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The JavaScript Database

:warning: :warning: :warning: WARNING: this library is no longer maintained, and may have bugs and security issues. Feel free to fork but no pull request or security alert will be answered.

Embedded persistent or in memory database for Node.js, nw.js, Electron and browsers, 100% JavaScript, no binary dependency. API is a subset of MongoDB's and it's <a href="#speed">plenty fast</a>.

IMPORTANT NOTE: Please don't submit issues for questions regarding your code. Only actual bugs or feature requests will be answered, all others will be closed without comment. Also, please follow the <a href="#bug-reporting-guidelines">bug reporting guidelines</a> and check the <a href="https://github.com/louischatriot/nedb/wiki/Change-log" target="_blank">change log</a> before submitting an already fixed bug :)

Support NeDB development

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No time to <a href="#pull-requests">help out</a>? You can support NeDB development by sending money or bitcoins!

Money: Donate to author

Bitcoin address: 1dDZLnWpBbodPiN8sizzYrgaz5iahFyb1

Installation, tests

Module name on npm and bower is nedb.

npm install nedb --save    # Put latest version in your package.json
npm test                   # You'll need the dev dependencies to launch tests
bower install nedb         # For the browser versions, which will be in browser-version/out

API

It is a subset of MongoDB's API (the most used operations).

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:

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.

Also, if loadDatabase fails, all commands registered to the executor afterwards will not be executed. They will be registered and executed, in sequence, only after a successful loadDatabase.

// 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)
db.users.loadDatabase();
db.robots.loadDatabase();

Persistence

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, for performance reasons. The database is automatically compacted (i.e. put back in the one-line-per-document format) every time you load each database within your application.

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. The datastore will fire a compaction.done event once compaction is finished.

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 a typical development machine) and no other operation can happen when it does, so most projects actually don't need to use it.

Compaction will also immediately remove any documents whose data line has become corrupted, assuming that the total percentage of all corrupted documents in that database still falls below the specified corruptAlertThreshold option's value.

Durability works similarly to major databases: compaction forces the OS to physically flush data to disk, while appends to the data file do not (the OS is responsible for flushing the data). That guarantees that a server crash can never cause complete data loss, while preserving performance. The worst that can happen is a crash between two syncs, causing a loss of all data between the two syncs. Usually syncs are 30 seconds appart so that's at most 30 seconds of data. <a href="http://oldblog.antirez.com/post/redis-persistence-demystified.html" target="_blank">This post by Antirez on Redis persistence</a> explains this in more details, NeDB being very close to Redis AOF persistence with appendfsync option set to no.

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({ "completeData.planets.name": "Mars" }, function (err, docs) {
  // docs contains document 5
});

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

db.find({ "completeData.planets.0.name": "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, $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 the query value is an array to perform an exact match, then whether there is an array-specific comparison function (for now there is only $size and $elemMatch) being used. If not, the query is treated as a query on every element and there is a match if at least one element matches.

// Exact match
db.find({ satellites: ['Phobos', 'Deimos'] }, function (err, docs) {
  // docs contains Mars
})
db.find({ satellites: ['Deimos', 'Phobos'] }, function (err, docs) {
  // docs is empty
})

// Using an array-specific comparison function
// $elemMatch operator will provide match for a document, if an element from the array field satisfies all the conditions specified with the `$elemMatch` operator
db.find({ completeData: { planets: { $elemMatch: { name: 'Earth', number: 3 } } } }, function (err, docs) {
  // docs contains documents with id 5 (completeData)
});

db.find({ completeData: { planets: { $elemMatch: { name: 'Earth', number: 5 } } } }, function (err, docs) {
  // docs is empty
});

// You can use inside #elemMatch query any known document query operator
db.find({ completeData: { planets: { $elemMatch: { name: 'Earth', number: { $gt: 2 } } } } }, function (err, docs) {
  // docs contains documents with id 5 (completeData)
});

// 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:

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!

Projections

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. You can project on nested documents.

// 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
db.find({ planet: 'Mars' }).projection({ planet: 1, system: 1 }).exec(function (err, docs) {
  // docs is [{ planet: 'Mars', system: 'solar', _id: 'id1' }]
});

// Project on a nested document
db.findOne({ planet: 'Earth' }).projection({ planet: 1, 'humans.genders': 1 }).exec(function (err, doc) {
  // doc is { planet: 'Earth', _id: 'id2', humans: { genders: 2 } }
});

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:

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: { data: { 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
  });
});

// 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']
});

// $slice can be used in cunjunction with $push and $each to limit the size of the resulting array.
// A value of 0 will update the array to an empty array. A positive value n will keep only the n first elements
// A negative value -n will keep only the last n elements.
// If $slice is specified but not $each, $each is set to []
db.update({ _id: 'id6' }, { $push: { fruits: { $each: ['banana'], $slice: 2 } } }, {}, function () {
  // Now the fruits array is ['apple', 'orange']
});

// $min/$max to update only if provided value is less/greater than current value
// Let's say the database contains this document
// doc = { _id: 'id', name: 'Name', value: 5 }
db.update({ _id: 'id1' }, { $min: { value: 2 } }, {}, function () {
  // The document will be updated to { _id: 'id', name: 'Name', value: 2 }
});

db.update({ _id: 'id1' }, { $min: { value: 8 } }, {}, function () {
  // The document will not be modified
});

Removing documents

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

// 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
});

// Removing all documents with the 'match-all' query
db.remove({}, { multi: true }, function (err, numRemoved) {
});

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 to speed up basic queries and queries using $in, $lt, $lte, $gt and $gte. The indexed values cannot be of type array of object.

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:

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) {
});

// Example of using expireAfterSeconds to remove documents 1 hour
// after their creation (db's timestampData option is true here)
db.ensureIndex({ fieldName: 'createdAt', expireAfterSeconds: 3600 }, function (err) {
});

// You can also use the option to set an expiration date like so
db.ensureIndex({ fieldName: 'expirationDate', expireAfterSeconds: 0 }, function (err) {
  // Now all documents will expire when system time reaches the date in their
  // expirationDate field
});

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

The browser version and its minified counterpart are 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>
<script>
  var db = new Nedb();   // Create an in-memory only datastore
  
  db.insert({ planet: 'Earth' }, function (err) {
   db.find({}, function (err, docs) {
     // docs contains the two planets Earth and Mars
   });
  });
</script>

If you specify a filename, the database will be persistent, and automatically select the best storage method available (IndexedDB, WebSQL or localStorage) depending on the browser. In most cases that means a lot of data can be stored, typically in hundreds of MB. WARNING: the storage system changed between v1.3 and v1.4 and is NOT back-compatible! Your application needs to resync client-side when you upgrade NeDB.

NeDB is compatible with all major browsers: Chrome, Safari, Firefox, IE9+. Tests are in the browser-version/test directory (files index.html and testPersistence.html).

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

Performance

Speed

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 a typical, not-so-fast dev machine, for a collection containing 10,000 documents, with indexing:

You can run these simple benchmarks 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).

Use in other services

Pull requests

Important: I consider NeDB to be feature-complete, i.e. it does everything I think it should and nothing more. As a general rule I will not accept pull requests anymore, except for bugfixes (of course) or if I get convinced I overlook a strong usecase. Please make sure to open an issue before spending time on any PR.

If you submit a pull request, thanks! There are a couple rules to follow though to make it manageable:

Bug reporting guidelines

If you report a bug, thank you! That said for the process to be manageable please strictly adhere to the following guidelines. I'll not be able to handle bug reports that don't:

Bitcoins

You don't have time? You can support NeDB by sending bitcoins to this address: 1dDZLnWpBbodPiN8sizzYrgaz5iahFyb1

License

See License