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https://github.com/antoniopresto/react-native-local-mongodb

react-native local mongodb storage.
https://github.com/antoniopresto/react-native-local-mongodb

Last synced: about 2 months ago
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react-native local mongodb storage.

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README 

        
**Embedded persistent or in memory database for react-native**. API is a subset of MongoDB's (NeDB).



## Installation, tests

Module name on npm is `react-native-local-mongodb`.



#

> Starting with version 2.0.0, the `insert`,` update`, `remove`,` find`, and `findOne` methods no longer return promises - now they have a corresponding `Async` method (insertAsync, updateAsync, etc )

#



```

npm install react-native-local-mongodb --save    # Put latest version in your package.json

npm test                   # You'll need the dev dependencies to launch tests

```



## API

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



* Creating/loading a database

* Persistence

* Using a custom storage 

* 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

* Browser version



### Creating/loading a database

You can use react-native-local-mongodb 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): the key where AsyncStorage will save data.

* `inMemoryOnly` (optional, defaults to `false`): as the name implies.

* `timestampData` (optional, defaults to `false`): timestamp the insertion and last update of all documents, with the fields `createdAt` and `updatedAt`. User-specified values override automatic generation, usually useful for testing.

* `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.

* `afterSerialization` (optional): hook you can use to transform data after it was serialized and before it is written to disk. Can be used for example to encrypt data before writing database to disk. This function takes a string as parameter (one line of an react-native-local-mongodb data file) and outputs the transformed string, **which must absolutely not contain a `\n` character** (or data will be lost).

* `beforeDeserialization` (optional): inverse of `afterSerialization`. Make sure to include both and not just one or you risk data loss. For the same reason, make sure both functions are inverses of one another. Some failsafe mechanisms are in place to prevent data loss if you misuse the serialization hooks: react-native-local-mongodb checks that never one is declared without the other, and checks that they are reverse of one another by testing on random strings of various lengths. In addition, if too much data is detected as corrupt, react-native-local-mongodb will refuse to start as it could mean you're not using the deserialization hook corresponding to the serialization hook used before (see below).

* `corruptAlertThreshold` (optional): between 0 and 1, defaults to 10%. react-native-local-mongodb will refuse to start if more than this percentage of the datafile is corrupt. 0 means you don't tolerate any corruption, 1 means you don't care.

* `compareStrings` (optional): function compareStrings(a, b) compares

  strings a and b and return -1, 0 or 1. If specified, it overrides

default string comparison which is not well adapted to non-US characters

in particular accented letters. Native `localCompare` will most of the

time be the right choice



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`.



```javascript

// Type 1: In-memory only datastore (no need to load the database)

var Datastore = require('react-native-local-mongodb')

  , db = new Datastore();



// Type 2: Persistent datastore with manual loading

const Datastore = require('react-native-local-mongodb')

  , db = new Datastore({ filename: 'asyncStorageKey', storage: AsyncStorage });

  

db.loadDatabase(function (err) {    // Callback is optional

  // Now commands will be executed

});



// Type 3: Persistent datastore with automatic loading

var Datastore = require('react-native-local-mongodb')

  , db = new Datastore({ filename: 'asyncStorageKey', storage: AsyncStorage, autoload: true });

// You can issue commands right away

```



### Persistence

Under the hood, react-native-local-mongodb's persistence uses an append-only format, meaning that all updates and deletes actually result in lines added at the end of the string in AsyncStorage, 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. This post by Antirez on Redis persistence explains this in more details, react-native-local-mongodb being very close to Redis AOF persistence with `appendfsync` option set to `no`.



### Using a custom Storage

You can pass a custom storage in the Datastore configuration object. Your storage must be an object that matches [this interface](https://github.com/react-native-community/react-native-async-storage/blob/67ae214ad5caebc83bb21e659e898924cb27fe6e/types/index.d.ts#L19).



```javascript

import Datastore from 'react-native-local-mongodb';

import AsyncStorage from '@react-native-community/async-storage';



const db = new Datastore({

  filename: 'asyncStorageKey',

  storage: AsyncStorage

});



// === or ===



const items = {};



const db2 = new Datastore({

  filename: 'asyncStorageKey',

  

  // custom storage

  storage: {

    setItem: (key, value, cb) => {

      items[key] = value;

      cb(null, value);

    },



    getItem: (key, cb) => {

      const res = items[key];

      cb(null, res);

    },



    removeItem: (key, cb) => {

      const res = delete items[key];

      cb(null, res);

    },

    

    // ...

  },

});

```



### 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, react-native-local-mongodb will automatically generate 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 '.'.



```javascript

var doc = { hello: 'world'

               , n: 5

               , today: new Date()

               , react-native-local-mongodbIsAwesome: true

               , notthere: null

               , notToBeSaved: undefined  // Will not be saved

               , fruits: [ 'apple', 'orange', 'pear' ]

               , infos: { name: 'react-native-local-mongodb' }

               };



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.



```javascript

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.



```javascript

// 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`: 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)



```javascript

// $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, react-native-local-mongodb 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



```javascript

// 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:



* 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 */ } }`



```javascript

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) - a promise is returned`.



```javascript

// 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.



```javascript

// 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:



```javascript

// 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, `$inc` to increment a field's value and `$min`/`$max` to change field's value, only if provided value is less/greater than current value. To work on arrays, you have `$push`, `$pop`, `$addToSet`, `$pull`, and the special `$each` and `$slice`. 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. If your `update` is a simple object with no modifiers, it is the inserted document. In the other case, the `query` is stripped from all operator recursively, and the `update` is applied to it.

  * `returnUpdatedDocs` (defaults to `false`, not MongoDB-compatible) if set to true and update is not an upsert, 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

* `callback` (optional) signature: `(err, numAffected, affectedDocuments, upsert)`. **Warning**: the API was changed between v1.7.4 and v1.8. Please refer to the change log to see the change.

  * For an upsert, `affectedDocuments` contains the inserted document and the `upsert` flag is set to `true`.

  * For a standard update with `returnUpdatedDocs` flag set to `false`, `affectedDocuments` is not set.

  * For a standard update with `returnUpdatedDocs` flag set to `true` and `multi` to `false`, `affectedDocuments` is the updated document.

  * For a standard update with `returnUpdatedDocs` flag set to `true` and `multi` to `true`, `affectedDocuments` is the array of updated documents.



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



```javascript

// 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 react-native-local-mongodb 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`

* `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



```javascript

// 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

react-native-local-mongodb 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:



* **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.

* **expireAfterSeconds** (number of seconds, optional): if set, the created index is a TTL (time to live) index, that will automatically remove documents when the system date becomes larger than the date on the indexed field plus `expireAfterSeconds`. Documents where the indexed field is not specified or not a `Date` object are ignored



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.



```javascript

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: 'react-native-local-mongodb' }, function (err) {

  // err is null

  db.insert({ somefield: 'react-native-local-mongodb' }, 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).



## License



See [License](LICENSE)