RDB is the ultimate Object Relational Mapper for Node.js, offering seamless integration with popular databases like Postgres, MS SQL, MySQL, Sybase SAP, and SQLite. Whether you're building applications in TypeScript or JavaScript (including both CommonJS and ECMAScript), RDB has got you covered.

Key Features

• Rich Querying Model: RDB provides a powerful and intuitive querying model, making it easy to retrieve, filter, and manipulate data from your databases.
• Concise API: With a concise and developer-friendly API, RDB enables you to interact with your database using simple and expressive syntax.
• No Code Generation Required: Enjoy full IntelliSense, even in table mappings, without the need for cumbersome code generation.
• TypeScript and JavaScript Support: RDB fully supports both TypeScript and JavaScript, allowing you to leverage the benefits of static typing and modern ECMAScript features.
• Works in the Browser: RDB even works in the browser through hosting in Express, giving you the flexibility to build web applications with ease.

This is the Modern Typescript Documentation. Are you looking for the Classic Documentation ?

Installation

$ npm install rdb

Example

Here we choose SQLite.

$ npm install sqlite3

_{📄 db.js}

import rdb from 'rdb';

const map = rdb.map(x => ({
  customer: x.table('customer').map(({ column }) => ({
    id: column('id').numeric().primary().notNullExceptInsert(),
    name: column('name').string(),
    balance: column('balance').numeric(),
    isActive: column('isActive').boolean(),
  })),

  order: x.table('_order').map(({ column }) => ({
    id: column('id').numeric().primary().notNullExceptInsert(),
    orderDate: column('orderDate').date().notNull(),
    customerId: column('customerId').numeric().notNullExceptInsert(),
  })),

  orderLine: x.table('orderLine').map(({ column }) => ({
    id: column('id').numeric().primary().notNullExceptInsert(),
    orderId: column('orderId').numeric(),
    product: column('product').string(),
  })),

  deliveryAddress: x.table('deliveryAddress').map(({ column }) => ({
    id: column('id').numeric().primary().notNullExceptInsert(),
    orderId: column('orderId').numeric(),
    name: column('name').string(),
    street: column('street').string(),
    postalCode: column('postalCode').string(),
    postalPlace: column('postalPlace').string(),
    countryCode: column('countryCode').string(),
  }))

})).map(x => ({
  order: x.order.map(v => ({
    customer: v.references(x.customer).by('customerId'),
    lines: v.hasMany(x.orderLine).by('orderId'),
    deliveryAddress: hasOne(x.deliveryAddress).by('orderId'),
  }))
}));

export default map.sqlite('demo.db');

_{📄 update.js}

import db from './db';

updateRow();

async function updateRow() {
  const order = await db.order.getById(2, {
    lines: true
  });
  order.lines.push({
    product: 'broomstick'
  });

  await order.saveChanges();
}

API

Mapping tables

To define a mapping, you employ the map() method, linking your tables and columns to corresponding object properties. You provide a callback function that engages with a parameter representing a database table.

Each column within your database table is designated by using the column() method, in which you specify its name. This action generates a reference to a column object that enables you to articulate further column properties like its data type or if it serves as a primary key.

Relationships between tables can also be outlined. By using methods like hasOne, hasMany, and references, you can establish connections that reflect the relationships in your data schema. In the example below, an 'order' is linked to a 'customer' reference, a 'deliveryAddress', and multiple 'lines'. The hasMany and hasOne relations represents ownership - the tables 'deliveryAddress' and 'orderLine' are owned by the 'order' table, and therefore, they contain the 'orderId' column referring to their parent table, which is 'order'. Conversely, the customer table is independent and can exist without any knowledge of the 'order' table. Therefore we say that the order table references the customer table - necessitating the existence of a 'customerId' column in the 'order' table.

_{📄 db.js}

import rdb from 'rdb';

const map = rdb.map(x => ({
  customer: x.table('customer').map(({ column }) => ({
    id: column('id').numeric().primary().notNullExceptInsert(),
    name: column('name').string(),
    balance: column('balance').numeric(),
    isActive: column('isActive').boolean(),
  })),

  order: x.table('_order').map(({ column }) => ({
    id: column('id').numeric().primary().notNullExceptInsert(),
    orderDate: column('orderDate').date().notNull(),
    customerId: column('customerId').numeric().notNullExceptInsert(),
  })),

  orderLine: x.table('orderLine').map(({ column }) => ({
    id: column('id').numeric().primary().notNullExceptInsert(),
    orderId: column('orderId').numeric(),
    product: column('product').string(),
  })),

  deliveryAddress: x.table('deliveryAddress').map(({ column }) => ({
    id: column('id').numeric().primary().notNullExceptInsert(),
    orderId: column('orderId').numeric(),
    name: column('name').string(),
    street: column('street').string(),
    postalCode: column('postalCode').string(),
    postalPlace: column('postalPlace').string(),
    countryCode: column('countryCode').string(),
  }))

})).map(x => ({
  order: x.order.map(({ hasOne, hasMany, references }) => ({
    customer: references(x.customer).by('customerId'),
    deliveryAddress: hasOne(x.deliveryAddress).by('orderId'),
    lines: hasMany(x.orderLine).by('orderId')
  }))
}));

export default map.sqlite('demo.db');

The init.js script resets our SQLite database. It's worth noting that SQLite databases are represented as single files, which makes them wonderfully straightforward to manage.

At the start of the script, we import our database mapping from the db.js file. This gives us access to the db object, which we'll use to interact with our SQLite database.

Then, we define a SQL string. This string outlines the structure of our SQLite database. It first specifies to drop existing tables named 'deliveryAddress', 'orderLine', '_order', and 'customer' if they exist. This ensures we have a clean slate. Then, it dictates how to create these tables anew with the necessary columns and constraints.

Because of a peculiarity in SQLite, which only allows one statement execution at a time, we split this SQL string into separate statements. We do this using the split() method, which breaks up the string at every semicolon.

_{📄 init.js}

import db from './db';

const sql = `DROP TABLE IF EXISTS deliveryAddress; DROP TABLE IF EXISTS orderLine; DROP TABLE IF EXISTS _order; DROP TABLE IF EXISTS customer;
CREATE TABLE customer (
    id INTEGER PRIMARY KEY,
    name TEXT,
    balance NUMERIC,
    isActive INTEGER
);

CREATE TABLE _order (
    id INTEGER PRIMARY KEY,
    orderDate TEXT,
    customerId INTEGER REFERENCES customer
);

CREATE TABLE orderLine (
    id INTEGER PRIMARY KEY,
    orderId INTEGER REFERENCES _order,
    product TEXT
);

CREATE TABLE deliveryAddress (
    id INTEGER PRIMARY KEY,
    orderId INTEGER REFERENCES _order,
    name TEXT, 
    street TEXT,
    postalCode TEXT,
    postalPlace TEXT,
    countryCode TEXT
)
`;


async function init() {
  const statements = sql.split(';');
  for (let i = 0; i < statements.length; i++) {
    await db.query(statements[i]);
  }
}
export default init;

In SQLite, columns with the INTEGER PRIMARY KEY attribute are designed to autoincrement by default. This means that each time a new record is inserted into the table, SQLite automatically produces a numeric key for the id column that is one greater than the largest existing key. This mechanism is particularly handy when you want to create unique identifiers for your table rows without manually entering each id.

Connecting

SQLite

$ npm install sqlite3

import map from './db';
const db = map.sqlite('demo.db');

With connection pool

$ npm install sqlite3

import map from './db';
const db = map.sqlite('demo.db', { size: 10 });

MySQL

$ npm install mysql2

import map from './db';
const db = map.mysql('mysql://test:test@mysql/test');

MS SQL

$ npm install tedious

import map from './db';
const db = map.mssql({
          server: 'mssql',
          options: {
            encrypt: false,
            database: 'test'
          },
          authentication: {
            type: 'default',
            options: {
              userName: 'sa',
              password: 'P@assword123',
            }
          }
        });

PostgreSQL

$ npm install pg

import map from './db';
const db = map.pg('postgres://postgres:postgres@postgres/postgres');

SAP Adaptive Server

$ npm install msnodesqlv8

import { fileURLToPath } from 'url';
import { dirname } from 'path';
import map from './db';

const __filename = fileURLToPath(import.meta.url);
const __dirname = dirname(__filename);
//download odbc driver from sap web pages
const db = map.pg(`Driver=${__dirname}/libsybdrvodb.so;SERVER=sapase;Port=5000;UID=sa;PWD=sybase;DATABASE=test`);

Inserting rows

In the code below, we initially import the table-mapping feature "db.js" and the setup script "init.js", both of which were defined in the preceding step. The setup script executes a raw query that creates the necessary tables. Subsequently, we insert two customers, named "George" and "Harry", into the customer table, and this is achieved through calling "db.customer.insert".

Next, we insert an array of two orders in the order table. Each order contains an orderDate, customer information, deliveryAddress, and lines for the order items. We use the customer constants "george" and "harry" from previous inserts. Observe that we don't pass in any primary keys. This is because all tables here have autoincremental keys. The second argument to "db.order.insert" specifies a fetching strategy. This fetching strategy plays a critical role in determining the depth of the data retrieved from the database after insertion. The fetching strategy specifies which associated data should be retrieved and included in the resulting orders object. In this case, the fetching strategy instructs the database to retrieve the customer, deliveryAddress, and lines for each order.

Without a fetching strategy, "db.order.insert" would only return the root level of each order. In that case you would only get the id, orderDate, and customerId for each order.

import db from './db';
import init from './init';

insertRows();

async function insertRows() {
  await init();

  const george = await db.customer.insert({
    name: 'George',
    balance: 177,
    isActive: true
  });

  const harry = await db.customer.insert({
    name: 'Harry',
    balance: 200,
    isActive: true
  });

  const orders = await db.order.insert([
    {
      orderDate: new Date(2022, 0, 11, 9, 24, 47),
      customer: george,
      deliveryAddress: {
        name: 'George',
        street: 'Node street 1',
        postalCode: '7059',
        postalPlace: 'Jakobsli',
        countryCode: 'NO'
      },
      lines: [
        { product: 'Bicycle' },
        { product: 'Small guitar' }
      ]
    },
    {
      customer: harry,
      orderDate: new Date(2021, 0, 11, 12, 22, 45),
      deliveryAddress: {
        name: 'Harry Potter',
        street: '4 Privet Drive, Little Whinging',
        postalCode: 'GU4',
        postalPlace: 'Surrey',
        countryCode: 'UK'
      },
      lines: [
        { product: 'Magic wand' }
      ]
    }
  ], {customer: true, deliveryAddress: true, lines: true}); //fetching strategy
  console.dir(orders, {depth: Infinity});
}

Fetching rows

RDB has a rich querying model. As you navigate through, you'll learn about the various methods available to retrieve data from your tables, whether you want to fetch all rows, many rows with specific criteria, or a single row based on a primary key. If you want do dig even deeper into filtering possibilities, there is a whole section about it further down the page.

The fetching strategy in RDB is optional, and its use is influenced by your specific needs. You can define the fetching strategy either on the table level or the column level. This granularity gives you the freedom to decide how much related data you want to pull along with your primary request.

All rows

import db from './db';

getRows();

async function getRows() {
  const orders = await db.order.getAll({
    customer: true, 
    deliveryAddress: true, 
    lines: true
  });
  console.dir(orders, {depth: Infinity});
}

Limit, offset and order by
This script demonstrates how to fetch orders with customer, lines and deliveryAddress, limiting the results to 10, skipping the first row, and sorting the data based on the orderDate in descending order followed by id. The lines are sorted by product.

import db from './db';

getRows();

async function getRows() {
  const orders = await db.order.getAll({
    offset: 1,
    orderBy: ['orderDate desc', 'id'],
    limit: 10,
    customer: true, 
    deliveryAddress: true, 
    lines: {
      orderBy: 'product'
    },
  });
  console.dir(orders, {depth: Infinity});
}

Many rows filtered

import db from './db';

getRows();

async function getRows() {
  const filter = db.order.lines.any(line => line.product.contains('i'))
                 .and(db.order.customer.balance.greaterThan(180));
  const orders = await db.order.getMany(filter, {
    customer: true, 
    deliveryAddress: true, 
    lines: true
  });
  console.dir(orders, {depth: Infinity});
}

Single row filtered

import db from './db';

getRows();

async function getRows() {
  const filter = db.order.customer(customer => customer.isActive.eq(true)
                 .and(customer.startsWith('Harr')));
                 //equivalent, but creates slighly different sql:
                 // const filter = db.order.customer.isActive.eq(true).and(db.order.customer.startsWith('Harr'));
  const order = await db.order.getOne(filter, {
    customer: true, 
    deliveryAddress: true, 
    lines: true
  });
  console.dir(order, {depth: Infinity});
}

Single row by primary key

import db from './db';

getRows();

async function getRows() {
  const order = await db.order.getById(1, {
    customer: true, 
    deliveryAddress: true, 
    lines: true
  });
  console.dir(order, {depth: Infinity});
}

Many rows by primary key

import db from './db';

getRows();

async function getRows() {
  const orders = await db.order.getMany([
      {id: 1},
      {id: 2}
    ], 
    {
      customer: true, 
      deliveryAddress: true, 
      lines: true
  });
  console.dir(orders, {depth: Infinity});
}

Updating rows

To update rows, modify the property values and invoke the method saveChanges(). The function updates only the modified columns, not the entire row. Rows in child relations can also be updated as long as the parent order owns the child tables. In our illustration, the order table owns both the deliveryAddress and the lines tables because they're part of a hasOne/hasMany relationship. Contrastingly, the customer is part of a reference relationship and thus can't be updated here. But you can detach the reference to the customer by assigning it to null or undefined. (Setting order.customerId to null or undefined achieves the same result.)

Updating a single row

import db from './db';

update();

async function update() {
  const order = await db.order.getById(1, {
    customer: true, 
    deliveryAddress: true, 
    lines: true
  });

  order.orderDate = new Date();
  order.deliveryAddress = null;
  order.lines.push({product: 'Cloak of invisibility'});

  await order.saveChanges();
  console.dir(order, {depth: Infinity});
}

Updating many rows

import db from './db';

update();

async function update() {
  let orders = await db.order.getAll({
    orderBy: 'id',
    lines: true, 
    deliveryAddress: true, 
    customer: true
  });

  orders[0].orderDate = new Date();
  orders[0].deliveryAddress.street = 'Node street 2';
  orders[0].lines[1].product = 'Big guitar';

  orders[1].orderDate = '2023-07-14T12:00:00'; //iso-string is allowed
  orders[1].deliveryAddress = null;
  orders[1].customer = null;
  orders[1].lines.push({product: 'Cloak of invisibility'});

  await orders.saveChanges();
  console.dir(orders, {depth: Infinity});
}

Updating with concurrency
Rows get updated using an optimistic concurrency approach by default. This means if a property being edited was meanwhile altered, an exception is raised, indicating the row was modified by a different user. You can change the concurrency strategy either at the table or column level.

Currently, there are three concurrency strategies:

• optimistic: Raises an exception if another user changes the property during an update.
• overwrite: Overwrites the property, regardless of changes by others.
• skipOnConflict: Silently avoids updating the property if another user has modified it in the interim.

In the example below, we've set the concurrency strategy for orderDate to 'overwrite'. This implies that if other users modify orderDate while you're making changes, their updates will be overwritten.

import db from './db';

update();

async function update() {
  const order = await db.order.getById(1, {
    customer: true, 
    deliveryAddress: true, 
    lines: true
  });

  order.orderDate = new Date();
  order.deliveryAddress = null;
  order.lines.push({product: 'Cloak of invisibility'});

  await order.saveChanges( {
    orderDate: {
      concurrency: 'overwrite'
  }});
  console.dir(order, {depth: Infinity});
}

Deleting rows

Rows in owner tables cascade deletes to their child tables. In essence, if a table has ownership over other tables through hasOne and hasMany relationships, removing a record from the parent table also removes its corresponding records in its child tables. This approach safeguards against leaving orphaned records and upholds data integrity. On the contrary, tables that are merely referenced, through reference relationships , remain unaffected upon deletions. For a deeper dive into these relationships and behaviors, refer to the section on table mapping.

Deleting a single row

import db from './db';

deleteRow();

async function deleteRow() {    
  const order = await db.order.getById(1);

  await order.delete();
  //will also delete deliveryAddress and lines
  //but not customer
}

Deleting many rows

import db from './db';

deleteRows();

async function deleteRows() {
  const filter = db.order.customer.name.eq('George');
  let orders = await db.order.getMany(filter);

  await orders.delete();
}

Deleting with concurrency

Concurrent operations can lead to conflicts. When you still want to proceed with the deletion regardless of potential interim changes, the 'overwrite' concurrency strategy can be used. This example demonstrates deleting rows even if the "delivery address" has been modified in the meantime. You can read more about concurrency strategies in the 'Updating rows' section.

import db from './db';

deleteRows();

async function deleteRows() {
  const filter = db.order.deliveryAddress.name.eq('George');
  let orders = await db.order.getMany(filter, {
    customer: true, 
    deliveryAddress: true, 
    lines: true
  });

  await orders.delete({
    deliveryAddress: {
      concurrency: 'overwrite'
    }
  });
}

Batch delete

When removing a large number of records based on a certain condition, batch deletion can be efficient.

However, it's worth noting that batch deletes don't follow the cascade delete behavior by default. To achieve cascading in batch deletes, you must explicitly call the deleteCascade method.

import db from './db';

deleteRows();

async function deleteRows() {
  const filter = db.order.deliveryAddress.name.eq('George');
  await db.order.delete(filter);
}

Batch delete cascade

When deleting records, sometimes associated data in related tables also needs to be removed. This cascade delete helps maintain database integrity.

import db from './db';

deleteRows();

async function deleteRows() {
  const filter = db.order.deliveryAddress.name.eq('George');
  await db.order.deleteCascade(filter);
}

Batch delete by primary key

For efficiency, you can also delete records directly if you know their primary keys.

import db from './db';

deleteRows();

async function deleteRows() {
  db.customer.delete([{id: 1}, {id: 2}]);
}

In the browser

You can use RDB directly in the browser using the adapter for the Express.js framework. Instead of sending raw SQL queries from the client to the server, this approach records the method calls in the client. These method calls are then replayed at the server, ensuring a higher level of security by not exposing raw SQL on the client side. Raw sql queries and raw sql filters are disabled at the http client due to security reasons. If you would like RDB to support other web frameworks, like nestJs, fastify, etc, please let me know.

_{📄 server.js}

import db from './db';
import { json } from 'body-parser';
import express from 'express';

express().disable('x-powered-by')
  .use(json({ limit: '100mb' }))
  .use('/rdb', db.express())
  .listen(3000, () => console.log('Example app listening on port 3000!'));

_{📄 browser.js}

import map from './db';

updateRows();

async function updateRows() {
  const db = map.http('http://localhost:3000/rdb');
  const filter = db.order.lines.any(
    line => line.product.startsWith('Magic wand'))
    .and(db.order.customer.name.startsWith('Harry')
  );

  const order = await db.order.getOne(filter, {
    lines: true
  });
  
  order.lines.push({
    product: 'broomstick'
  });

  await order.saveChanges();
}

Interceptors and base filter

In the next setup, axios interceptors are employed on the client side to add an Authorization header of requests. Meanwhile, on the server side, an Express.js middleware (validateToken) is utilized to ensure the presence of the Authorization header, while a base filter is applied on the order table to filter incoming requests based on the customerId extracted from this header. This combined approach enhances security by ensuring that users can only access data relevant to their authorization level and that every request is accompanied by a token. In real-world applications, it's advisable to use a more comprehensive token system and expand error handling to manage a wider range of potential issues.
One notable side effect compared to the previous example, is that only the order table is exposed for interaction, while all other potential tables in the database remain shielded from direct client access (except for related tables).

_{📄 server.js}

import db from './db';
import { json } from 'body-parser';
import express from 'express';

express().disable('x-powered-by')
  .use(json({ limit: '100mb' }))
  .use('/rdb', validateToken)
  .use('/rdb', db.express({
    order: {
      baseFilter: (db, req, _res) => {
        const customerId = Number.parseInt(req.headers.authorization.split(' ')[1]); //Bearer 2
        return db.order.customerId.eq(Number.parseInt(customerId));
      }
    }
  }))
  .listen(3000, () => console.log('Example app listening on port 3000!'));

function validateToken(req, res, next) {
  // For demo purposes, we're just checking against existence of authorization header
  // In a real-world scenario, this would be a dangerous approach because it bypasses signature validation
  const authHeader = req.headers.authorization;
  if (authHeader)
    return next();
  else
    return res.status(401).json({ error: 'Authorization header missing' });
}

_{📄 browser.js}

import map from './db';

updateRows();

async function updateRows() {
  const db = map.http('http://localhost:3000/rdb');
  
  db.interceptors.request.use((config) => {
    // For demo purposes, we're just adding hardcoded token
    // In a real-world scenario, use a proper JSON web token
    config.headers.Authorization = 'Bearer 2' //customerId
    return config;
  });

  db.interceptors.response.use(
    response => response, 
    (error) => {
      if (error.response && error.response.status === 401) {
        console.dir('Unauthorized, dispatch a login action');
        //redirectToLogin();
      }
      return Promise.reject(error);
    }
  );

  const filter = db.order.lines.any(
    line => line.product.startsWith('Magic wand'))
    .and(db.order.customer.name.startsWith('Harry')
  );

  const order = await db.order.getOne(filter, {
    lines: true
  });
  
  order.lines.push({
    product: 'broomstick'
  });

  await order.saveChanges();

}

Transactions

We initiate a database transaction using db.transaction. Within the transaction, a customer is retrieved and its balance updated using the tx object to ensure operations are transactional. An error is deliberately thrown to demonstrate a rollback, ensuring all previous changes within the transaction are reverted. Always use the provided tx object for operations within the transaction to maintain data integrity.

import db from './db';

execute();

async function execute() {
  await db.transaction(async tx => {
    const customer = await tx.customer.getById(1);
      customer.balance = 100;
      await customer.saveChanges();
      throw new Error('This will rollback');
  });
}

Data types

RDB is database agnostic - meaning it can work with multiple database systems without being specifically tied to any one of them. When the ORM behaves consistently across various databases, developers don't need to remember specific quirks or differences when switching between databases. They can rely on the ORM to provide the same mapping behavior, which reduces the cognitive load and potential for errors. There are currently 8 column types in RDB:

• string maps to VARCHAR or TEXT in sql
• numeric maps to INTEGER, DECIMAL, NUMERIC, TINYINT FLOAT/REAL or DOUBLE in sql.
• boolean maps to BIT, TINYINT(1) or INTEGER in sql.
• uuid is represented as string in javascript and maps to UUID, GUID or VARCHAR in sql.
• date is represented as ISO 8601 string in javascript and maps to DATE, DATETIME, TIMPESTAMP or DAY in sql. Representing datetime values as ISO 8601 strings, rather than relying on JavaScript's native Date object, has multiple advantages, especially when dealing with databases and servers in different time zones. The datetime values are inherently accompanied by their respective time zones. This ensures that the datetime value remains consistent regardless of where it's being viewed or interpreted. On the other hand, JavaScript's Date object is typically tied to the time zone of the environment in which it's executed, which could lead to inconsistencies between the client and the database server.
• dateWithTimeZone is represented as ISO 8601 string in javascript and maps to TIMESTAMP WITH TIME ZONE in postgres and DATETIMEOFFSET in ms sql.
  Contrary to what its name might imply, timestamptz (TIMESTAMP WITH TIME ZONE) in postgres doesn't store the time zone data. Instead, it adjusts the provided time value to UTC (Coordinated Universal Time) before storing it. When a timestamptz value is retrieved, PostgreSQL will automatically adjust the date-time to the time zone setting of the PostgreSQL session (often the server's timezone, unless changed by the user). The primary benefit of DATETIMEOFFSET in ms sql is its ability to keep track of the time zone context. If you're dealing with global applications where understanding the original time zone context is critical (like for coordinating meetings across time zones or logging events), DATETIMEOFFSET is incredibly valuable.
• binary is represented as a base64 string in javascript and maps to BLOB, BYTEA or VARBINARY(max) in sql.
• json and jsonOf<T> are represented as an object or array in javascript and maps to JSON, JSONB, NVARCHAR(max) or TEXT (sqlite) in sql.

_{📄 db.js}

import rdb from 'rdb';

/**
 * @typedef {Object} Pet
 * @property {string} name - The name of the pet.
 * @property {string} kind - The kind of pet
 */

/** @type {Pet} */
let pet;

const map = rdb.map(x => ({
    demo: x.table('demo').map(x => ({
      id: x.column('id').uuid().primary().notNull(),
      name: x.column('name').string(),
      balance: x.column('balance').numeric(),
      regularDate: x.column('regularDate').date(),
      tzDate: x.column('tzDate').dateWithTimeZone(),
      picture: x.column('picture').binary(),
      pet: x.column('pet').jsonOf(pet), //generic
      pet2: x.column('pet2').json(), //non-generic
  }))
}));

_{📄 db.ts}

import rdb from 'rdb';

interface Pet {
    name: string;
    kind: string;
}

const map = rdb.map(x => ({
    demo: x.table('demo').map(x => ({
      id: x.column('id').uuid().primary().notNull(),
      name: x.column('name').string(),
      balance: x.column('balance').numeric(),
      regularDate: x.column('regularDate').date(),
      tzDate: x.column('tzDate').dateWithTimeZone(),
      picture: x.column('picture').binary(),
      pet: x.column('pet').jsonOf<Pet>(), //generic
      pet2: x.column('pet2').json(), //non-generic
  }))
}));

Validation

In the previous sections you have already seen the notNull() validator being used on some columns. This will not only generate correct typescript mapping, but also throw an error if value is set to null or undefined. However, sometimes we do not want the notNull-validator to be run on inserts. Typically, when we have an autoincremental key or server generated uuid, it does not make sense to check for null on insert. This is where notNullExceptInsert() comes to rescue. You can also create your own custom validator as shown below. The last kind of validator, is the ajv JSON schema validator. This can be used on json columns as well as any other column type.

_{📄 db.js}

import rdb from 'rdb';

/**
 * @typedef {Object} Pet
 * @property {string} name - The name of the pet.
 * @property {string} kind - The kind of pet
 */

/** @type {Pet} */
let pet;

let petSchema = {
    "properties": {
        "name": { "type": "string" },
        "kind": { "type": "string" }
    }
};

function validateName(name?: string) {
	if (value && value.length > 10)
		throw new Error('Length cannot exceed 10 characters');
}

const map = rdb.map(x => ({
    demo: x.table('demo').map(x => ({
      id: x.column('id').uuid().primary().notNullExceptInsert(),
      name: x.column('name').string().validate(validateName),
      pet: x.column('pet').jsonOf(pet).JSONSchema(petSchema)
  }))
}));

_{📄 db.ts}

import rdb from 'rdb';

interface Pet {
    name: string;
    kind: string;
}

let petSchema = {
    "properties": {
        "name": { "type": "string" },
        "kind": { "type": "string" }
    }
};

function validateName(value) {
	if (value && value.length > 10)
		throw new Error('Length cannot exceed 10 characters');
}

const map = rdb.map(x => ({
    demo: x.table('demo').map(x => ({
      id: x.column('id').uuid().primary().notNullExceptInsert(),
      name: x.column('name').string().validate(validateName),
      pet: x.column('pet').jsonOf<Pet>().JSONSchema(petSchema)
  }))
}));

What it is not

• It is not about migrations

  The allure of ORMs handling SQL migrations is undeniably attractive and sweet. However, this sweetness can become painful. Auto-generated migration scripts might not capture all nuances. Using dedicated migration tools separate from the ORM or manually managing migrations might be the less painful route in the long run. RDB aim for database agnosticism. And when you're dealing with migrations, you might want to use features specific to a database platform. However, I might consider adding support for (non-auto-generated) migrations at a later point. But for now, it is not on the roadmap.

• It is not about NoSql databases

  Applying ORMs to NoSQL, which inherently diverges from the relational model, can lead to data representation mismatches and a loss of specialized NoSQL features. Moreover, the added ORM layer can introduce performance inefficiencies, complicate debugging, and increase maintenance concerns. Given the unique capabilities of each NoSQL system, crafting custom data access solutions tailored to specific needs often provides better results than a generalized ORM approach.

• It is not about GrapQL

  RDB, already supports remote data operations via HTTP, eliminating the primary need for integrating GraphQL. RDB's built-in safety mechanisms and tailored optimization layers ensure secure and efficient data operations, which might be compromised by adding GraphQL. Furthermore, RDB's inherent expressivity and powerful querying capabilities could be overshadowed by the introduction of GraphQL. Integrating GraphQL could introduce unnecessary complexity, potential performance overhead, and maintenance challenges, especially as both systems continue to evolve. Therefore, considering RDB's robust features and design, supporting GraphQL might not offer sufficient advantages to warrant the associated complications.

Changelog

Code of Conduct