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Mapper makes 80% of data access easy and provides unobtrusive access to SQL for the 20% complicated, speed-critical tasks.


Wanted a lightweight data mapper that is fast and likes SQL.


To use mapper in your project

# For Ubuntu
sudo apt-get install libmysqlclient-dev

npm install mapper

To run Backbone or AngularJS Example

git clone git://
cd mapper
npm install -d
make test                   # creates necessary database and config.json
node example/app.js

then browse http://localhost:3000


Connection pooling - adding SOON


Conect to Database

var Mapper = require('mapper');
var conn = { user: 'grace', password: 'secret', database: 'app_dev' };

// set verbose flag to trace SQL
// set strict to be warned of invalid columns in JSON objects
Mapper.connect(conn, {verbose: true, strict: false});

Define Data Access Objects

// Table name and optional primary key
var Comment ="Comments")
  , Post ="Posts", "id");

Define Relationships

Post.hasMany("comments", Comment, "postId");
Comment.belongsTo("post", Post, "postId");


var insertId;

// These are equivalent, where first is more SQL like
Post.insert({ title: 'First Post' }).exec(function(err, result) {
    insertId = result.insertId;
Post.create({ title: 'First Post' }, function(err, result) { ... });


// Select inserted post
Post.where({ id: insertId }).one(function(err, post) {
    assert.equal(post.title, 'First Post,');

Post.findById(insertId, function(err, post) { ... });


// update inserted post
  .update()                         // optional since set() is used
  .set({ title: 'New Title' })
  .where({ id: insertId })
  .exec(function (err, result) {
    assert.equal(result.affectedRows, 1);

// if doc has id set, then save is simple. Note,
// pluck only the columns you want updated, function(err, result) { ... });


// delete all posts with a specific title
Post.delete().where({ title: 'New Title' }).exec(function(err, result) {
    assert.equal(result.affectedRows, 1);

Post.deleteById(insertId, function(err, result) {});

Gets the first page of posts and populate comments property with the second page of comments for each post retrieved.

  .select('id', 'title', 'excerpt')
  .page(0, 25)
  .order('id DESC')
  .load('comments', function(c) {'comment', 'createdAt')
     .order('id DESC')
     .page(1, 50);
  .all(function(err, posts) {
    // boo-yah!

OR, if you prefer SQL

var sql = ("SELECT id, title, excerpt FROM `Posts` \
            ORDER BY id DESC LIMIT 0, 25";

Post.all(sql, function(err, posts) {
  Post.load('comments', function(c) {
    c.sql("SELECT comment, createdAt FROM Comments ORDER BY id DESC LIMIT 1, 50");
  }).in(posts, function(err) {
    // boo-yah!

SQL goodness

Executing multiple statements in a series

  "SELECT * FROM posts WHERE author = ?", [1],

  // SQL may be separated by `,`
  "SELECT * ",
  "FROM comments WHERE author = ?", [1],

  function(err, results) {
    // posts are in results[0][0..n]
    // comments are in results[1][0..n]

Executing multiple statements in parallel

  "SELECT * FROM posts WHERE author = ?", [1],
  "SELECT * FROM comments WHERE author = ?", [1],
  function(err, results) {


Time for 100,000 iterations alternating between insert and select. See test/bench or run make bench.

time node test/bench/testMysql.js (mysql 2.0.0-alpha3)

real        1m27.239s
user        0m58.506s
sys         0m3.288s

time node test/bench/testMapperDao.js

real        0m30.701s
user        0m11.346s
sys         0m4.403s

time node test/bench/testLibMysql.js

real        0m26.044s
user        0m8.207s
sys         0m3.784s

time node test/bench/testMongo.js (just for fun)

real        0m41.771s
user        0m30.830s
sys         0m2.910s

The takeaway is mysql-libmysqlclient is a much faster driver than the widely used mysql driver. Mapper, which is based on mysql-libmysqlclient adds overhead yet outperforms the raw mysql driver.

Even more surprising is Mapper is faster than MongoDB using the official MongoDB driver for node.js.

Implementation Best Practice

A simple approach, without over-engineering your project, is to maintain 3 distinct layers in your code:

  1. Data Access Objects (DAO) - Responsible for interacting with the database. There should be 1 DAO for each table used by project.
  2. Models - A model uses one or more DAO adding business logic, validations as needed.
  3. Resources or Services - This layer should only use models never DAO.

On a more complex project where a few tables might be better stored in Redis for example, insert a Repository layer between DAO and models to insulate models completely from low-level data access.