}
};

// Implement a Database Table Class
#include <boost/container/flat_map.hpp>
#include <boost/container/stable_vector.hpp>
#include <iostream>
#include <string>

class DatabaseTable {
public:
    using RecordStorage = boost::container::stable_vector<Record>;
    using IndexMap = boost::container::flat_map<int, size_t>; // Fast lookup

    void insert(int id, const std::string& name) {
        size_t index = records.size();
        records.emplace_back(id, name);
        index_map[id] = index;
    }

    const Record* find(int id) {
        auto it = index_map.find(id);
        if (it != index_map.end()) {
            return &records[it->second];
        }
        return nullptr;
    }

    void print_all() const {
        for (const auto& record : records) {
            std::cout << "ID: " << record.id << ", Name: " << record.name << "\n";
        }
    }

private:
    RecordStorage records;  // Stores records in a stable manner
    IndexMap index_map;     // Provides fast ID lookups
};

// Demonstrate Database Operations
int main() {
    DatabaseTable db;

    // Insert records
    db.insert(101, "Alice");
    db.insert(102, "Bob");
    db.insert(103, "Charlie");

    // Retrieve a record
    const Record* record = db.find(102);
    if (record) {
        std::cout << "Found: ID = " << record->id << ", Name = " << record->name << "\n";
    } else {
        std::cout << "Record not found!\n";
    }

    // Print all records
    std::cout << "All records:\n";
    db.print_all();

    return 0;
}

Note: Key features of this sample are that it is memory-efficient (reducing fragmentation and with good performance), stable_vector prevents invalid references when resizing, and flat_map is faster than std::map for heavy use.

Optimize Memory Allocation

As we are dealing with frequent allocations of small objects (the database records) we’ll enhance our database engine by using Boost.Pool. This library avoids repeated calls to malloc, new and delete.

#include <boost/container/flat_map.hpp>
#include <boost/pool/pool.hpp>
#include <iostream>
#include <string>

struct Record {
    int id;
    std::string name;

    Record(int id, std::string name) : id(id), name(std::move(name)) {}
};

class DatabaseTable {
public:
    using IndexMap = boost::container::flat_map<int, Record*>;

    DatabaseTable() : recordPool(sizeof(Record)) {}

    Record* insert(int id, const std::string& name) {
        void* memory = recordPool.malloc(); // Allocate memory from the pool
        if (!memory) {
            throw std::bad_alloc();
        }

        Record* newRecord = new (memory) Record(id, name); // Placement new
        index_map[id] = newRecord;
        return newRecord;
    }

    void remove(int id) {
        auto it = index_map.find(id);
        if (it != index_map.end()) {
            it->second->~Record(); // Call destructor
            recordPool.free(it->second); // Free memory back to the pool
            index_map.erase(it);
        }
    }

    Record* find(int id) {
        auto it = index_map.find(id);
        return (it != index_map.end()) ? it->second : nullptr;
    }

    void print_all() {
        for (const auto& pair : index_map) {
            std::cout << "ID: " << pair.first << ", Name: " << pair.second->name << "\n";
        }
    }

    ~DatabaseTable() {
        for (const auto& pair : index_map) {
            pair.second->~Record();
            recordPool.free(pair.second);
        }
    }

private:
    boost::pool<> recordPool;
    IndexMap index_map;
};

// Demonstrate Efficient Memory Use
int main() {
    DatabaseTable db;

    // Insert records
    db.insert(101, "Alice");
    db.insert(102, "Bob");
    db.insert(103, "Charlie");

    // Retrieve a record
    Record* record = db.find(102);
    if (record) {
        std::cout << "Found: ID = " << record->id << ", Name = " << record->name << "\n";
    }

    // Remove a record
    db.remove(102);
    if (!db.find(102)) {
        std::cout << "Record 102 removed successfully.\n";
    }

    // Print all records
    std::cout << "All records:\n";
    db.print_all();

    return 0;
}

Note: Custom Object Pools can be tuned for your specific object sizes.

Integrate Shared Memory

In a realistic database environment, you would probably want to enable a shared-memory database table that multiple processes can access simultaneously. For this, we need the features of Boost.Interprocess. This library enables multiple processes to share the same data faster than inter-process communication (IPC) via files or sockets, and includes mutexes and condition variables.

We modify our DatabaseTable to store records in shared memory instead of standard heap memory.

#include <boost/interprocess/managed_shared_memory.hpp>
#include <boost/interprocess/sync/named_mutex.hpp>
#include <boost/container/flat_map.hpp>
#include <iostream>
#include <string>

namespace bip = boost::interprocess;

struct Record {
    int id;
    char name[32];

    Record(int id, const std::string& name) : id(id) {
        std::strncpy(this->name, name.c_str(), sizeof(this->name));
        this->name[sizeof(this->name) - 1] = '\0'; // Ensure null termination
    }
};

class SharedDatabase {
public:
    SharedDatabase()
        : segment(bip::open_or_create, "SharedMemory", 65536) // 64 KB shared memory
    {
        table = segment.find_or_construct<TableType>("RecordTable")();
    }

    void insert(int id, const std::string& name) {
        bip::scoped_lock<bip::named_mutex> lock(mutex);
        if (table->find(id) == table->end()) {
            Record* record = segment.construct<Record>(bip::anonymous_instance)(id, name);
            (*table)[id] = record;
        }
    }

    Record* find(int id) {
        bip::scoped_lock<bip::named_mutex> lock(mutex);
        auto it = table->find(id);
        return (it != table->end()) ? it->second : nullptr;
    }

    void remove(int id) {
        bip::scoped_lock<bip::named_mutex> lock(mutex);
        auto it = table->find(id);
        if (it != table->end()) {
            segment.destroy_ptr(it->second);
            table->erase(it);
        }
    }

    void print_all() {
        bip::scoped_lock<bip::named_mutex> lock(mutex);
        for (const auto& pair : *table) {
            std::cout << "ID: " << pair.first << ", Name: " << pair.second->name << "\n";
        }
    }

private:
    using TableType = boost::container::flat_map<int, Record*, std::less<int>, bip::allocator<std::pair<const int, Record*>, bip::managed_shared_memory::segment_manager>>;

    bip::managed_shared_memory segment;
    TableType* table;
    static inline bip::named_mutex mutex{bip::open_or_create, "SharedDBMutex"};
};

// Process 1 (Writer) – Insert and Modify Data
int main() {
    SharedDatabase db;

    db.insert(1, "Alice");
    db.insert(2, "Bob");

    std::cout << "Process 1 - Initial Records:\n";
    db.print_all();

    return 0;
}

// Process 2 (Reader) – Access Shared Memory Data
int main() {
    SharedDatabase db;

    std::cout << "Process 2 - Records in Shared Memory:\n";
    db.print_all();

    return 0;
}

Note: The sample now avoids manual memory management, prevents race conditions through the use of mutexes, and multiple apps or processes can interact with the database.

Create a Thread-safe Queue for Inter-thread Communication

With multiple apps or processes now accessing our database, would seem like a good idea to avoid locks or bottlenecks. Boost.Lockfree offers message queues and pre-allocated ring buffers for this purpose.

#include <boost/interprocess/managed_shared_memory.hpp>
#include <boost/interprocess/sync/named_mutex.hpp>
#include <boost/container/flat_map.hpp>
#include <boost/lockfree/queue.hpp>
#include <iostream>
#include <string>
#include <thread>
#include <atomic>

namespace bip = boost::interprocess;

// Structure for storing records
struct Record {
    int id;
    char name[32];

    Record(int id, const std::string& name) : id(id) {
        std::strncpy(this->name, name.c_str(), sizeof(this->name));
        this->name[sizeof(this->name) - 1] = '\0'; // Ensure null termination
    }
};

// Enum for operation types in the queue
enum class OperationType { INSERT, REMOVE, FIND, PRINT };

// Structure for a queued database operation
struct DatabaseTask {
    OperationType type;
    int id;
    std::string name;
};

// Shared database class
class SharedDatabase {
public:
    SharedDatabase()
        : segment(bip::open_or_create, "SharedMemory", 65536), // 64 KB shared memory
          task_queue(128) // Lock-free queue with capacity of 128 tasks
    {
        table = segment.find_or_construct<TableType>("RecordTable")();
    }

    void enqueue_task(const DatabaseTask& task) {
        while (!task_queue.push(task)); // Non-blocking push
    }

    void process_tasks() {
        DatabaseTask task;
        while (task_queue.pop(task)) { // Non-blocking pop
            execute_task(task);
        }
    }

    void execute_task(const DatabaseTask& task) {
        bip::scoped_lock<bip::named_mutex> lock(mutex);

        switch (task.type) {
            case OperationType::INSERT:
                if (table->find(task.id) == table->end()) {
                    Record* record = segment.construct<Record>(bip::anonymous_instance)(task.id, task.name);
                    (*table)[task.id] = record;
                }
                break;

            case OperationType::REMOVE:
                if (table->find(task.id) != table->end()) {
                    segment.destroy_ptr((*table)[task.id]);
                    table->erase(task.id);
                }
                break;

            case OperationType::FIND:
                if (table->find(task.id) != table->end()) {
                    std::cout << "Found: ID=" << task.id << ", Name=" << (*table)[task.id]->name << "\n";
                } else {
                    std::cout << "Record with ID=" << task.id << " not found.\n";
                }
                break;

            case OperationType::PRINT:
                for (const auto& pair : *table) {
                    std::cout << "ID: " << pair.first << ", Name: " << pair.second->name << "\n";
                }
                break;
        }
    }

private:
    using TableType = boost::container::flat_map<int, Record*, std::less<int>, bip::allocator<std::pair<const int, Record*>, bip::managed_shared_memory::segment_manager>>;

    bip::managed_shared_memory segment;
    TableType* table;
    static inline bip::named_mutex mutex{bip::open_or_create, "SharedDBMutex"};

    boost::lockfree::queue<DatabaseTask> task_queue;
};

// Run Multiple Threads to Insert and Query Records
int main() {
    SharedDatabase db;

    // Start a worker thread to process tasks
    std::thread worker([&db]() {
        while (true) {
            db.process_tasks();
            std::this_thread::sleep_for(std::chrono::milliseconds(100));
        }
    });

    // Insert records
    db.enqueue_task({OperationType::INSERT, 1, "Alice"});
    db.enqueue_task({OperationType::INSERT, 2, "Bob"});
    db.enqueue_task({OperationType::INSERT, 3, "Charlie"});

    // Find a record
    db.enqueue_task({OperationType::FIND, 2, ""});

    // Print all records
    db.enqueue_task({OperationType::PRINT, 0, ""});

    // Remove a record
    db.enqueue_task({OperationType::REMOVE, 2, ""});

    // Print all records again
    db.enqueue_task({OperationType::PRINT, 0, ""});

    // Let the worker thread process
    std::this_thread::sleep_for(std::chrono::seconds(1));

    return 0;
}

Note: A lock-free queue prevents thread contention, while a separate worker thread processes the queued tasks.

Add Serialization to Persistently Store the Database

Finally, let’s add the features of Boost.Serialization to allow us to save and restore snapshots of our shared-memory database, making it persistent across program runs. We will extend our sample to serialize the records into an archive format (such as binary, XML, or text).

#include <boost/serialization/access.hpp>
#include <boost/serialization/string.hpp>

struct Record {
    int id;
    std::string name;

    Record() = default; // Needed for deserialization
    Record(int id, const std::string& name) : id(id), name(name) {}

    template<class Archive>
    void serialize(Archive& ar, const unsigned int version) {
        ar & id & name;
    }
};

// Implement Save and Load Functions
// Serialize the entire database to a file and deserialize it to restore data.
#include <boost/archive/text_oarchive.hpp>
#include <boost/archive/text_iarchive.hpp>
#include <boost/serialization/map.hpp>
#include <fstream>

class SharedDatabase {
public:
    SharedDatabase()
        : segment(bip::open_or_create, "SharedMemory", 65536),
          task_queue(128)
    {
        table = segment.find_or_construct<TableType>("RecordTable")();
    }

    void save_snapshot(const std::string& filename) {
        std::map<int, Record> snapshot;

        for (const auto& pair : *table) {
            snapshot[pair.first] = *pair.second;
        }

        std::ofstream ofs(filename);
        boost::archive::text_oarchive oa(ofs);
        oa << snapshot;

        std::cout << "📀 Snapshot saved to " << filename << "\n";
    }

    void load_snapshot(const std::string& filename) {
        std::ifstream ifs(filename);
        if (!ifs) {
            std::cerr << "⚠ Snapshot file not found!\n";
            return;
        }

        std::map<int, Record> snapshot;
        boost::archive::text_iarchive ia(ifs);
        ia >> snapshot;

        for (const auto& pair : snapshot) {
            if (table->find(pair.first) == table->end()) {
                Record* record = segment.construct<Record>(bip::anonymous_instance)(pair.first, pair.second.name);
                (*table)[pair.first] = record;
            }
        }

        std::cout << "📂 Snapshot loaded from " << filename << "\n";
    }

private:
    using TableType = boost::container::flat_map<int, Record*, std::less<int>, bip::allocator<std::pair<const int, Record*>, bip::managed_shared_memory::segment_manager>>;

    bip::managed_shared_memory segment;
    TableType* table;
    static inline bip::named_mutex mutex{bip::open_or_create, "SharedDBMutex"};

    boost::lockfree::queue<DatabaseTask> task_queue;
};

// Modify main to Save and Restore Snapshots
int main() {
    SharedDatabase db;

    // Load a previous snapshot (if it exists)
    db.load_snapshot("database_snapshot.txt");

    // Insert new records
    db.enqueue_task({OperationType::INSERT, 1, "Alice"});
    db.enqueue_task({OperationType::INSERT, 2, "Bob"});
    db.enqueue_task({OperationType::INSERT, 3, "Charlie"});

    // Print current records
    db.enqueue_task({OperationType::PRINT, 0, ""});

    // Save snapshot before exiting
    db.save_snapshot("database_snapshot.txt");

    return 0;
}

Note: Text based snapshots are easily readable, editable, and help verify your code is running correctly. You can always switch to a binary format for some final testing.

Perhaps now consider Boost.Filesystem for file management, and for a heavier duty database engine - integrate Boost.Asio to handle remote database transactions.

The Boost libraries have a lot to offer this particular scenario!