@seald-io/nedb

What is @seald-io/nedb?

@seald-io/nedb is a lightweight, embedded, and persistent database for Node.js applications. It is designed to be a simple and efficient solution for small to medium-sized applications that need a database without the overhead of a full-fledged database management system.

What are @seald-io/nedb's main functionalities?

Inserting Documents

This feature allows you to insert documents into the database. The `insert` method takes a document object and a callback function that handles the result of the insertion.

const Datastore = require('@seald-io/nedb');
const db = new Datastore({ filename: 'path/to/datafile', autoload: true });

const doc = { name: 'Alice', age: 25, city: 'Wonderland' };
db.insert(doc, function (err, newDoc) {
  if (err) console.error(err);
  else console.log('Inserted:', newDoc);
});

Finding Documents

This feature allows you to query the database for documents that match a given criteria. The `find` method takes a query object and a callback function that handles the result of the query.

db.find({ age: 25 }, function (err, docs) {
  if (err) console.error(err);
  else console.log('Found:', docs);
});

Updating Documents

This feature allows you to update documents in the database. The `update` method takes a query object, an update object, options, and a callback function that handles the result of the update.

db.update({ name: 'Alice' }, { $set: { age: 26 } }, {}, function (err, numReplaced) {
  if (err) console.error(err);
  else console.log('Updated:', numReplaced);
});

Removing Documents

This feature allows you to remove documents from the database. The `remove` method takes a query object, options, and a callback function that handles the result of the removal.

db.remove({ name: 'Alice' }, {}, function (err, numRemoved) {
  if (err) console.error(err);
  else console.log('Removed:', numRemoved);
});

Other packages similar to @seald-io/nedb

nedb

nedb is the original package from which @seald-io/nedb is forked. It provides similar functionality as a lightweight, embedded database for Node.js. The main difference is that @seald-io/nedb is a maintained fork with additional features and bug fixes.

lowdb

lowdb is a small local JSON database for Node.js, which is ideal for small projects, CLI tools, and scripts. It offers a simple API and is easy to use, but it does not support advanced querying and indexing like @seald-io/nedb.

sqlite3

sqlite3 is a binding for SQLite, a self-contained, high-reliability, embedded, full-featured, public-domain SQL database engine. It is more powerful and supports SQL queries, but it is also more complex and heavier compared to @seald-io/nedb.

lokijs

lokijs is a fast, in-memory, document-oriented database for Node.js. It is designed for performance and can be used for applications that require high-speed data operations. However, it is primarily in-memory and may not be as suitable for persistent storage as @seald-io/nedb.

README

The JavaScript Database

This module is a fork of nedb written by Louis Chatriot.

Since the original maintainer doesn't support this package anymore, we forked it and maintain it for the needs of Seald.

Embedded persistent or in memory database for Node.js, Electron and browsers, 100% JavaScript, no binary dependency. API is a subset of MongoDB's and it's plenty fast.

Installation

Module name on npm is @seald-io/nedb.

npm install @seald-io/nedb

Then to import, you just have to:

const Datastore = require('@seald-io/nedb')

Documentation

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

Since version 3.0.0, NeDB provides a Promise-based equivalent for each function which is suffixed with Async, for example loadDatabaseAsync.

The original callback-based interface is still available, fully retro-compatible (as far as the test suites can tell) and are a shim to this Promise-based version.

Don't hesitate to open an issue if it breaks something in your project.

The rest of the readme will only show the Promise-based API, the full documentation is available in the API.md file at the root of the repository. It is generated by running npm run generateDocs:markdown.

• Creating/loading a database
• Dropping a database
• Persistence
• Inserting documents
• Finding documents
  • Basic Querying
  • Operators ($lt, $lte, $gt, $gte, $in, $nin, $ne, $exists, $regex)
  • Array fields
  • Logical operators $or, $and, $not, $where
  • Sorting and paginating
  • Projections
• Counting documents
• Updating documents
• Removing documents
• Indexing
• Other environments

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.

If the Datastore is persistent (if you give it options.filename, you'll need to load the database using Datastore#loadDatabaseAsync, or using options.autoload.

// Type 1: In-memory only datastore (no need to load the database)
const Datastore = require('@seald-io/nedb')
const db = new Datastore()

// Type 2: Persistent datastore with manual loading
const Datastore = require('@seald-io/nedb')
const db = new Datastore({ filename: 'path/to/datafile' })
try {
  await db.loadDatabaseAsync()
} catch (error) {
  // loading has failed
}
// loading has succeeded
 
// Type 3: Persistent datastore with automatic loading
const Datastore = require('@seald-io/nedb')
const db = new Datastore({ filename: 'path/to/datafile', autoload: true }) // You can await db.autoloadPromise to catch a potential error when autoloading.
// You can issue commands right away

// 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
await db.users.loadDatabaseAsync()
await db.robots.loadDatabaseAsync()

Dropping a database

Since v3.0.0, you can drop the database by using Datastore#dropDatabaseAsync:

const Datastore = require('@seald-io/nedb')
const db = new Datastore()
await d.insertAsync({ hello: 'world' })
await d.dropDatabaseAsync()
assert.equal(d.getAllData().length, 0)
assert.equal(await exists(testDb), false)

It is not recommended to keep using an instance of Datastore when its database has been dropped as it may have some unintended side effects.

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.

Breaking change: since v3.0.0, calling methods of yourDatabase.persistence is deprecated. The same functions exists directly on the Datastore.

You can manually call the compaction function with yourDatabase#compactDatafileAsync.

You can also set automatic compaction at regular intervals with yourDatabase#setAutocompactionInterval, and stop automatic compaction with yourDatabase#stopAutocompaction.

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 generate one for you (a 16-characters alphanumerical string). The _id of a document, once set, cannot be modified.

Field names cannot start with '$' or contain the characters '.' and ','.

const doc = {
  hello: 'world',
  n: 5,
  today: new Date(),
  nedbIsAwesome: true,
  notthere: null,
  notToBeSaved: undefined,  // Will not be saved
  fruits: ['apple', 'orange', 'pear'],
  infos: { name: '@seald-io/nedb' }
}

try {
  const newDoc = await db.insertAsync(doc)
  // newDoc is the newly inserted document, including its _id
  // newDoc has no key called notToBeSaved since its value was undefined
} catch (error) {
  // if an error happens
}

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.

const newDocs = await db.insertAsync([{ a: 5 }, { a: 42 }])
// 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
try {
  await db.insertAsync([{ a: 5 }, { a: 42 }, { a: 5 }])
} catch (error) {
  // err is a 'uniqueViolated' error
  // The database was not modified
}

Finding documents

Use findAsync to look for multiple documents matching you query, or findOneAsync 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
const docs = await db.findAsync({ system: 'solar' })
// 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
const docs = await db.findAsync({ planet: /ar/ })
// docs contains Mars and Earth

// Finding all inhabited planets in the solar system
const docs = await db.findAsync({ system: 'solar', inhabited: true })
// docs is an array containing document Earth only

// Use the dot-notation to match fields in subdocuments
const docs = await db.findAsync({ 'humans.genders': 2 })
// docs contains Earth


// Use the dot-notation to navigate arrays of subdocuments
const docs = await db.findAsync({ 'completeData.planets.name': 'Mars' })
// docs contains document 5

const docs = await db.findAsync({ 'completeData.planets.name': 'Jupiter' })
// docs is empty

const docs = await db.findAsync({ 'completeData.planets.0.name': 'Earth' })
// 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!
const docs = await db.findAsync({ humans: { genders: 2 } })
// docs is empty, because { genders: 2 } is not equal to { genders: 2, eyes: true }


// Find all documents in the collection
const docs = await db.findAsync({})

// The same rules apply when you want to only find one document
const doc = await db.findOneAsync({ _id: 'id1' })
// 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
const docs = await db.findAsync({ 'humans.genders': { $gt: 5 } })
// docs contains Omicron Persei 8, whose humans have more than 5 genders (7).

// When used with strings, lexicographical order is used
const docs = await db.findAsync({ planet: { $gt: 'Mercury' } })
// docs contains Omicron Persei 8

// Using $in. $nin is used in the same way
const docs = await db.findAsync({ planet: { $in: ['Earth', 'Jupiter'] } })
// docs contains Earth and Jupiter

// Using $exists
const docs = await db.findAsync({ satellites: { $exists: true } })
// docs contains only Mars

// Using $regex with another operator
const docs = await db.findAsync({
  planet: {
    $regex: /ar/,
    $nin: ['Jupiter', 'Earth']
  }
})
// 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.

• $size: match on the size of the array
• $elemMatch: matches if at least one array element matches the query entirely

// Exact match
const docs = await db.findAsync({ satellites: ['Phobos', 'Deimos'] })
// docs contains Mars

const docs = await db.findAsync({ satellites: ['Deimos', 'Phobos'] })
// 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
const docs = await db.findAsync({
  completeData: {
    planets: {
      $elemMatch: {
        name: 'Earth',
        number: 3
      }
    }
  }
})
// docs contains documents with id 5 (completeData)

const docs = await db.findAsync({
  completeData: {
    planets: {
      $elemMatch: {
        name: 'Earth',
        number: 5
      }
    }
  }
})
// docs is empty

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

// Note: you can't use nested comparison functions, e.g. { $size: { $lt: 5 } } will throw an error
const docs = await db.findAsync({ satellites: { $size: 2 } })
// docs contains Mars

const docs = await db.findAsync({ satellites: { $size: 1 } })
// docs is empty

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

// This also works for queries that use comparison operators
const docs = await db.findAsync({ satellites: { $lt: 'Amos' } })
// docs is empty since Phobos and Deimos are after Amos in lexicographical order

// This also works with the $in and $nin operator
const docs = await db.findAsync({ satellites: { $in: ['Moon', 'Deimos'] } })
// 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 */ } }

const docs = await db.findAsync({ $or: [{ planet: 'Earth' }, { planet: 'Mars' }] })
// docs contains Earth and Mars

const docs = await db.findAsync({ $not: { planet: 'Earth' } })
// docs contains Mars, Jupiter, Omicron Persei 8

const docs = await db.findAsync({ $where: function () { return Object.keys(this) > 6 } })
// docs with more than 6 properties

// You can mix normal queries, comparison queries and logical operators
const docs = await db.findAsync({
  $or: [{ planet: 'Earth' }, { planet: 'Mars' }],
  inhabited: true
})
// docs contains Earth

Sorting and paginating

Datastore#findAsync, Datastore#findOneAsync and Datastore#countAsync don't actually return a Promise, but a Cursor which is a Thenable which calls Cursor#execAsync when awaited.

This pattern allows to chain Cursor#sort, Cursor#skip, Cursor#limit and Cursor#projection and await the result.

// 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)
const docs = await db.findAsync({}).sort({ planet: 1 }).skip(1).limit(2)
// docs is [doc3, doc1]

// You can sort in reverse order like this
const docs = await db.findAsync({ system: 'solar' }).sort({ planet: -1 })
// docs is [doc1, doc3, doc2]

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

Projections

You can give findAsync and findOneAsync 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
const docs = await db.findAsync({ planet: 'Mars' }, { planet: 1, system: 1 })
// docs is [{ planet: 'Mars', system: 'solar', _id: 'id1' }]

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

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

// Failure: using both modes at the same time
const docs = await db.findAsync({ planet: 'Mars' }, { planet: 0, system: 1 })
// 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
const docs = await db.findAsync({ planet: 'Mars' }).projection({
  planet: 1,
  system: 1
})
// docs is [{ planet: 'Mars', system: 'solar', _id: 'id1' }]

// Project on a nested document
const doc = await db.findOneAsync({ planet: 'Earth' }).projection({
  planet: 1,
  'humans.genders': 1
})
// doc is { planet: 'Earth', _id: 'id2', humans: { genders: 2 } }

Counting documents

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

// Count all planets in the solar system
const count = await db.countAsync({ system: 'solar' })
// count equals to 3

// Count all documents in the datastore
const count = await db.countAsync({})
// count equals to 4

Updating documents

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

update specifies how the documents should be modified. It is either a new document or a set of modifiers (you cannot use both together):

• A new document will replace the matched docs;
• Modifiers create the fields they need to modify if they don't exist, and you can apply them to subdocs (see the API reference)

options is an object with three possible parameters:

• multi which allows the modification of several documents if set to true.
• upsert will insert a new document corresponding if it doesn't exist (either the update is a simple object with no modifiers, or the query modified by the modifiers in the update) if set to true.
• returnUpdatedDocs will return the array of documents matched by the find query and updated (updated documents will be returned even if the update did not actually modify them) if set to true.

It resolves into an Object with the following properties:

• numAffected: how many documents were affected by the update;
• upsert: if a document was actually upserted (not always the same as options.upsert;
• affectedDocuments:
  • if upsert is true the document upserted;
  • if options.returnUpdatedDocs is true either the affected document or, if options.multi is true an Array of the affected documents, else null;

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
const { numReplaced } = await db.updateAsync({ planet: 'Jupiter' }, { planet: 'Pluton' }, {})
// 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
const { numReplaced } = await db.updateAsync({ system: 'solar' }, { $set: { system: 'solar system' } }, { multi: true })
// 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
await db.updateAsync({ planet: 'Mars' }, {
  $set: {
    'data.satellites': 2,
    'data.red': true
  }
}, {})
// 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
await db.updateAsync({ planet: 'Mars' }, { $set: { data: { satellites: 3 } } }, {})
// 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
await db.updateAsync({ planet: 'Mars' }, { $unset: { planet: true } }, {})
// 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
const { numReplaced, affectedDocuments, upsert } = await db.updateAsync({ planet: 'Pluton' }, {
  planet: 'Pluton',
  inhabited: false
}, { upsert: true })
// numReplaced = 1, affectedDocuments = { _id: 'id5', planet: 'Pluton', inhabited: false }, 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 :)
await db.updateAsync({ planet: 'Pluton' }, { $inc: { distance: 38 } }, { upsert: true })
// 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
await db.updateAsync({ _id: 'id6' }, { $push: { fruits: 'banana' } }, {})
// 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
await db.updateAsync({ _id: 'id6' }, { $pop: { fruits: 1 } }, {})
// 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
await db.updateAsync({ _id: 'id6' }, { $addToSet: { fruits: 'apple' } }, {})
// 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
await db.updateAsync({ _id: 'id6' }, { $pull: { fruits: 'apple' } }, {})
// Now the fruits array is ['orange', 'pear']

await db.updateAsync({ _id: 'id6' }, { $pull: { fruits: { $in: ['apple', 'pear'] } } }, {})
// 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
await db.updateAsync({ _id: 'id6' }, { $push: { fruits: { $each: ['banana', 'orange'] } } }, {})
// 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 []
await db.updateAsync({ _id: 'id6' }, {
  $push: {
    fruits: {
      $each: ['banana'],
      $slice: 2
    }
  }
})
// 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 }
await db.updateAsync({ _id: 'id1' }, { $min: { value: 2 } }, {})
// The document will be updated to { _id: 'id', name: 'Name', value: 2 }


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

Removing documents

db.removeAsync(query, options) will remove documents matching query. Can remove multiple documents if options.multi is set. Returns the Promise<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
const { numRemoved } = await db.removeAsync({ _id: 'id2' }, {})
// numRemoved = 1


// Remove multiple documents
const { numRemoved } = await db.removeAsync({ system: 'solar' }, { multi: true })
// numRemoved = 3
// All planets from the solar system were removed


// Removing all documents with the 'match-all' query
const { numRemoved } = await db.removeAsync({}, { multi: true })

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.

Breaking change: since v4.0.0, commas (,) can no longer be used in indexed field names.

The following is illegal:

db.ensureIndexAsync({ fieldName: 'some,field' })
db.ensureIndexAsync({ fieldName: ['some,field', 'other,field'] })

This is a side effect of the compound index implementation.

To create an index, use datastore#ensureIndexAsync(options). It resolves when the index is persisted on disk (if the database is persistent) and may throw an Error (usually a unique constraint that was violated). It 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. For a compound index, use an array of field names.
• unique (optional, defaults to false): enforce field uniqueness.
• sparse (optional, defaults to false): don't index documents for which the field is not defined.
• expireAfterSeconds (number of seconds, optional): if set, the created index is a TTL (time to live) index, that will automatically remove documents when the indexed field value is older than expireAfterSeconds.

Note: the _id is automatically indexed with a unique constraint.

You can remove a previously created index with datastore#removeIndexAsync(fieldName).

try {
  await db.ensureIndexAsync({ fieldName: 'somefield' })
} catch (error) {
  // If there was an error, error is not null
}

// Using a unique constraint with the index
await db.ensureIndexAsync({ fieldName: 'somefield', unique: true })

// Using a sparse unique index
await db.ensureIndexAsync({
  fieldName: 'somefield',
  unique: true,
  sparse: true
})

// Using a compound index
await db.ensureIndexAsync({ fieldName: ["field1", "field2"] });

try {
  // Format of the error message when the unique constraint is not met
  await db.insertAsync({ somefield: '@seald-io/nedb' })
  // works
  await db.insertAsync({ somefield: '@seald-io/nedb' })
  //rejects
} catch (error) {
  // error is { errorType: 'uniqueViolated',
  //            key: 'name',
  //            message: 'Unique constraint violated for key name' }
}


// Remove index on field somefield
await db.removeIndexAsync('somefield')

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

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

Other environments

NeDB runs on Node.js (it is tested on Node 12, 14 and 16), the browser (it is tested on the latest version of Chrome) and React-Native using @react-native-async-storage/async-storage.

Browser bundle

The npm package contains a bundle and its minified counterpart for the browser. They are located 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 using localforage (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 uses modern Javascript features such as async, Promise, class, const , let, Set, Map, ... The bundle does not polyfill these features. If you need to target another environment, you will need to make your own bundle.

Using the browser and react-native fields

NeDB uses the browser and react-native fields to replace some modules by an environment specific shim.

The way this works is by counting on the bundler that will package NeDB to use this fields. This is done by default by Webpack for the browser field. And this is done by default by Metro for the react-native field.

This is done for:

• the storage module which uses Node.js fs. It is replaced in the browser by one that uses localforage, and in react-native by one that uses @react-native-async-storage/async-storage
• the customUtils module which uses Node.js crypto module. It is replaced by a good enough shim to generate ids that uses Math.random().
• the byline module which uses Node.js stream (a fork of node-byline included in the repo because it is unmaintained). It isn't used in the browser nor react-native versions, therefore it is shimmed with an empty object.

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:

• Insert: 10,680 ops/s
• Find: 43,290 ops/s
• Update: 8,000 ops/s
• Remove: 11,750 ops/s

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

• An ODM for NeDB: follicle
• A layer to add a promise-only interface: nedb-promises

Modernization

This fork of NeDB will be incrementally updated to:

• cleanup the benchmark and update the performance statistics;
• remove deprecated features;
• add a way to change the Storage module by dependency injection, which will pave the way to cleaner browser react-native versions (cf https://github.com/seald/nedb/pull/19).
• use async functions and Promises instead of callbacks with async@0.2.6;
• expose a Promise-based interface;
• remove the underscore dependency;

Pull requests guidelines

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

• The pull request should be atomic, i.e. contain only one feature. If it contains more, please submit multiple pull requests. Reviewing massive, 1000 loc+ pull requests is extremely hard.
• Likewise, if for one unique feature the pull request grows too large (more than 200 loc tests not included), please get in touch first.
• Please stick to the current coding style. It's important that the code uses a coherent style for readability (this package uses standard).
• Do not include stylistic improvements ('housekeeping'). If you think one part deserves lots of housekeeping, use a separate pull request so as not to pollute the code.
• Don't forget tests for your new feature. Also don't forget to run the whole test suite before submitting to make sure you didn't introduce regressions.
• Update the JSDoc and regenerate the markdown docs.
• Update the readme accordingly.

License

See License

Changelog

[4.0.2] - 2023-05-05

Fixed

• Fixed typo in documentation #36