Editor’s note: This post was updated 5 October 2022 to include information about using Sequelize, Knex, and other ORMs with Node.js, when to use an ORM, and the “best” ORM to use for Node.js. The article was also updated to remove outdated information and make general edits.
In a nutshell, ORM refers to object-relational mapping. And this means we map relational sequel objects — foreign keys and primary keys to actual objects.
The result of this is that ORM creates a structure for CRUD operations in our database. Consequently, it also provides a layer of abstraction over the SQL statement by allowing developers to operate on the object instead of going through the hassle of writing SQL queries. Thus, ORM improves readability, documentation, and the speed of development.
However, in this tutorial, we’ll consider three reasons why you should avoid using ORM in your project. While the concepts discussed are applicable to every language and platform, the code examples will be written in Node.js-flavored JavaScript, and we will be considering packages obtained from the npm repository.
I don’t intend to diss any of the modules mentioned in this post. A lot of hard work has gone into each and every one of them. They are used by production applications all around the world which merrily respond to plenty of requests every day. I’ve also deployed applications using ORMs and regret nothing.
ORM is the process of mapping between objects and relational database systems. Different database systems access data in myriad ways, and ORM helps you maintain objects even when the sources and apps they access change over time.
ORMs are commonly used to streamline the migration of data between databases.
Before we get to the reasons why you shouldn’t use ORM with Node.js, let’s list some benefits. When used properly, ORM in Node.js enables you to:
ORMs are powerful tools. The ORMs we’ll be examining in this post are able to communicate with SQL backends such as SQLite, PostgreSQL, MySQL, and MSSQL. The examples in this post will make use of PostgreSQL, which is a very powerful open source SQL server.
There are ORMs capable of communicating with NoSQL backends, such as the Mongoose ORM backed by MongoDB, but we won’t be considering those in this post.
First, run the following commands to start an instance of PostgreSQL locally. It will be configured in such a way that requests made to the default PostgreSQL port on localhost:5432 will be forwarded to the container. It’ll also write the files to disk in your home directory so that subsequent instantiations will retain the data we’re already created:
mkdir -p ~/data/pg-node-orms docker run --name pg-node-orms -p 5432:5432 -e POSTGRES_PASSWORD=hunter12 -e POSTGRES_USER=orm-user -e POSTGRES_DB=orm-db -v ~/data/pg-node-orms:/var/lib/postgresql/data -d postgres
Now that you’ve got a database running, we need to add some tables and data to the database. This will allow us to query against the data and get a better understanding of the various layers of abstraction.
Run the next command to start an interactive PostgreSQL prompt:
docker run -it --rm --link pg-node-orms:postgres postgres psql -h postgres -U orm-user orm-db
At the prompt type in the password from the previous code block, hunter12. Now that you’re connected, copy and paste the following queries into the prompt and press the Enter key:
CREATE TYPE item_type AS ENUM ( 'meat', 'veg', 'spice', 'dairy', 'oil' ); CREATE TABLE item ( id SERIAL PRIMARY KEY, name VARCHAR(64) NOT NULL, type item_type ); CREATE INDEX ON item (type); INSERT INTO item VALUES (1, 'Chicken', 'meat'), (2, 'Garlic', 'veg'), (3, 'Ginger', 'veg'), (4, 'Garam Masala', 'spice'), (5, 'Turmeric', 'spice'), (6, 'Cumin', 'spice'), (7, 'Ground Chili', 'spice'), (8, 'Onion', 'veg'), (9, 'Coriander', 'spice'), (10, 'Tomato', 'veg'), (11, 'Cream', 'dairy'), (12, 'Paneer', 'dairy'), (13, 'Peas', 'veg'), (14, 'Ghee', 'oil'), (15, 'Cinnamon', 'spice'); CREATE TABLE dish ( id SERIAL PRIMARY KEY, name VARCHAR(64) NOT NULL, veg BOOLEAN NOT NULL ); CREATE INDEX ON dish (veg); INSERT INTO dish VALUES (1, 'Chicken Tikka Masala', false), (2, 'Matar Paneer', true); CREATE TABLE ingredient ( dish_id INTEGER NOT NULL REFERENCES dish (id), item_id INTEGER NOT NULL REFERENCES item (id), quantity FLOAT DEFAULT 1, unit VARCHAR(32) NOT NULL ); INSERT INTO ingredient VALUES (1, 1, 1, 'whole breast'), (1, 2, 1.5, 'tbsp'), (1, 3, 1, 'tbsp'), (1, 4, 2, 'tsp'), (1, 5, 1, 'tsp'), (1, 6, 1, 'tsp'), (1, 7, 1, 'tsp'), (1, 8, 1, 'whole'), (1, 9, 1, 'tsp'), (1, 10, 2, 'whole'), (1, 11, 1.25, 'cup'), (2, 2, 3, 'cloves'), (2, 3, 0.5, 'inch piece'), (2, 13, 1, 'cup'), (2, 6, 0.5, 'tsp'), (2, 5, 0.25, 'tsp'), (2, 7, 0.5, 'tsp'), (2, 4, 0.5, 'tsp'), (2, 11, 1, 'tbsp'), (2, 14, 2, 'tbsp'), (2, 10, 3, 'whole'), (2, 8, 1, 'whole'), (2, 15, 0.5, 'inch stick');
You now have a populated database. You can type quit to disconnect from the psql client and get control of your terminal back. If you ever want to run raw SQL commands again, you can run that same docker run command again.
Finally, you’ll also need to create a file named connection.json containing the following JSON structure. This will be used by the Node applications later to connect to the database:
{
"host": "localhost",
"port": 5432,
"database": "orm-db",
"user": "orm-user",
"password": "hunter12"
}
Before diving into too much code, let’s clarify a few different layers of abstraction. Just like everything in computer science, there are tradeoffs as we add layers of abstraction. With each added layer of abstraction, we attempt to trade a decrease in performance with an increase in developer productivity (though this is not always the case).
This is basically as low-level as you can get — short of manually generating TCP packets and delivering them to the database.
A database driver is going to handle connecting to a database (and sometimes connection pooling). At this level, you’re going to be writing raw SQL strings and delivering them to a database, and receiving a response from the database.
In the Node.js ecosystem there are many libraries operating at this layer. Here are three popular libraries:
Each of these libraries essentially works the same way: take the database credentials, instantiate a new database instance, connect to the database, and send it queries in the form of a string and asynchronously handle the result.
Here is a simple example using the pg module to get a list of ingredients required to cook chicken tikka masala:
Learn more →#!/usr/bin/env node
// $ npm install pg
const { Client } = require('pg');
const connection = require('./connection.json');
const client = new Client(connection);
client.connect();
const query = `SELECT
ingredient.*, item.name AS item_name, item.type AS item_type
FROM
ingredient
LEFT JOIN
item ON item.id = ingredient.item_id
WHERE
ingredient.dish_id = $1`;
client
.query(query, [1])
.then(res => {
console.log('Ingredients:');
for (let row of res.rows) {
console.log(`${row.item_name}: ${row.quantity} ${row.unit}`);
}
client.end();
});
This is the intermediary level between using the simpler database driver module vs. a full-fledged ORM. The most notable module which operates at this layer is Knex.
This module is able to generate queries for a few different SQL dialects. This module depends on one of the aforementioned libraries — you’ll need to install the particular ones you plan on using with Knex.
When creating a Knex instance, you provide the connection details, along with the dialect you plan on using, and are then able to start making queries. The queries you write will closely resemble the underlying SQL queries.
One nicety is that you’re able to programmatically generate dynamic queries in a much more convenient way than if you were to concatenate strings together to form SQL (which often introduces security vulnerabilities).
Here is a simple example using the knex module to get a list of ingredients required to cook chicken tikka masala:
#!/usr/bin/env node
// $ npm install pg knex
const knex = require('knex');
const connection = require('./connection.json');
const client = knex({
client: 'pg',
connection
});
client
.select([
'*',
client.ref('item.name').as('item_name'),
client.ref('item.type').as('item_type'),
])
.from('ingredient')
.leftJoin('item', 'item.id', 'ingredient.item_id')
.where('dish_id', '=', 1)
.debug()
.then(rows => {
console.log('Ingredients:');
for (let row of rows) {
console.log(`${row.item_name}: ${row.quantity} ${row.unit}`);
}
client.destroy();
});
This is the highest level of abstraction we’re going to consider. When working with ORMs, we typically need to do a lot more configuration ahead of time. The point of ORM, as the name implies, is to map a record in a relational database to an object (typically, but not always, a class instance) in our application.
What this means is that we’re defining the structure of these objects, as well as their relationships, in our application code.
In this example, we’re going to look at the most popular of the ORMs: Sequelize. We’re also going to model the relationships represented in our original PostgreSQL schema using Sequelize.
Here is the same example using the Sequelize module to get a list of ingredients required to cook chicken tikka masala:
#!/usr/bin/env node
// $ npm install sequelize pg
const Sequelize = require('sequelize');
const connection = require('./connection.json');
const DISABLE_SEQUELIZE_DEFAULTS = {
timestamps: false,
freezeTableName: true,
};
const { DataTypes } = Sequelize;
const sequelize = new Sequelize({
database: connection.database,
username: connection.user,
host: connection.host,
port: connection.port,
password: connection.password,
dialect: 'postgres',
operatorsAliases: false
});
const Dish = sequelize.define('dish', {
id: { type: DataTypes.INTEGER, primaryKey: true, autoIncrement: true },
name: { type: DataTypes.STRING },
veg: { type: DataTypes.BOOLEAN }
}, DISABLE_SEQUELIZE_DEFAULTS);
const Item = sequelize.define('item', {
id: { type: DataTypes.INTEGER, primaryKey: true, autoIncrement: true },
name: { type: DataTypes.STRING },
type: { type: DataTypes.STRING }
}, DISABLE_SEQUELIZE_DEFAULTS);
const Ingredient = sequelize.define('ingredient', {
dish_id: { type: DataTypes.INTEGER, primaryKey: true },
item_id: { type: DataTypes.INTEGER, primaryKey: true },
quantity: { type: DataTypes.FLOAT },
unit: { type: DataTypes.STRING }
}, DISABLE_SEQUELIZE_DEFAULTS);
Item.belongsToMany(Dish, {
through: Ingredient, foreignKey: 'item_id'
});
Dish.belongsToMany(Item, {
through: Ingredient, foreignKey: 'dish_id'
});
Dish.findOne({where: {id: 1}, include: [{model: Item}]}).then(rows => {
console.log('Ingredients:');
for (let row of rows.items) {
console.log(
`${row.dataValues.name}: ${row.ingredient.dataValues.quantity} ` +
row.ingredient.dataValues.unit
);
}
sequelize.close();
});
We have tried to classify some ORMs in previous sections, but in this section, we will be comparing these ORMs.
Let’s start with Sequelize. Sequelize is a full-blown ORM. It is feature-packed and compels you to hide SQL behind object representation.
Knex, on the other hand, is more low-level as it is a plain query builder. Knex is great because it allows you easily find and see what is going on in the database without the abstraction of ORM. However, as the application grows and becomes more complex, we see that working with complex relations with Knex can be tedious and time-consuming.
Objection.js sits in the middle. It combines the good parts of different ORMs without compromising the power of writing raw SQL queries.
Now that you’ve seen an example of how to perform similar queries using the different abstraction layers, let’s dive into three reasons why you should be wary of using ORM.
Many people pick up ORM because they don’t want to take the time to learn the underlying SQL. The belief is that SQL is hard to learn and that by learning ORM, we can simply write our applications using a single language instead of two.
At first glance, this seems to hold up. ORM is going to be written in the same language as the rest of the application, while SQL is a completely different syntax.
There is a problem with this line of thinking, however. The problem is that ORMs represent some of the most complex libraries you can get your hands on. The surface area of ORM is very large and learning it inside and out is no easy task.
Once you have learned a particular ORM, this knowledge likely won’t transfer that well. This is true if you switch from one platform to another, such as JS/Node.js to C#/.NET. But perhaps even less obvious is that this is true if you switch from one ORM to another within the same platform, such as Sequelize to Bookshelf with Node.js.
Consider the following ORM examples which each generate a list of all recipe items which are vegetarian.
With Sequelize:
#!/usr/bin/env node
// $ npm install sequelize pg
const Sequelize = require('sequelize');
const { Op, DataTypes } = Sequelize;
const connection = require('./connection.json');
const DISABLE_SEQUELIZE_DEFAULTS = {
timestamps: false,
freezeTableName: true,
};
const sequelize = new Sequelize({
database: connection.database,
username: connection.user,
host: connection.host,
port: connection.port,
password: connection.password,
dialect: 'postgres',
operatorsAliases: false
});
const Item = sequelize.define('item', {
id: { type: DataTypes.INTEGER, primaryKey: true, autoIncrement: true },
name: { type: DataTypes.STRING },
type: { type: DataTypes.STRING }
}, DISABLE_SEQUELIZE_DEFAULTS);
// SELECT "id", "name", "type" FROM "item" AS "item"
// WHERE "item"."type" = 'veg';
Item
.findAll({where: {type: 'veg'}})
.then(rows => {
console.log('Veggies:');
for (let row of rows) {
console.log(`${row.dataValues.id}t${row.dataValues.name}`);
}
sequelize.close();
});
With Bookshelf:
#!/usr/bin/env node
// $ npm install bookshelf knex pg
const connection = require('./connection.json');
const knex = require('knex')({
client: 'pg',
connection,
// debug: true
});
const bookshelf = require('bookshelf')(knex);
const Item = bookshelf.Model.extend({
tableName: 'item'
});
// select "item".* from "item" where "type" = ?
Item
.where('type', 'veg')
.fetchAll()
.then(result => {
console.log('Veggies:');
for (let row of result.models) {
console.log(`${row.attributes.id}t${row.attributes.name}`);
}
knex.destroy();
});
With Waterline:
#!/usr/bin/env node
// $ npm install sails-postgresql waterline
const pgAdapter = require('sails-postgresql');
const Waterline = require('waterline');
const waterline = new Waterline();
const connection = require('./connection.json');
const itemCollection = Waterline.Collection.extend({
identity: 'item',
datastore: 'default',
primaryKey: 'id',
attributes: {
id: { type: 'number', autoMigrations: {autoIncrement: true} },
name: { type: 'string', required: true },
type: { type: 'string', required: true },
}
});
waterline.registerModel(itemCollection);
const config = {
adapters: {
'pg': pgAdapter
},
datastores: {
default: {
adapter: 'pg',
host: connection.host,
port: connection.port,
database: connection.database,
user: connection.user,
password: connection.password
}
}
};
waterline.initialize(config, (err, ontology) => {
const Item = ontology.collections.item;
// select "id", "name", "type" from "public"."item"
// where "type" = $1 limit 9007199254740991
Item
.find({ type: 'veg' })
.then(rows => {
console.log('Veggies:');
for (let row of rows) {
console.log(`${row.id}t${row.name}`);
}
Waterline.stop(waterline, () => {});
});
});
With Objection:
#!/usr/bin/env node
// $ npm install knex objection pg
const connection = require('./connection.json');
const knex = require('knex')({
client: 'pg',
connection,
// debug: true
});
const { Model } = require('objection');
Model.knex(knex);
class Item extends Model {
static get tableName() {
return 'item';
}
}
// select "item".* from "item" where "type" = ?
Item
.query()
.where('type', '=', 'veg')
.then(rows => {
for (let row of rows) {
console.log(`${row.id}t${row.name}`);
}
knex.destroy();
});
The syntax for a simple read operation varies greatly between these examples. As the operation you’re trying to perform increases in complexity, such as operations involving multiple tables, the ORM syntax will vary from between implementations even more.
There are at least dozens of ORMs for Node.js alone, and at least hundreds of ORMs for all platforms. Learning all of those tools would be a nightmare!
Lucky for us, there are really only a few SQL dialects to worry about. By learning how to generate queries using raw SQL you can easily transfer this knowledge between different platforms.
Recall that the purpose of ORM is to take the underlying data stored in a database and map it into an object that we can interact within our application. This often comes with some inefficiencies when we use ORM to fetch certain data.
Consider, for example, the queries we first looked at in the section on layers of abstraction. In that query, we simply wanted a list of ingredients and respective quantities for a particular recipe. First, we made the query by writing SQL by hand. Next, we made the query by using the query builder, Knex. Finally, we made a query by using the ORM Sequelize. Let’s take a look at the queries which have been generated by those three commands.
pg driver.This first query is exactly the one we wrote by hand. It represents the most succinct method to get exactly the data we want:
SELECT ingredient.*, item.name AS item_name, item.type AS item_type FROM ingredient LEFT JOIN item ON item.id = ingredient.item_id WHERE ingredient.dish_id = ?;
When we prefix this query with EXPLAIN and send it to the PostgreSQL server, we get a cost operation of 34.12.
This next query was mostly generated for us, but due to the explicit nature of the Knex query builder, we should have a pretty good expectation of what the output will look like:
select *, "item"."name" as "item_name", "item"."type" as "item_type" from "ingredient" left join "item" on "item"."id" = "ingredient"."item_id" where "dish_id" = ?;
New lines have been added by me for readability. Other than some minor formatting and unnecessary table names in my handwritten example, these queries are identical. In fact, once the EXPLAIN query is run, we get the same score of 34.12.
Now let’s take a look at the query generated by ORM:
SELECT "dish"."id", "dish"."name", "dish"."veg", "items"."id" AS "items.id", "items"."name" AS "items.name", "items"."type" AS "items.type", "items->ingredient"."dish_id" AS "items.ingredient.dish_id", "items->ingredient"."item_id" AS "items.ingredient.item_id", "items->ingredient"."quantity" AS "items.ingredient.quantity", "items->ingredient"."unit" AS "items.ingredient.unit" FROM "dish" AS "dish" LEFT OUTER JOIN ( "ingredient" AS "items->ingredient" INNER JOIN "item" AS "items" ON "items"."id" = "items->ingredient"."item_id" ) ON "dish"."id" = "items->ingredient"."dish_id" WHERE "dish"."id" = ?;
New lines have been added by me for readability.
As you can tell this query is a lot different from the previous two queries. Why is it behaving so differently? Well, due to the relationships we’ve defined, Sequelize is trying to get more information than what we’ve asked for. In particular, we’re getting information about the dish itself when we really only care about the ingredients belonging to that dish.
The cost of this query, according to EXPLAIN, is 42.32.
Not all queries can be represented as an ORM operation. When we need to generate these queries, we have to fall back to generating the SQL query by hand. This often means a codebase with heavy ORM usage will still have a few handwritten queries strewn about it. The implications here are that, as a developer working on one of these projects, we end up needing to know BOTH the ORM syntax as well as some underlying SQL syntax.
A common situation that doesn’t work too well with ORMs is when a query contains a subquery. Consider the situation where I know that I have already purchased all the ingredients for dish #2 in our database, however, I still need to purchase whatever ingredients are needed for dish #1. In order to get this list I might run the following query:
SELECT *
FROM item
WHERE
id NOT IN
(SELECT item_id FROM ingredient WHERE dish_id = 2)
AND id IN
(SELECT item_id FROM ingredient WHERE dish_id = 1);
To the best of my knowledge, this query cannot be cleanly represented using the aforementioned ORMs. To combat these situations it’s common for ORM to offer the ability to inject raw SQL into the query interface.
Sequelize offers a .query() method to execute raw SQL as if you were using the underlying database driver. With both the Bookshelf and Objection ORMs, you get access to the raw Knex object which you provide during instantiation and can use that for its query builder powers.
The Knex object also has a .raw() method to execute raw SQL. With Sequelize, you also get a Sequelize.literal() method which can be used to intersperse raw SQL in various parts of a Sequelize ORM call.
But in each of these situations, you still need to know some underlying SQL to generate certain queries.
In previous sections, we gave reasons to why ORM may not be required. However, as the most popular Node.js ORM and with around 1.35 million weekly downloads at the time of writing this article, Sequelize puzzles us with its popularity.
Although we made valid points in the previous section, we have to note that 1.35 million people cannot be wrong weekly. So let’s look at some of the reasons one might use Sequelize in the first place:
sequelize.query method for writing raw queries where this is preferredUsing the low-level database driver modules is rather enticing. There is no overhead when generating a query for the database as we are manually writing the query. The overall dependencies our project relies upon are also minimized. However, generating dynamic queries can be very tedious, and in my opinion, is the biggest drawback of using a simple database driver.
Consider, for example, a web interface where a user can select criteria when they want to retrieve items. If there is only a single option that a user can input, such as color, our query might look like the following:
SELECT * FROM things WHERE color = ?;
This single query works nicely with the simple database driver. However, consider if the color is optional and that there’s a second optional field called is_heavy. We now need to support a few different permutations of this query:
SELECT * FROM things; -- Neither SELECT * FROM things WHERE color = ?; -- Color only SELECT * FROM things WHERE is_heavy = ?; -- Is Heavy only SELECT * FROM things WHERE color = ? AND is_heavy = ?; -- Both
However, due to the aforementioned reasons, a fully featured ORM isn’t the tool we want to reach for either.
The query builder ends up being a pretty nice tool in these situations. The interface exposed by Knex is so close to the underlying SQL query that we are forced to always know what the SQL query looks like. This relationship is similar to how something like TypeScript translates to JavaScript.
Using a query builder is a fine solution as long as you fully understand the underlying SQL it’s generating. Never use it as a tool to hide from what is happening at a lower layer. Only use it as a matter of convenience and in situations where you know exactly what it’s doing.
If you ever find yourself questioning what a generated query actually looks like you can add a debug field to the Knex() instantiation call. Doing so looks like this:
const knex = require('knex')({
client: 'pg',
connection,
debug: true // Enable Query Debugging
});
In fact, most of the libraries mentioned in this post include some sort of method for debugging the calls being executed.
From our discussion so far, we have seen the weak points of ORM. However, in this subsection, we will look at certain scenarios where ORM is a good choice.
Generally, since ORMs provide a high-level abstraction over a database, it offers less control than a raw query. And for this reason, it is slower in performance than raw query. But to make up for these downsides, ORM often ships with a lot of great features:
In summary, use ORM to achieve code standardization, security, maintainability, language abstraction, DRY, etc.
So far, we have learned about some Node.js ORM and query builders. However, picking the “best” ORM for Node.js is not easy because they all have pros and cons. And in my opinion, the best Node.js ORM changes depending on your application needs.
Previously, we classified libraries used to query and manipulate data into three categories based on their level of abstraction:
Since there is no ORM that solves all your problems, the best ORM is the ORM that is most suitable for your application need. And with the information above, you should know what you are willing to trade and what your application requires. Consequently, you can choose which ORM is best for your Node.js application.
We’ve looked at three different layers of abstracting database interactions, namely the low-level database drivers, query builders, and the high-level ORMs.
We’ve also examined the tradeoffs of using each layer as well as the SQL queries being generated, including the difficulty of generating dynamic queries with a database driver, the added complexity of ORMs, and finally the sweet spot of using a query generator.
Thank you for reading and be sure to take this into consideration when you build your next project.
Once you’re done following along, you may run the following commands to completely remove the docker container and remove the database files from your computer:
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