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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.
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
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:
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();
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 '$' or contain a '.'.
var document = { hello: 'world'
, n: 5
, today: new Date()
, nedbIsAwesome: true
, notthere: null
, notToBeSaved: undefined // Will not be saved
, fruits: [ 'apple', 'orange', 'pear' ]
, infos: { name: 'nedb' }
};
db.insert(document, function (err, newDoc) { // Callback is optional
// newDoc is the newly inserted document, including its _id
// newDoc has no key called notToBeSaved since its value was undefined
});
Use find
to look for multiple documents matching you query, or findOne
to look for one specific document. You can select documents based on field equality or use comparison operators ($lt
, $lte
, $gt
, $gte
, $in
, $nin
, $ne
). You can also use logical operators $or
, $and
and $not
. See below for the syntax.
// 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
});
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
});
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!)
});
You can combine queries using logical operators:
$or
and $and
, the syntax is { $op: [query1, query2, ...] }
.$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
});
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!)
$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 syntaxoptions
is an object with two possible parameters
multi
(defaults to false
) which allows the modification of several documents if set to trueupsert
(defaults to false
) if you want to insert a new document corresponding to the update
rules if your query
doesn't match anythingcallback
(optional) signature: err, numReplaced, upsert
numReplaced
is the number of documents replacedupsert
is set to true if the upsert mode was chosen and a document was insertedNote: 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
});
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 updatingoptions
only one option for now: multi
which allows the removal of multiple documents if set to true. Default is falsecallback
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
});
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.
Also note that if you use a unique constraint on a field, you will only be able to save one document in which is undefined
. The second time you do that, the index will reject the document since there is already one with the undefined
value. I am working on a "sparse" option just like the MongoDB one, enabling indexes to check uniqueness only when the field is defined.
Finally, the _id
is always indexed with a unique constraint, so queries specifying a value for it are very fast.
// The syntax is close, but not identical to MongoDB's
// fieldName is of course required
d.ensureIndex({ fieldName: 'somefield' }, function (err) {
// If there was an error, err is not null
});
// Using a unique constraint with the index
d.ensureIndex({ fieldName: 'somefield', unique: true }, function (err) {
});
// The ensureIndex method can be called whenever you want: before or after a loadDatabase(),
// after some data was inserted/modified/removed. It will fail to create the index if the
// unique constraint is not satisfied
// Format of the error message when the unique constraint is not met
d.insert({ name: 'nedb' }, function (err) {
// err is null
d.insert({ name: '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).
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:
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.
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).
(The MIT License)
Copyright (c) 2013 Louis Chatriot <louis.chatriot@gmail.com>
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.
FAQs
File-based embedded data store for node.js
The npm package nedb receives a total of 17,925 weekly downloads. As such, nedb popularity was classified as popular.
We found that nedb demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 1 open source maintainer collaborating on the project.
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