nedb

NeDB (Node embedded database)

Embedded persistent database for Node.js, written in Javascript, with no dependency (except npm modules of course). You can think of it as a SQLite for Node.js projects, which can be used with a simple require statement. The API is a subset of MongoDB's. You can use it as a persistent or an in-memory only datastore.

Installation, tests

Module name on npm is nedb.

npm install nedb --save   // Put latest version in your package.json

make test   // You'll need the dev dependencies to test it

API

It's a subset of MongoDB's API (the most used operations). The current API will not change, but I will add operations as they are needed. Summary of the API:

• Creating/loading a database
• Inserting documents
• Finding documents
• Updating documents
• Removing documents
• Indexing

Creating/loading a database

You can use NeDB as an in-memory only datastore or as a persistent datastore.

// In-memory only datastore
var Datastore = require('nedb')
  , db = new Datastore();   // Call the constructor without any argument
// No need to call loadDatabase(), you can begin inserting data and query it right away


// Persistent datastore
var Datastore = require('nedb')
  , db = new Datastore('path/to/datafile');
  
db.loadDatabase(function (err) {    // Callback is optional
  // err is the error, if any
});


// Of course you can create multiple datastores if you need several
// collections. For example:
db = {};
db.users = new Datastore('path/to/users.db');
db.robots = new Datastore('path/to/robots.db');

// You need to load each database (here we do it asynchronously)
db.users.loadDatabase();
db.robots.loadDatabase();

Inserting documents

The native types are String, Number, Boolean, Date and null. You can also use arrays and subdocuments (objects). If a field is undefined, it will not be saved (this is different from MongoDB which transforms undefined in null, something I find counter-intuitive).

An _id field will be automatically generated by NeDB. It's a 16-characters alphanumerical string that cannot be modified once it has been generated. Unlike with MongoDB, you cannot specify it (that shouldn't be a problem anyway).

Field names cannot begin by '$' or contain a '.'.

var document = { hello: 'world'
               , n: 5
               , today: new Date()
               , nedbIsAwesome: true
               , notthere: null
               , notToBeSaved: undefined  // Will not be saved
               , fruits: [ 'apple', 'orange', 'pear' ]
               , infos: { name: 'nedb' }
               };

db.insert(document, function (err, newDoc) {   // Callback is optional
  // newDoc is the newly inserted document, including its _id
  // newDoc has no key called notToBeSaved since its value was undefined
});

Finding documents

Use find to look for multiple documents matching you query, or findOne to look for one specific document. You can select documents based on field equality or use comparison operators ($lt, $lte, $gt, $gte, $in, $nin, $ne). You can also use logical operators $or, $and and $not. See below for the syntax.

Basic querying

// Let's say our datastore contains the following collection
// { _id: 'id1', planet: 'Mars', system: 'solar', inhabited: false, satellites: ['Phobos', 'Deimos'] }
// { _id: 'id2', planet: 'Earth', system: 'solar', inhabited: true, humans: { genders: 2, eyes: true } }
// { _id: 'id3', planet: 'Jupiter', system: 'solar', inhabited: false }
// { _id: 'id4', planet: 'Omicron Persei 8', system: 'futurama', inhabited: true, humans: { genders: 7 } }

// Finding all planets in the solar system
db.find({ system: 'solar' }, function (err, docs) {
  // docs is an array containing documents Mars, Earth, Jupiter
  // If no document is found, docs is equal to []
});

// Finding all inhabited planets in the solar system
db.find({ system: 'solar', inhabited: true }, function (err, docs) {
  // docs is an array containing document Earth only
});

// Use the dot-notation to match fields in subdocuments
db.find({ "humans.genders": 2 }, function (err, docs) {
  // docs contains Earth
});

// You can also deep-compare objects. Don't confuse this with dot-notation!
db.find({ humans: { genders: 2 } }, function (err, docs) {
  // docs is empty, because { genders: 2 } is not equal to { genders: 2, eyes: true }
});

// Find all documents in the collection
db.find({}, function (err, docs) {
});

// The same rules apply when you want to only find one document
db.findOne({ _id: 'id1' }, function (err, doc) {
  // doc is the document Mars
  // If no document is found, doc is null
});

Comparison operators ($lt, $lte, $gt, $gte, $in, $nin, $ne)

The syntax is { field: { $op: value } } where $op is any comparison operator:

• $lt, $lte: less than, less than or equal
• $gt, $gte: greater than, greater than or equal
• $in: member of. value must be an array of values
• $ne, $nin: not equal, not a member of

// $lt, $lte, $gt and $gte work on numbers and strings
db.find({ "humans.genders": { $gt: 5 } }, function (err, docs) {
  // docs contains Omicron Persei 8, whose humans have more than 5 genders (7).
});

// When used with strings, lexicographical order is used
db.find({ planet: { $gt: 'Mercury' }}, function (err, docs) {
  // docs contains Omicron Persei 8
})

// Using $in. $nin is used in the same way
db.find({ planet: { $in: ['Earth', 'Jupiter'] }}, function (err, docs) {
  // docs contains Earth and Jupiter
});

Array fields

When a field in a document is an array, NeDB tries the query on every element and there is a match if at least one element matches.

// If a document's field is an array, matching it means matching any element of the array
db.find({ satellites: 'Phobos' }, function (err, docs) {
  // docs contains Mars. Result would have been the same if query had been { satellites: 'Deimos' }
});

// This also works for queries that use comparison operators
db.find({ satellites: { $lt: 'Amos' } }, function (err, docs) {
  // docs is empty since Phobos and Deimos are after Amos in lexicographical order
});

// This also works with the $in and $nin operator
db.find({ satellites: { $in: ['Moon', 'Deimos'] } }, function (err, docs) {
  // docs contains Mars (the Earth document is not complete!)
});

Logical operators $or, $and, $not

You can combine queries using logical operators:

• For $or and $and, the syntax is { $op: [query1, query2, ...] }.
• For $not, the syntax is { $not: query }

db.find({ $or: [{ planet: 'Earth' }, { planet: 'Mars' }] }, function (err, docs) {
  // docs contains Earth and Mars
});

db.find({ $not: { planet: 'Earth' } }, function (err, docs) {
  // docs contains Mars, Jupiter, Omicron Persei 8
});

// You can mix normal queries, comparison queries and logical operators
db.find({ $or: [{ planet: 'Earth' }, { planet: 'Mars' }], inhabited: true }, function (err, docs) {
  // docs contains Earth
});

Updating documents

db.update(query, update, options, callback) will update all documents matching query according to the update rules:

• query is the same kind of finding query you use with find and findOne
• update specifies how the documents should be modified. It is either a new document or a set of modifiers (you cannot use both together, it doesn't make sense!)
  • A new document will replace the matched docs
  • The available modifiers are $set to change a field's value and $inc to increment a field's value. The modifiers create the fields they need to modify if they don't exist, and you can apply them to subdocs. See examples below for the syntax
• options is an object with two possible parameters
  • multi (defaults to false) which allows the modification of several documents if set to true
  • upsert (defaults to false) if you want to insert a new document corresponding to the update rules if your query doesn't match anything
• callback (optional) signature: err, numReplaced, upsert
  • numReplaced is the number of documents replaced
  • upsert is set to true if the upsert mode was chosen and a document was inserted

Note: you can't change a document's _id.

// Let's use the same example collection as in the "finding document" part
// { _id: 'id1', planet: 'Mars', system: 'solar', inhabited: false }
// { _id: 'id2', planet: 'Earth', system: 'solar', inhabited: true }
// { _id: 'id3', planet: 'Jupiter', system: 'solar', inhabited: false }
// { _id: 'id4', planet: 'Omicron Persia 8', system: 'futurama', inhabited: true }

// Replace a document by another
db.update({ planet: 'Jupiter' }, { planet: 'Pluton'}, {}, function (err, numReplaced) {
  // numReplaced = 1
  // The doc #3 has been replaced by { _id: 'id3', planet: 'Pluton' }
  // Note that the _id is kept unchanged, and the document has been replaced
  // (the 'system' and inhabited fields are not here anymore)
});

// Set an existing field's value
db.update({ system: 'solar' }, { $set: { system: 'solar system' } }, { multi: true }, function (err, numReplaced) {
  // numReplaced = 3
  // Field 'system' on Mars, Earth, Jupiter now has value 'solar system'
});

// Setting the value of a non-existing field in a subdocument by using the dot-notation
db.update({ planet: 'Mars' }, { $set: { "data.satellites": 2, "data.red": true } }, {}, function () {
  // Mars document now is { _id: 'id1', system: 'solar', inhabited: false
  //                      , data: { satellites: 2, red: true }
  //                      }
  // Not that to set fields in subdocuments, you HAVE to use dot-notation
  // Using object-notation will just replace the top-level field
  db.update({ planet: 'Mars' }, { $set: { date: { satellites: 3 } } }, {}, function () {
    // Mars document now is { _id: 'id1', system: 'solar', inhabited: false
    //                      , data: { satellites: 3 }
    //                      }
    // You lost the "data.red" field which is probably not the intended behavior
  });
});

// Upserting a document
db.update({ planet: 'Pluton' }, { planet: 'Pluton', inhabited: false }, { upsert: true }, function (err, numReplaced, upsert) {
  // numReplaced = 1, upsert = true
  // A new document { _id: 'id5', planet: 'Pluton', inhabited: false } has been added to the collection
});

// If you upsert with a modifier, the upserted doc is the query modified by the modifier
// This is simpler than it sounds :)
db.update({ planet: 'Pluton' }, { $inc: { distance: 38 } }, { upsert: true }, function () {
  // A new document { _id: 'id5', planet: 'Pluton', distance: 38 } has been added to the collection  
});

Removing documents

db.remove(query, options, callback) will remove all documents matching query according to options

• query is the same as the ones used for finding and updating
• options only one option for now: multi which allows the removal of multiple documents if set to true. Default is false
• callback is optional, signature: err, numRemoved

// Let's use the same example collection as in the "finding document" part
// { _id: 'id1', planet: 'Mars', system: 'solar', inhabited: false }
// { _id: 'id2', planet: 'Earth', system: 'solar', inhabited: true }
// { _id: 'id3', planet: 'Jupiter', system: 'solar', inhabited: false }
// { _id: 'id4', planet: 'Omicron Persia 8', system: 'futurama', inhabited: true }

// Remove one document from the collection
// options set to {} since the default for multi is false
db.remove({ _id: 'id2' }, {}, function (err, numRemoved) {
  // numRemoved = 1
});

// Remove multiple documents
db.remove({ system: 'solar' }, { multi: true }, function (err, numRemoved) {
  // numRemoved = 3
  // All planets from the solar system were removed
});

Indexing

NeDB supports indexing. It gives a very nice speed boost and can be used to enforce a unique constraint on a field. You can index any field, including fields in nested documents using the dot notation. For now, indexes are only used for value equality, but I am planning on adding value comparison soon.

To create an index, use datastore.ensureIndex(options, cb), where callback is optional and get passed an error if any. The options are:

• fieldName (required): name of the field to index. Use the dot notation to index a field in a nested document.
• unique (optional, defaults to false): enforce field uniqueness. Note that a unique index will raise an error if you try to index two documents for which the field is not defined.
• sparse (optional, defaults to false): don't index documents for which the field is not defined. Use this option along with "unique" if you want to accept multiple documents for which it is not defined.

Notes:

• The _id is automatically indexed with a unique constraint, so queries specifying a value for it are very fast.
• Currently, indexes are implemented as binary search trees. I will use self-balancing binary search trees in the future to guarantee a consistent performance (the index on _id is already balanced since the _id is randomly generated).

db.ensureIndex({ fieldName: 'somefield' }, function (err) {
  // If there was an error, err is not null
});

// Using a unique constraint with the index
db.ensureIndex({ fieldName: 'somefield', unique: true }, function (err) {
});

// Using a sparse unique index
db.ensureIndex({ fieldName: 'somefield', unique: true, sparse: true }, function (err) {
});


// Format of the error message when the unique constraint is not met
db.insert({ somefield: 'nedb' }, function (err) {
  // err is null
  db.insert({ somefiled: 'nedb' }, function (err) {
    // err is { errorType: 'uniqueViolated'
    //        , key: 'name'
    //        , message: 'Unique constraint violated for key name' }
  });
});

Note: the ensureIndex function creates the index synchronously, so it's best to use it at application startup. It's quite fast so it doesn't increase startup time much (35 ms for a collection containing 10,000 documents).

Performance

Speed

NeDB is not intended to be a replacement of large-scale databases such as MongoDB! Its goal is to provide you with a clean and easy way to query data and persist it to disk, for web applications that do not need lots of concurrent connections, for example a continuous integration and deployment server and desktop applications built with Node Webkit.

As such, it was not designed for speed. That said, it is still pretty fast on the expected datasets, especially if you use indexing. On my machine (3 years old, no SSD), with a collection containing 10,000 documents:

• An insert takes 0.14 ms without indexing, 0.16 ms with indexing
• A read takes 6.4 ms without indexing, 0.02 ms with indexing
• An update takes 11 ms without indexing, 0.22 ms with indexing
• A deletion takes 10 ms without indexing, 0.14ms with indexing

You can run the simple benchmarks I use by executing the scripts in the benchmarks folder. Run them with the --help flag to see how they work.

Memory footprint

A copy of the whole database is kept in memory. This is not much on the expected kind of datasets (20MB for 10,000 2KB documents). If requested, I'll introduce an option to not use this cache to decrease memory footprint (at the cost of a lower speed).

Use in other services

• connect-nedb-session is a session store for Connect and Express, backed by nedb
• If you've outgrown NeDB, switching to MongoDB won't be too hard as it is the same API. Use this utility to transfer the data from a NeDB database to a MongoDB collection

License

(The MIT License)

Copyright (c) 2013 Louis Chatriot <[e-mail]>

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the 'Software'), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.