Lightweight is a thin and modern C++ ODBC wrapper for easy and fast raw database access. Documentation is available at https://lastrada-software.github.io/Lightweight/.

It supports both low-level access to the SQL API as well as provides hight level abstraction that allow easy database access.

Here you can see an example of datamapper usage (our tool for the high level abstraction)

Supported platforms

Only ODBC is supported, so it should work on any platform that has an ODBC driver and a modern enough C++ compiler.

Windows (Visual Studio 2022, toolkit v143)
Linux (GCC 14, Clang 19)

Supported databases

Microsoft SQL
PostgreSQL
SQLite3
Oracle database (work in progress)

Namespace

All functionality is placed inside a Lightweight namespace, we also provide an alias for this namespace Light, that is slightly shorter.

High level API

High level API of the library provided by the type DataMapper

Simple one record example

Example of its usage to save/load/update/delete entry in the database for one table

#include <Lightweight/Lightweight.hpp>
 
// Define a person structure, mapping to a table from the database
struct Person
{
    Field<SqlGuid, PrimaryKey::AutoAssign> id;
    Field<SqlAnsiString<25>> name;
    Field<bool> is_active { true };
    Field<std::optional<int>> age;
};
 
void CRUD(DataMapper& dm)
{
    // Creates the table if it does not exist
    dm.CreateTable<Person>();
 
    // Create a new person and create a database entry 
    auto person = Person {.name = "John Doe", .is_active = true, .age = 24};
    dm.Create(person);
 
    // Update the age and save to the database
    person.age = 25;
    dm.Update(person);
 
    // Query the person by primary key
    if (auto const po = dm.Query<Person>(person.id); po)
        std::println("Person: {} ({})", po->name, DataMapper::Inspect(*po));
 
    // Query all persons
    std::vector<Person> const persons = dm.Query<Person>().All(); 
    
    // Query all persons with some filter and order by name
    auto const records = dm.Query<Person>()
                             .Where(FieldNameOf<&Person::is_active>, "=", true)
                             .OrderBy(FieldNameOf<&Person::name>)
                             .All();
 
    // Delete the person
    dm.Delete(person);
}

Foreign keys relation

Now consider the following example we have two tables User and Email, with foreign key in Email pointing to the User this will translate in the following structs

struct User
{
    Light::Field<Light::SqlGuid, Light::PrimaryKey::AutoAssign, SqlRealName { "user_id" }> id {};
    Light::Field<Light::SqlAnsiString<30>> name {};
};
 
struct Email
{
    Light::Field<Light::SqlGuid, Light::PrimaryKey::AutoAssign> id {};
    Light::Field<Light::SqlAnsiString<30>> address {};
    Light::BelongsTo<&User::id, Light::SqlRealName { "user_id" }> user {};
};

In the presented example we used rename of the columns, for more details see how-to#rename-column-name page. you can query the email and get access to the user record as well

auto dm = Light::DataMapper{};
auto email = dm.QuerySingle<Email>(some_email_id).value_or(Email{});
auto user_name = email.user->name; // lazily loads the user record

Mapping query results to a simple struct

If you have a SQL query that returns some values, but it does not corresponds to the existing table in the database, you can map the result to a simple struct. The struct must have fields that match the columns in the query. The fields can be of any type that can be converted from the column type. The struct can have more fields than the columns in the query, but the fields that match the columns must be in the same order as the columns in the query.

#include <Lightweight/Lightweight.hpp>
 
struct SimpleStruct
{
    uint64_t pkFromA;
    uint64_t pkFromB;
    SqlAnsiString<30> c1FromA;
    SqlAnsiString<30> c2FromA;
    SqlAnsiString<30> c1FromB;
    SqlAnsiString<30> c2FromB;
};
 
void SimpleStructExample(DataMapper& dm)
{
    if (auto maybeObject = dm.Query<SimpleString>(
        "SELECT A.pk, B.pk, A.c1, A.c2, B.c1, B.c2 FROM A LEFT JOIN B ON A.pk = B.pk"); maybeObject)
    ))
    {
        for (auto const& obj : *maybeObject)
            std::println("{}", DataMapper::Inspect(obj));
    }
}

Mapping query to multiple struct

We also provide an API to create SQL queries, this can be usefull if you want to use information from existing structures. The following example shows how to create a query that joins multiple tables and maps the result to multiple structs. Consider the following structs

struct CustomBindingA
{
    Field<uint64_t, PrimaryKey::ServerSideAutoIncrement> id {};
    Field<int> number {};
    Field<SqlAnsiString<20>> name {};
    Field<SqlDynamicWideString<1000>> description {};
};
 
struct CustomBindingB
{
    Field<uint64_t, PrimaryKey::ServerSideAutoIncrement> id {};
    Field<SqlAnsiString<20>> title {};
    Field<SqlDateTime> date_time {};
    Field<uint64_t> a_id {};
    Field<uint64_t> c_id {};
};
 
struct CustomBindingC
{
    Field<uint64_t, PrimaryKey::ServerSideAutoIncrement> id {};
    Field<double> value {};
    Field<SqlAnsiString<20>> comment {};
};

Create a query to join those tables to get in a single query

auto dm = DataMapper {};
auto query = dm.FromTable(RecordTableName<CustomBindingA>)
                 .Select()
                 .Fields<CustomBindingA, CustomBindingB>()
                 .Field(QualifiedColumnName<"C.id">)
                 .Field(QualifiedColumnName<"C.comment">)
                 .InnerJoin<&CustomBindingB::a_id, &CustomBindingA::id>()
                 .InnerJoin<&CustomBindingC::id, &CustomBindingB::c_id>()
                 .OrderBy(QualifiedColumnName<"A.id">)
                 .All();

This create the following SQL query

SELECT "A"."id", "A"."number", "A"."name", "A"."description", "B"."id", "B"."title", "B"."date_time", "B"."a_id", "B"."c_id", ""C"."id"", "C"."comment" FROM "A"
 INNER JOIN "B" ON "B"."a_id" = "A"."id"
 INNER JOIN "C" ON "C"."id" = "B"."c_id"
 ORDER BY "A"."id" ASC

Now you can execute it and get the result as a std::vector<std::tuple<CustomBindingA, CustomBindingB, ParfOfC> like this

struct PartOfC
{
    uint64_t id {};
    SqlAnsiString<20> comment {};
};
 
auto const records = dm.QueryToTuple<CustomBindingA, CustomBindingB, PartOfC>(query);
for (auto const& [a, b, c]: records)
{
    // ...
}

Using SQLite for testing on Windows operating system

You need to have the SQLite3 ODBC driver for SQLite installed.

ODBC driver download URL: http://www.ch-werner.de/sqliteodbc/
Example connection string: "DRIVER={SQLite3 ODBC Driver};Database=file::memory:"

SQLite ODBC driver installation on other operating systems

# Fedora Linux
sudo dnf install sqliteodbc
 
# Ubuntu Linux
sudo apt install sqliteodbc
 
# macOS
arch -arm64 brew install sqliteodbc

sqliteODBC Documentation: http://www.ch-werner.de/sqliteodbc/html/index.html
Example connection string: "DRIVER=SQLite3;Database=file::memory:"

Generate example for the existing database

You can use ddl2cpp to generate header file for you database schema as well as an example file that you can compile

First, configure cmake project and compile ddl2cpp target

sh

cmake --build build --target ddl2cpp

Generate header file from the existing database by providing connection string to the tool

sh

./build/src/tools/ddl2cpp --connection-string "DRIVER=SQLite3;Database=test.db" --make-aliases --naming-convention CamelCase --output ./src/examples/example.hpp --generate-example

You can also avoid all those command line arguments by creating a config file that muts be in your current working directory or in one of its parent directories. The config file must be named ddl2cpp.yml and must contain the following content:

ConnectionString: 'DSN=YourDSN;UID=YourUser;PWD=YourSecret'
OutputDirectory: 'src/entities'
MakeAliases: true
NamingConvention: CamelCase

Now you can configure cmake to compile example

sh

cmake --preset linux-clang-debug -DLIGHWEIGHT_EXAMPLE=ON -B build

Finally, compile and run the example

sh

cmake --build build && ./build/src/examples/example

Compile using C++26 reflection support

sh

docker buildx build --progress=plain -f .github/DockerReflection --load .