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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.

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.

NeDB was benchmarked against the popular client-side database TaffyDB and NeDB is much, much faster. That's why there is now a browser version, which can also provide persistence.

Check the change log in the wiki if you think nedb doesn't behave as the documentation describes! Most of the issues I get are due to non-latest version NeDBs.

Support NeDB development

No time to help out? You can support NeDB development by sending money or bitcoins!

Money: Donate to author

Bitcoin address: 1dDZLnWpBbodPiN8sizzYrgaz5iahFyb1

Installation, tests

Module name on npm is nedb.

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

npm 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:

Creating/loading a database

You can use NeDB as an in-memory only datastore or as a persistent datastore. One datastore is the equivalent of a MongoDB collection. The constructor is used as follows new Datastore(options) where options is an object with the following fields:

  • filename (optional): path to the file where the data is persisted. If left blank, the datastore is automatically considered in-memory only. It cannot end with a ~ which is used in the temporary files NeDB uses to perform crash-safe writes
  • inMemoryOnly (optional, defaults to false): as the name implies.
  • autoload (optional, defaults to false): if used, the database will automatically be loaded from the datafile upon creation (you don't need to call loadDatabase). Any command issued before load is finished is buffered and will be executed when load is done.
  • onload (optional): if you use autoloading, this is the handler called after the loadDatabase. It takes one error argument. If you use autoloading without specifying this handler, and an error happens during load, an error will be thrown.
  • nodeWebkitAppName (optional, DEPRECATED): if you are using NeDB from whithin a Node Webkit app, specify its name (the same one you use in the package.json) in this field and the filename will be relative to the directory Node Webkit uses to store the rest of the application's data (local storage etc.). It works on Linux, OS X and Windows. Now that you can use require('nw.gui').App.dataPath in Node Webkit to get the path to the data directory for your application, you should not use this option anymore and it will be removed.

If you use a persistent datastore without the autoload option, you need to call loadDatabase manually. This function fetches the data from datafile and prepares the database. Don't forget it! If you use a persistent datastore, no command (insert, find, update, remove) will be executed before loadDatabase is called, so make sure to call it yourself or use the autoload option.

// Type 1: In-memory only datastore (no need to load the database)
var Datastore = require('nedb')
  , db = new Datastore();

// Type 2: Persistent datastore with manual loading
var Datastore = require('nedb')
  , db = new Datastore({ filename: 'path/to/datafile' });
db.loadDatabase(function (err) {    // Callback is optional
  // Now commands will be executed

// Type 3: Persistent datastore with automatic loading
var Datastore = require('nedb')
  , db = new Datastore({ filename: 'path/to/datafile', autoload: true });
// You can issue commands right away

// Type 4: Persistent datastore for a Node Webkit app called 'nwtest'
// For example on Linux, the datafile will be ~/.config/nwtest/nedb-data/something.db
var Datastore = require('nedb')
  , path = require('path')
  , db = new Datastore({ filename: path.join(require('nw.gui').App.dataPath, 'something.db') });

// Of course you can create multiple datastores if you need several
// collections. In this case it's usually a good idea to use autoload for all collections.
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)

Compacting the database

Under the hood, NeDB's persistence uses an append-only format, meaning that all updates and deletes actually result in lines added at the end of the datafile. The reason for this is that disk space is very cheap and appends are much faster than rewrites since they don't do a seek. The database is automatically compacted (i.e. put back in the one-line-per-document format) everytime your application restarts.

You can manually call the compaction function with yourDatabase.persistence.compactDatafile which takes no argument. It queues a compaction of the datafile in the executor, to be executed sequentially after all pending operations.

You can also set automatic compaction at regular intervals with yourDatabase.persistence.setAutocompactionInterval(interval), interval in milliseconds (a minimum of 5s is enforced), and stop automatic compaction with yourDatabase.persistence.stopAutocompaction().

Keep in mind that compaction takes a bit of time (not too much: 130ms for 50k records on my slow machine) and no other operation can happen when it does, so most projects actually don't need to use it.

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).

If the document does not contain an _id field, NeDB will automatically generated one for you (a 16-characters alphanumerical string). The _id of a document, once set, cannot be modified.

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

var doc = { 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(doc, 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

You can also bulk-insert an array of documents. This operation is atomic, meaning that if one insert fails due to a unique constraint being violated, all changes are rolled back.

db.insert([{ a: 5 }, { a: 42 }], function (err, newDocs) {
  // Two documents were inserted in the database
  // newDocs is an array with these documents, augmented with their _id

// If there is a unique constraint on field 'a', this will fail
db.insert([{ a: 5 }, { a: 42 }, { a: 5 }], function (err) {
  // err is a 'uniqueViolated' error
  // The database was not modified

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, $not and $where. See below for the syntax.

You can use regular expressions in two ways: in basic querying in place of a string, or with the $regex operator.

You can sort and paginate results using the cursor API (see below).

You can use standard projections to restrict the fields to appear in the results (see below).

Basic querying

Basic querying means are looking for documents whose fields match the ones you specify. You can use regular expression to match strings. You can use the dot notation to navigate inside nested documents, arrays, arrays of subdocuments and to match a specific element of an array.

// 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 } }
// { _id: 'id5', completeData: { planets: [ { name: 'Earth', number: 3 }, { name: 'Mars', number: 2 }, { name: 'Pluton', number: 9 } ] } }

// 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 planets whose name contain the substring 'ar' using a regular expression
db.find({ planet: /ar/ }, function (err, docs) {
  // docs contains Mars and Earth

// 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

// Use the dot-notation to navigate arrays of subdocuments
db.find({ "": "Mars" }, function (err, docs) {
  // docs contains document 5

db.find({ "": "Jupiter" }, function (err, docs) {
  // docs is empty

db.find({ "": "Earth" }, function (err, docs) {
  // docs contains document 5
  // If we had tested against "Mars" docs would be empty because we are matching against a specific array element

// 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

Operators ($lt, $lte, $gt, $gte, $in, $nin, $ne, $exists, $regex)

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
  • $exists: checks whether the document posses the property field. value should be true or false
  • $regex: checks whether a string is matched by the regular expression. Contrary to MongoDB, the use of $options with $regex is not supported, because it doesn't give you more power than regex flags. Basic queries are more readable so only use the $regex operator when you need to use another operator with it (see example below)
// $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

// Using $exists
db.find({ satellites: { $exists: true } }, function (err, docs) {
  // docs contains only Mars

// Using $regex with another operator
db.find({ planet: { $regex: /ar/, $nin: ['Jupiter', 'Earth'] } }, function (err, docs) {
  // docs only contains Mars because Earth was excluded from the match by $nin

Array fields

When a field in a document is an array, NeDB first tries to see if there is an array-specific comparison function (for now there is only $size) being used and tries it first. If there isn't, the query is treated as a query on every element and there is a match if at least one element matches.

// Using an array-specific comparison function
// Note: you can't use nested comparison functions, e.g. { $size: { $lt: 5 } } will throw an error
db.find({ satellites: { $size: 2 } }, function (err, docs) {
  // docs contains Mars

db.find({ satellites: { $size: 1 } }, function (err, docs) {
  // docs is empty

// 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, $where

You can combine queries using logical operators:

  • For $or and $and, the syntax is { $op: [query1, query2, ...] }.
  • For $not, the syntax is { $not: query }
  • For $where, the syntax is { $where: function () { /* object is "this", return a boolean */ } }
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

db.find({ $where: function () { return Object.keys(this) > 6; } }, function (err, docs) {
  // docs with more than 6 properties

// 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

Sorting and paginating

If you don't specify a callback to find, findOne or count, a Cursor object is returned. You can modify the cursor with sort, skip and limit and then execute it with exec(callback).

// Let's say the database contains these 4 documents
// doc1 = { _id: 'id1', planet: 'Mars', system: 'solar', inhabited: false, satellites: ['Phobos', 'Deimos'] }
// doc2 = { _id: 'id2', planet: 'Earth', system: 'solar', inhabited: true, humans: { genders: 2, eyes: true } }
// doc3 = { _id: 'id3', planet: 'Jupiter', system: 'solar', inhabited: false }
// doc4 = { _id: 'id4', planet: 'Omicron Persei 8', system: 'futurama', inhabited: true, humans: { genders: 7 } }

// No query used means all results are returned (before the Cursor modifiers)
db.find({}).sort({ planet: 1 }).skip(1).limit(2).exec(function (err, docs) {
  // docs is [doc3, doc1]

// You can sort in reverse order like this
db.find({ system: 'solar' }).sort({ planet: -1 }).exec(function (err, docs) {
  // docs is [doc1, doc3, doc2]

// You can sort on one field, then another, and so on like this:
db.find({}).sort({ firstField: 1, secondField: -1 }) ...   // You understand how this works!


You can give find and findOne an optional second argument, projections. The syntax is the same as MongoDB: { a: 1, b: 1 } to return only the a and b fields, { a: 0, b: 0 } to omit these two fields. You cannot use both modes at the time, except for _id which is by default always returned and which you can choose to omit.

// Same database as above

// Keeping only the given fields
db.find({ planet: 'Mars' }, { planet: 1, system: 1 }, function (err, docs) {
  // docs is [{ planet: 'Mars', system: 'solar', _id: 'id1' }]

// Keeping only the given fields but removing _id
db.find({ planet: 'Mars' }, { planet: 1, system: 1, _id: 0 }, function (err, docs) {
  // docs is [{ planet: 'Mars', system: 'solar' }]

// Omitting only the given fields and removing _id
db.find({ planet: 'Mars' }, { planet: 0, system: 0, _id: 0 }, function (err, docs) {
  // docs is [{ inhabited: false, satellites: ['Phobos', 'Deimos'] }]

// Failure: using both modes at the same time
db.find({ planet: 'Mars' }, { planet: 0, system: 1 }, function (err, docs) {
  // err is the error message, docs is undefined

// You can also use it in a Cursor way but this syntax is not compatible with MongoDB
// If upstream compatibility is important don't use this method
db.find({ planet: 'Mars' }).projection({ planet: 1, system: 1 }).exec(function (err, docs) {
  // docs is [{ planet: 'Mars', system: 'solar', _id: 'id1' }]

Counting documents

You can use count to count documents. It has the same syntax as find. For example:

// Count all planets in the solar system
db.count({ system: 'solar' }, function (err, count) {
  // count equals to 3

// Count all documents in the datastore
db.count({}, function (err, count) {
  // count equals to 4

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 modifiers create the fields they need to modify if they don't exist, and you can apply them to subdocs. Available field modifiers are $set to change a field's value, $unset to delete a field and $inc to increment a field's value. To work on arrays, you have $push, $pop, $addToSet, $pull, and the special $each. 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, newDoc
    • numReplaced is the number of documents replaced
    • newDoc is the created document 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, "": 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: { data: { satellites: 3 } } }, {}, function () {
    // Mars document now is { _id: 'id1', system: 'solar', inhabited: false
    //                      , data: { satellites: 3 }
    //                      }
    // You lost the "" field which is probably not the intended behavior

// Deleting a field
db.update({ planet: 'Mars' }, { $unset: { planet: true } }, {}, function () {
  // Now the document for Mars doesn't contain the planet field
  // You can unset nested fields with the dot notation of course

// Upserting a document
db.update({ planet: 'Pluton' }, { planet: 'Pluton', inhabited: false }, { upsert: true }, function (err, numReplaced, upsert) {
  // numReplaced = 1, upsert = { _id: 'id5', planet: 'Pluton', inhabited: false }
  // 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  

// If we insert a new document { _id: 'id6', fruits: ['apple', 'orange', 'pear'] } in the collection,
// let's see how we can modify the array field atomically

// $push inserts new elements at the end of the array
db.update({ _id: 'id6' }, { $push: { fruits: 'banana' } }, {}, function () {
  // Now the fruits array is ['apple', 'orange', 'pear', 'banana']

// $pop removes an element from the end (if used with 1) or the front (if used with -1) of the array
db.update({ _id: 'id6' }, { $pop: { fruits: 1 } }, {}, function () {
  // Now the fruits array is ['apple', 'orange']
  // With { $pop: { fruits: -1 } }, it would have been ['orange', 'pear']

// $addToSet adds an element to an array only if it isn't already in it
// Equality is deep-checked (i.e. $addToSet will not insert an object in an array already containing the same object)
// Note that it doesn't check whether the array contained duplicates before or not
db.update({ _id: 'id6' }, { $addToSet: { fruits: 'apple' } }, {}, function () {
  // The fruits array didn't change
  // If we had used a fruit not in the array, e.g. 'banana', it would have been added to the array

// $pull removes all values matching a value or even any NeDB query from the array
db.update({ _id: 'id6' }, { $pull: { fruits: 'apple' } }, {}, function () {
  // Now the fruits array is ['orange', 'pear']
db.update({ _id: 'id6' }, { $pull: { fruits: $in: ['apple', 'pear'] } }, {}, function () {
  // Now the fruits array is ['orange']

// $each can be used to $push or $addToSet multiple values at once
// This example works the same way with $addToSet
db.update({ _id: 'id6' }, { $push: { fruits: {$each: ['banana', 'orange'] } } }, {}, function () {
  // Now the fruits array is ['apple', 'orange', 'pear', 'banana', 'orange']

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


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 to speed up basic queries and queries using $in, $lt, $lte, $gt and $gte.

To create an index, use datastore.ensureIndex(options, cb), where callback is optional and get passed an error if any (usually a unique constraint that was violated). ensureIndex can be called when you want, even after some data was inserted, though it's best to call it at application startup. 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.

Note: the _id is automatically indexed with a unique constraint, no need to call ensureIndex on it.

You can remove a previously created index with datastore.removeIndex(fieldName, cb).

If your datastore is persistent, the indexes you created are persisted in the datafile, when you load the database a second time they are automatically created for you. No need to remove any ensureIndex though, if it is called on a database that already has the index, nothing happens.

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({ somefield: 'nedb' }, function (err) {
    // err is { errorType: 'uniqueViolated'
    //        , key: 'name'
    //        , message: 'Unique constraint violated for key name' }

// Remove index on field somefield
db.removeIndex('somefield', function (err) {

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).

Browser version

As of v0.8.0, you can use NeDB in the browser! You can find it and its minified version in the repository, in the browser-version/out directory. You only need to require nedb.js or nedb.min.js in your HTML file and the global object Nedb can be used right away, with the same API as the server version:

<script src="nedb.min.js"></script>
  var db = new Nedb();   // Create an in-memory only datastore
  db.insert({ planet: 'Earth' });
  db.insert({ planet: 'Mars' });

  db.find({}, function (err, docs) {
    // docs contains the two planets Earth and Mars

It has been tested and is compatible with Chrome, Safari, Firefox, IE 10, IE 9. Please open an issue if you need compatibility with IE 8/IE 7, I think it will need some work and am not sure it is needed, since most complex webapplications - the ones that would need NeDB - only work on modern browsers anyway. To launch the tests, simply open the file browser-version/test/index.html in a browser and you'll see the results of the tests for this browser.

If you fork and modify nedb, you can build the browser version from the sources, the build script is browser-version/build.js.

As of v0.11, NeDB is also persistent on the browser. To use this, simply create the collection with the filename option which will be the name of the localStorage variable storing data. Persistence should work on all browsers where NeDB works. Also, keep in mind that localStorage has size constraints, so it's probably a good idea to set recurring compaction every 2-5 minutes to save on space if your client app needs a lot of updates and deletes. See database compaction for more details on the append-only format used by NeDB.

Browser persistence is still young! It has been tested on most major browsers but please report any bugs you find



NeDB is not intended to be a replacement of large-scale databases such as MongoDB, and as such 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, with indexing:

  • Insert: 5,950 ops/s
  • Find: 25,440 ops/s
  • Update: 4,490 ops/s
  • Remove: 6,620 ops/s

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 mostly use NeDB for logging purposes and don't want the memory footprint of your application to grow too large, you can use NeDB Logger to insert documents in a NeDB-readable database
  • 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

Help out

Issues reporting and pull requests are always appreciated. For issues, make sure to always include a code snippet and describe the expected vs actual behavior. If you send a pull request, make sure to stick to NeDB's coding style and always test all the code you submit. You can look at the current tests to see how to do it


You don't have time? You can support NeDB by sending bitcoins to this adress:


See License