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morefun-forerunnerdb

ForerunnerDB - A NoSQL JSON Document DB

ForerunnerDB is developed with ❤ love by Irrelon Software Limited, a UK registered company.

ForerunnerDB is used in live projects that serve millions of users a day, is production ready and battle tested in real-world applications.

Version 1.3.806

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NPM Stats

TravisCI Build Test Status

Master Dev

Standout Features

  • Views - Virtual collections that are built from existing collections and limited by live queries.
  • Joins - Query with joins across multiple collections and views.
  • Sub-Queries - ForerunnerDB supports sub-queries across collections and views.
  • Collection Groups - Add collections to a group and operate CRUD on them as a single entity.
  • Data Binding (Browser Only) - Optional binding module to bind data to your DOM and have it update your page in realtime as data changes.
  • Persistent Storage (Browser & Node.js) - Optional persist module to save your data and load it back at a later time, great for multi-page apps.
  • Compression & Encryption - Support for compressing and encrypting your persisted data.
  • Built-In REST Server (Node.js) - Optional REST server with powerful access control, remote procedures, access collections, views etc via REST interface. Rapid prototyping is made very easy with ForerunnerDB server-side.
  • AngularJS and Ionic Support - Optional AngularJS module provides ForerunnerDB as an angular service.

What is ForerunnerDB

ForerunnerDB is a NoSQL JavaScript JSON database with a query language based on MongoDB (with some differences) and runs on browsers and Node.js. It is in use in many large production web applications and is transparently used by over 6 million clients. ForerunnerDB is the most advanced, battle-tested and production ready browser-based JSON database system available today.

What is ForerunnerDB's Primary Use Case?

ForerunnerDB was created primarily to allow web (and mobile web / hybrid) application developers to easily store, query and manipulate JSON data in the browser / mobile app via a simple query language, making handling JSON data significantly easier.

ForerunnerDB supports data persistence on both the client (via LocalForage) and in Node.js (by saving and loading JSON data files).

If you build advanced web applications with AngularJS or perhaps your own framework or if you are looking to build a server application / API that needs a fast queryable in-memory store with file-based data persistence and a very easy setup (simple installation via NPM and no requirements except Node.js) you will also find ForerunnerDB very useful.

An example hybrid application that runs on iOS, Android and Windows Mobile via Ionic (AngularJS + Cordova with some nice extensions) is available in this repository under the ionicExampleClient folder. See here for more details.

Download

NPM

If you are using Node.js (or have it installed) you can use NPM to download ForerunnerDB via:

npm install forerunnerdb

NPM Dev Builds

You can also install the development version which usually includes new features that are considered either unstable or untested. To install the development version you can ask NPM for the dev tag:

npm install forerunnerdb --tag dev

Bower

You can also install ForerunnerDB via the bower package manager:

bower install forerunnerdb

No Package Manager

If you are still a package manager hold-out or you would prefer a more traditional download, please click here.

How to Use

Use ForerunnerDB in Browser

fdb-all.min.js is the entire ForerunnerDB with all the added extras. If you prefer only the core database functionality (just collections, no views etc) you can use fdb-core.min.js instead. A list of the different builds is available for you to select the best build for your purposes.

Include the fdb-all.min.js file in your HTML (change path to the location you put forerunner):

<script src="./js/dist/fdb-all.min.js" type="text/javascript"></script>

Use ForerunnerDB in Node.js

After installing via npm (see above) you can require ForerunnerDB in your code:

var ForerunnerDB = require("forerunnerdb");
var fdb = new ForerunnerDB();

Create a Database

var db = fdb.db("myDatabaseName");

If you do not specify a database name a randomly generated one is provided instead.

Collections (Tables)

Data Binding: Enabled

To create or get a reference to a collection object, call db.collection (where collectionName is the name of your collection):

var collection = db.collection("collectionName");

In our examples we will use a collection called "item" which will store some fictitious items for sale:

var itemCollection = db.collection("item");

Auto-Creation

When you request a collection that does not yet exist it is automatically created. If it already exists you are given the reference to the existing collection. If you want ForerunnerDB to throw an error if a collection is requested that does not already exist you can pass an option to the collection() method instead:

var collection = db.collection("collectionName", {autoCreate: false});

Specifying a Primary Key Up-Front

If no primary key is specified ForerunnerDB uses "_id" by default.

On requesting a collection you can specify a primary key that the collection should be using. For instance to use a property called "name" as the primary key field:

var collection = db.collection("collectionName", {primaryKey: "name"});

You can also read or specify a primary key after instantiation via the primaryKey() method.

Capped Collections

Occasionally it is useful to create a collection that will store a finite number of records. When that number is reached, any further documents inserted into the collection will cause the oldest inserted document to be removed from the collection on a first-in-first-out rule (FIFO).

In this example we create a capped collection with a document limit of 5:

var collection = db.collection("collectionName", {capped: true, size: 5});

Inserting Documents

If you do not specify a value for the primary key, one will be automatically generated for any documents inserted into a collection. Auto-generated primary keys are pseudo-random 16 character strings.

PLEASE NOTE: When doing an insert into a collection, ForerunnerDB will automatically split the insert up into smaller chunks (usually of 100 documents) at a time to ensure the main processing thread remains unblocked. If you wish to be informed when the insert operation is complete you can pass a callback method to the insert call. Alternatively you can turn off this behaviour by calling yourCollection.deferredCalls(false);

You can either insert a single document object:

itemCollection.insert({
    _id: 3,
    price: 400,
    name: "Fish Bones"
});

or pass an array of documents:

itemCollection.insert([{
    _id: 4,
    price: 267,
    name:"Scooby Snacks"
}, {
    _id: 5,
    price: 234,
    name: "Chicken Yum Yum"
}]);

Inserting a Large Number of Documents

When inserting large amounts of documents ForerunnerDB may break your insert operation into multiple smaller operations (usually of 100 documents at a time) in order to avoid blocking the main processing thread of your browser / Node.js application. You can find out when an insert has completed either by passing a callback to the insert call or by switching off async behaviour.

Passing a callback:

itemCollection.insert([{
    _id: 4,
    price: 267,
    name:"Scooby Snacks"
}, {
    _id: 5,
    price: 234,
    name: "Chicken Yum Yum"
}], function (result) {
    // The result object will contain two arrays (inserted and failed) 
    // which represent the documents that did get inserted and those 
    // that didn't for some reason (usually index violation). Failed 
    // items also contain a reason. Inspect the failed array for further 
    // information. 
});

If you wish to switch off async behaviour you can do so on a per-collection basis via:

db.collection('myCollectionName').deferredCalls(false);

After async behaviour (deferred calls) has been disabled, you can insert records and be sure that they will all have inserted before the next statement is processed by the application's main thread.

Inserting Special Objects

JSON has limitations on the types of objects it will serialise and de-serialise back to an object. Two very good examples of this are the Date() and RegExp() objects. Both can be serialised via JSON.stringify() but when calling JSON.parse() on the serialised version neither type will be "re-materialised" back to their object representations.

For example:

var a = {
    dt: new Date()
};
 
a.dt instanceof Date; // true 
 
var b = JSON.stringify(a); // "{"dt":"2016-02-11T09:52:49.170Z"}" 
 
var c = JSON.parse(b); // {dt: "2016-02-11T09:52:49.170Z"} 
 
c.dt instanceof Date; // false 

As you can see, parsing the JSON string works but the dt key no longer contains a Date instance and only holds the string representation of the date. This is a fundamental drawback of using JSON.stringify() and JSON.parse() in their native form.

If you want ForerunnerDB to serialise / de-serialise your object instances you must use this format instead:

var a = {
    dt: fdb.make(new Date())
};

By wrapping the new Date() in fdb.make() we allow ForerunnerDB to provide the Date() object with a custom .toJSON() method that serialises it differently to the native implementation.

For convenience the make() method is also available on all ForerunnerDB class instances e.g. db, collection, view etc. For instance you can access make via:

var fdb = new ForerunnerDB(),
    db = fdb.db('test'),
    coll = db.collection('testCollection'),
    date = new Date();
 
// All of these calls will do the same thing: 
date = fdb.make(date);
date = db.make(date);
date = coll.make(date);

You can read more about how ForerunnerDB's serialiser works here.

Supported Instance Types and Usage

Date
var a = {
    dt: fdb.make(new Date())
};
RegExp
var a = {
    re: fdb.make(new RegExp(".*", "i"))
};

or

var a = {
    re: fdb.make(/.*/i))
};

Adding Custom Types to the Serialiser

ForerunnerDB's serialisation system allows for custom type handling so that you can expand JSON serialisation to your own custom class instances.

This can be a complex topic so it has been broken out into the Wiki section for further reading here.

Searching the Collection

PLEASE NOTE While we have tried to remain as close to MongoDB's query language as possible, small differences are present in the query matching logic. The main difference is described here: Find behaves differently from MongoDB

See the Special Considerations section for details about how names of keys / properties in a query object can affect a query's operation.

Much like MongoDB, searching for data in a collection is done using the find() method, which supports many of the same operators starting with a $ that MongoDB supports. For instance, finding documents in the collection where the price is greater than 90 but less than 150, would look like this:

itemCollection.find({
    price: {
        "$gt": 90,
        "$lt": 150
    }
});

And would return an array with all matching documents. If no documents match your search, an empty array is returned.

Regular Expressions

Searches support regular expressions for advanced text-based queries. Simply pass the regular expression object as the value for the key you wish to search, just like when using regular expressions with MongoDB.

Insert a document:

collection.insert([{
    "foo": "hello"
}]);

Search by regular expression:

collection.find({
    "foo": /el/
});

You can also use the RegExp object instead:

var myRegExp = new RegExp("el");
 
collection.find({
    "foo": myRegExp
});

Query Operators

ForerunnerDB supports many of the same query operators that MongoDB does, and adds some that are not available in MongoDB but which can help in browser-centric applications.

  • $gt Greater Than
  • $gte Greater Than / Equal To
  • $lt Less Than
  • $lte Less Than / Equal To
  • $eq Equal To (==)
  • $eeq Strict Equal To (===)
  • $ne Not Equal To (!=)
  • $nee Strict Not Equal To (!==)
  • $in Match Any Value In An Array Of Values
  • $nin Match Any Value Not In An Array Of Values
  • $distinct Match By Distinct Key/Value Pairs
  • $count Match By Length Of Sub-Document Array
  • $or Match any of the conditions inside the sub-query
  • $and Match all conditions inside the sub-query
  • $exists Check that a key exists in the document
  • $elemMatch Limit sub-array documents by query
  • $elemsMatch Multiple document version of $elemMatch
  • $aggregate Converts an array of documents into an array of values base on a path / key
  • $near Geospatial operation finds outward from a central point

$gt

Selects those documents where the value of the field is greater than (i.e. >) the specified value.

{ field: {$gt: value} }
Usage
var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test");
    
coll.insert([{
    _id: 1,
    val: 1
}, {
    _id: 2,
    val: 2
}, {
    _id: 3,
    val: 3
}]);
 
result = coll.find({
    val: {
        $gt: 1
    }
});

Result is:

[{
    _id: 2,
    val: 2
}, {
    _id: 3,
    val: 3
}]

$gte

Selects the documents where the value of the field is greater than or equal to (i.e. >=) the specified value.

{ field: {$gte: value} }
Usage
var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test");
    
coll.insert([{
    _id: 1,
    val: 1
}, {
    _id: 2,
    val: 2
}, {
    _id: 3,
    val: 3
}]);
 
result = coll.find({
    val: {
        $gte: 1
    }
});

Result is:

[{
    _id: 1,
    val: 1
}, {
    _id: 2,
    val: 2
}, {
    _id: 3,
    val: 3
}]

$lt

Selects the documents where the value of the field is less than (i.e. <) the specified value.

{ field: { $lt: value} }
Usage
var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test");
    
coll.insert([{
    _id: 1,
    val: 1
}, {
    _id: 2,
    val: 2
}, {
    _id: 3,
    val: 3
}]);
 
result = coll.find({
    val: {
        $lt: 2
    }
});

Result is:

[{
    _id: 1,
    val: 1
}]

$lte

Selects the documents where the value of the field is less than or equal to (i.e. <=) the specified value.

{ field: { $lte: value} }
Usage
var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test");
    
coll.insert([{
    _id: 1,
    val: 1
}, {
    _id: 2,
    val: 2
}, {
    _id: 3,
    val: 3
}]);
 
result = coll.find({
    val: {
        $lte: 2
    }
});

Result is:

[{
    _id: 1,
    val: 1
}, {
    _id: 2,
    val: 2
}]
    ```
 
#### $eq
Selects the documents where the value of the field is equal (i.e. ==) to the specified value.
 
```js
{field: {$eq: value} }
Usage
var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test");
    
coll.insert([{
    _id: 1,
    val: 1
}, {
    _id: 2,
    val: 2
}, {
    _id: 3,
    val: 3
}]);
 
result = coll.find({
    val: {
        $eq: 2
    }
});

Result is:

[{
    _id: 2,
    val: 2
}]

$eeq

Selects the documents where the value of the field is strict equal (i.e. ===) to the specified value. This allows for strict equality checks for instance zero will not be seen as false because 0 !== false and comparing a string with a number of the same value will also return false e.g. ('2' == 2) is true but ('2' === 2) is false.

{field: {$eeq: value} }
Usage
var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test");
    
coll.insert([{
    _id: 1,
    val: "2"
}, {
    _id: 2,
    val: 2
}, {
    _id: 3,
    val: "2"
}]);
 
result = coll.find({
    val: {
        $eeq: 2
    }
});

Result is:

[{
    _id: 2,
    val: 2
}]

$ne

Selects the documents where the value of the field is not equal (i.e. !=) to the specified value. This includes documents that do not contain the field.

{field: {$ne: value} }
Usage
var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test");
    
coll.insert([{
    _id: 1,
    val: 1
}, {
    _id: 2,
    val: 2
}, {
    _id: 3,
    val: 3
}]);
 
result = coll.find({
    val: {
        $ne: 2
    }
});

Result is:

[{
    _id: 1,
    val: 1
}, {
    _id: 3,
    val: 3
}]

$nee

Selects the documents where the value of the field is not equal equal (i.e. !==) to the specified value. This allows for strict equality checks for instance zero will not be seen as false because 0 !== false and comparing a string with a number of the same value will also return false e.g. ('2' != 2) is false but ('2' !== 2) is true. This includes documents that do not contain the field.

{field: {$nee: value} }
Usage
var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test");
    
coll.insert([{
    _id: 1,
    val: 1
}, {
    _id: 2,
    val: 2
}, {
    _id: 3,
    val: 3
}]);
 
result = coll.find({
    val: {
        $nee: 2
    }
});

Result is:

[{
    _id: 1,
    val: 1
}, {
    _id: 3,
    val: 3
}]

$in

Selects documents where the value of a field equals any value in the specified array.

{ field: { $in: [<value1>, <value2>, ... <valueN> ] } }
Usage
var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test");
    
coll.insert([{
    _id: 1,
    val: 1
}, {
    _id: 2,
    val: 2
}, {
    _id: 3,
    val: 3
}]);
 
result = coll.find({
    val: {
        $in: [1, 3]
    }
});

Result is:

[{
    _id: 1,
    val: 1
}, {
    _id: 3,
    val: 3
}]

$nin

Selects documents where the value of a field does not equal any value in the specified array.

{ field: { $nin: [ <value1>, <value2> ... <valueN> ]} }
Usage
var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test");
    
coll.insert([{
    _id: 1,
    val: 1
}, {
    _id: 2,
    val: 2
}, {
    _id: 3,
    val: 3
}]);
 
result = coll.find({
    val: {
        $nin: [1, 3]
    }
});

Result is:

[{
    _id: 2,
    val: 2
}]

$distinct

Selects the first document matching a value of the specified field. If any further documents have the same value for the specified field they will not be returned.

{ $distinct: { field: 1 } }
Usage
var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test");
    
coll.insert([{
    _id: 1,
    val: 1
}, {
    _id: 2,
    val: 1
}, {
    _id: 3,
    val: 1
}, {
    _id: 4,
    val: 2
}]);
 
result = coll.find({
    $distinct: {
        val: 1
    }
});

Result is:

[{
    _id: 1,
    val: 1
}, {
    _id: 4,
    val: 2
}]

$count

Version >= 1.3.326

This is equivalent to MongoDB's $size operator but please see below for usage.

Selects documents based on the length (count) of items in an array inside a document.

{ $count: { field: <value> } }
Select Documents Where The "arr" Array Field Has Only 1 Item
var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test");
    
coll.insert([{
    _id: 1,
    arr: []
}, {
    _id: 2,
    arr: [{
        val: 1
    }]
}, {
    _id: 3,
    arr: [{
        val: 1
    }, {
        val: 2
    }]
}]);
 
result = coll.find({
    $count: {
        arr: 1
    }
});

Result is:

[{
    _id: 2,
    arr: [{
        val: 1
    }]
}]
Select Documents Where The "arr" Array Field Has More Than 1 Item
var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test");
    
coll.insert([{
    _id: 1,
    arr: []
}, {
    _id: 2,
    arr: [{
        val: 1
    }]
}, {
    _id: 3,
    arr: [{
        val: 1
    }, {
        val: 2
    }]
}]);
 
result = coll.find({
    $count: {
        arr: {
            $gt: 1
        }
    }
});

Result is:

[{
    _id: 3,
    arr: [{
        val: 1
    }, {
        val: 2
    }]
}]

$or

The $or operator performs a logical OR operation on an array of two or more and selects the documents that satisfy at least one of the .

{ $or: [ { <expression1> }, { <expression2> }, ... , { <expressionN> } ] }
Usage
var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test");
    
coll.insert([{
    _id: 1,
    val: 1
}, {
    _id: 2,
    val: 2
}, {
    _id: 3,
    val: 3
}]);
 
result = coll.find({
    $or: [{
        val: 1
    }, {
        val: {
            $gte: 3 
        }
    }]
});

Result is:

[{
    _id: 1,
    val: 1
}, {
    _id: 3,
    val: 3
}]

$and

Performs a logical AND operation on an array of two or more expressions (e.g. , , etc.) and selects the documents that satisfy all the expressions in the array. The $and operator uses short-circuit evaluation. If the first expression (e.g. ) evaluates to false, ForerunnerDB will not evaluate the remaining expressions.

{ $and: [ { <expression1> }, { <expression2> } , ... , { <expressionN> } ] }
Usage
var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test");
    
coll.insert([{
    _id: 1,
    val: 1
}, {
    _id: 2,
    val: 2
}, {
    _id: 3,
    val: 3
}]);
 
result = coll.find({
    $and: [{
        _id: 3
    }, {
        val: {
            $gte: 3 
        }
    }]
});

Result is:

[{
    _id: 3,
    val: 3
}]

$exists

When is true, $exists matches the documents that contain the field, including documents where the field value is null. If is false, the query returns only the documents that do not contain the field.

{ field: { $exists: <boolean> } }
Usage
var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test");
    
coll.insert([{
    _id: 1,
    val: 1
}, {
    _id: 2,
    val: 2,
    moo: "hello"
}, {
    _id: 3,
    val: 3
}]);
 
result = coll.find({
    moo: {
        $exists: true
    }
});

Result is:

[{
    _id: 2,
    val: 2,
    moo: "hello"
}]

Projection

$elemMatch

The $elemMatch operator limits the contents of an array field from the query results to contain only the first element matching the $elemMatch condition.

The $elemMatch operator is specified in the options object of the find call rather than the query object.

MongoDB $elemMatch Documentation

Usage
var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test");
    
coll.insert({
    names: [{
        _id: 1,
        text: "Jim"
    }, {
        _id: 2,
        text: "Bob"
    }, {
        _id: 3,
        text: "Bob"
    }, {
        _id: 4,
        text: "Anne"
    }, {
        _id: 5,
        text: "Simon"
    }, {
        _id: 6,
        text: "Uber"
    }]
});
 
result = coll.find({}, {
    $elemMatch: {
        names: {
            text: "Bob"
        }
    }
});

Result is:

{
    names: [{
        _id: 2,
        text: "Bob"
    }]
}

Notice that only the FIRST item matching the $elemMatch clause is returned in the names array. If you require multiple matches use the ForerunnerDB-specific $elemsMatch operator instead.

$elemsMatch

The $elemsMatch operator limits the contents of an array field from the query results to contain only the elements matching the $elemMatch condition.

The $elemsMatch operator is specified in the options object of the find call rather than the query object.

Usage
var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test");
    
coll.insert({
    names: [{
        _id: 1,
        text: "Jim"
    }, {
        _id: 2,
        text: "Bob"
    }, {
        _id: 3,
        text: "Bob"
    }, {
        _id: 4,
        text: "Anne"
    }, {
        _id: 5,
        text: "Simon"
    }, {
        _id: 6,
        text: "Uber"
    }]
});
 
result = coll.find({}, {
    $elemsMatch: {
        names: {
            text: "Bob"
        }
    }
});

Result is:

{
    names: [{
        _id: 2,
        text: "Bob"
    }, {
        _id: 3,
        text: "Bob"
    }]
}

Notice that all items matching the $elemsMatch clause are returned in the names array. If you require match on ONLY the first item use the MongoDB-compliant $elemMatch operator instead.

$aggregate

Coverts an array of documents into an array of values that are derived from a key or path in the documents. This is very useful when combined with the $find operator to run sub-queries and return arrays of values from the results.

{ $aggregate: path}
Usage
var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test");
    
coll.insert([{
    _id: 1,
    val: 1
}, {
    _id: 2,
    val: 2
}, {
    _id: 3,
    val: 3
}]);
 
result = coll.find({}, {
    $aggregate: "val"
});

Result is:

[1, 2, 3]

$near

PLEASE NOTE: BETA STATUS - PASSES UNIT TESTING BUT MAY BE UNSTABLE

Finds other documents whose co-ordinates based on a 2d index are within the specified distance from the specified centre point. Co-ordinates must be presented in longitude / latitude for $near to work.

{
    field: {
        $near: {
            $point: [<longitude number>, <latitude number>],
            $maxDistance: <number>,
            $distanceUnits: <units string>
        }
    }
}
Usage
var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test");
 
coll.insert([{
    lngLat: [51.50722, -0.12750],
    name: 'Central London'
}, {
    lngLat: [51.525745, -0.167550], // 2.18 miles 
    name: 'Marylebone, London'
}, {
    lngLat: [51.576981, -0.335091], // 10.54 miles 
    name: 'Harrow, London'
}, {
    lngLat: [51.769451, 0.086509], // 20.33 miles 
    name: 'Harlow, Essex'
}]);
 
// Create a 2d index on the lngLat field 
coll.ensureIndex({
    lngLat: 1
}, {
    type: '2d'
});
 
// Query index by distance 
// $near queries are sorted by distance from centre point by default 
result = coll.find({
    lngLat: {
        $near: {
            $point: [51.50722, -0.12750],
            $maxDistance: 3,
            $distanceUnits: 'miles'
        }
    }
});

Result is:

[{
    "lngLat": [51.50722, -0.1275],
    "name": "Central London",
    "_id": "1f56c0b5885de40"
}, {
    "lngLat": [51.525745, -0.16755],
    "name": "Marylebone, London",
    "_id": "372a34d9f17fbe0"
}]

Ordering / Sorting Results

You can specify an $orderBy option along with the find call to order/sort your results. This uses the same syntax as MongoDB:

itemCollection.find({
    price: {
        "$gt": 90,
        "$lt": 150
    }
}, {
    $orderBy: {
        price: 1 // Sort ascending or -1 for descending 
    }
});

Grouping Results

Version >= 1.3.757

You can specify a $groupBy option along with the find call to group your results:

myColl = db.collection('myColl');
 
myColl.insert([{
    "price": "100",
    "category": "dogFood"
}, {
  "price": "60",
  "category": "catFood"
}, {
    "price": "70",
    "category": "catFood"
}, {
    "price": "65",
    "category": "catFood"
}, {
    "price": "35",
    "category": "dogFood"
}]);
 
myColl.find({}, {
    $groupBy: {
        "category": 1 // Group using the "category" field. Path's are also allowed e.g. "category.name" 
    }
});

Result is:

{
    "dogFood": [{
        "price": "100",
        "category": "dogFood"
    }, {
        "price": "35",
        "category": "dogFood"
    }],
    "catFood": [{
        "price": "60",
        "category": "catFood"
    }, {
        "price": "70",
        "category": "catFood"
    }, {
        "price": "65",
        "category": "catFood"
    }],
}

Limiting Return Fields

You can specify which fields are included in the return data for a query by adding them in the options object. This follows the same rules specified by MongoDB here:

MongoDB Documentation

Please note that the primary key field will always be returned unless explicitly excluded from the results via "_id: 0".

Usage

var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test");
 
coll.insert([{
    _id: 1,
    text: "Jim",
    val: 2131232
}, {
    _id: 2,
    text: "Bob",
    val: 2425234321
}, {
    _id: 3,
    text: "Bob",
    val: 54353454
}, {
    _id: 4,
    text: "Anne",
    val: 1231432
}, {
    _id: 5,
    text: "Simon",
    val: 87567455
}, {
    _id: 6,
    text: "Uber",
    val: 93472834
}]);
 
result = coll.find({}, {
    text: 1
});

Result is:

[{
    _id: 1,
    text: "Jim"
}, {
    _id: 2,
    text: "Bob"
}, {
    _id: 3,
    text: "Bob"
}, {
    _id: 4,
    text: "Anne"
}, {
    _id: 5,
    text: "Simon"
}, {
    _id: 6,
    text: "Uber"
}]

Pagination / Paging Through Results

Version >= 1.3.55

It is often useful to limit the number of results and then page through the results one page at a time. ForerunnerDB supports an easy pagination system via the $page and $limit query options combination.

Usage

var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test"),
    data = [],
    count = 100,
    result,
    i;
 
// Generate random data 
for (= 0; i < count; i++) {
    data.push({
        _id: String(i),
        val: i
    });
}
 
coll.insert(data);
 
// Query the first 10 records (page indexes are zero-based 
// so the first page is page 0 not page 1) 
result = coll.find({}, {
    $page: 0,
    $limit: 10
});
 
// Query the next 10 records 
result = coll.find({}, {
    $page: 1,
    $limit: 10
});

Skipping Records in a Query

Version >= 1.3.55

You can skip records at the beginning of a query result by providing the $skip query option. This operates in a similar fashion to the MongoDB skip() method.

Usage

var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test").truncate(),
    data = [],
    count = 100,
    result,
    i;
 
// Generate random data 
for (= 0; i < count; i++) {
    data.push({
        _id: String(i),
        val: i
    });
}
 
coll.insert(data);
result = coll.find({}, {
    $skip: 50
});

Finding and Returning Sub-Documents

When you have documents that contain arrays of sub-documents it can be useful to search and extract them. Consider this data structure:

var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test").truncate(),
    result,
    i;
 
coll.insert({
    _id: "1",
    arr: [{
        _id: "332",
        val: 20,
        on: true
    }, {
        _id: "337",
        val: 15,
        on: false
    }]
});
 
/**
 * Finds sub-documents from the collection's documents.
 * @param {Object} match The query object to use when matching parent documents
 * from which the sub-documents are queried.
 * @param {String} path The path string used to identify the key in which
 * sub-documents are stored in parent documents.
 * @param {Object=} subDocQuery The query to use when matching which sub-documents
 * to return.
 * @param {Object=} subDocOptions The options object to use when querying for
 * sub-documents.
 * @returns {*}
 */
result = coll.findSub({
    _id: "1"
}, "arr", {
    on: false
}, {
    //$stats: true, 
    //$split: true 
});

The result of this query is an array containing the sub-documents that matched the query parameters:

[{
    _id: "337",
    val: 15,
    on: false
}]

The result of findSub never returns a parent document's data, only data from the matching sub-document(s)

The fourth parameter (options object) allows you to specify if you wish to have stats and if you wish to split your results into separate arrays for each matching parent document.

Subqueries and Subquery Syntax

Version >= 1.3.469

Subqueries are ForerunnerDB specific and do not work in MongoDB

A subquery is a query object within another query object.

Subqueries are useful when the query you wish to run is reliant on data inside another collection or view and you do not want to run a separate query first to retrieve that data.

Subqueries in ForerunnerDB are specified using the $find operator inside your query.

Take the following example data:

var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    users = db.collection("users"),
    admins = db.collection("admins");
    
users.insert([{
    _id: 1,
    name: "Jim"
}, {
    _id: 2,
    name: "Bob"
}, {
    _id: 3,
    name: "Bob"
}, {
    _id: 4,
    name: "Anne"
}, {
    _id: 5,
    name: "Simon"
}]);
 
admins.insert([{
    _id: 2,
    enabled: true
}, {
    _id: 4,
    enabled: true
}, {
    _id: 5,
    enabled: false
}]);
 
result = users.find({
    _id: {
        $in: {
            $find: {
                $from: "admins",
                $query: {
                    enabled: true
                },
                $options: {
                    $aggregate: "_id"
                }
            }
        }
    }
});

When this query is executed the $find sub-query object is replaced with the results from the sub-query so that the final query with (aggregated)[#$aggregate] _id field looks like this:

result = users.find({
    _id: {
        $in: [3, 4]
    }
});

The result of the query after execution is:

[{
    "_id": 3,
    "name": "Bob"
}, {
    "_id": 4,
    "name": "Anne"
}]

Updating the Collection

This is one of the areas where ForerunnerDB and MongoDB are different. By default ForerunnerDB updates only the keys you specify in your update document, rather than outright replacing the matching documents like MongoDB does. In this sense ForerunnerDB behaves more like MySQL. In the call below, the update will find all documents where the price is greater than 90 and less than 150 and then update the documents' key "moo" with the value true.

collection.update({
    price: {
        "$gt": 90,
        "$lt": 150
    }
}, {
    moo: true
});

If you wish to fully replace a document with another one you can do so using the $replace operator described in the Update Operators section below.

If you want to replace a key's value you can use the $overwrite operator described in the Update Operators section below.

Quick Updates

You can target individual documents for update by their id (primary key) via a quick helper method:

collection.updateById(1, {price: 180});

This will update the document with the _id field of 1 to a new price of 180.

Update Operators

$replace

The $replace operator will take the passed object and overwrite the target document with the object's keys and values. If a key exists in the existing document but not in the passed object, ForerunnerDB will remove the key from the document.

The $replace operator is equivalent to calling MongoDB's update without using a MongoDB $set operator.

When using $replace the primary key field will NEVER be replaced even if it is specified. If you wish to change a record's primary key id, remove the document and insert a new one with your desired id.

db.collection("test").update({
    <query>
}, {
    $replace: {
        <field>: <value>,
        <field>: <value>,
        <field>: <value>
    }
});

In the following example the existing document is outright replaced by a new one:

db.collection("test").insert({
    _id: "445324",
    name: "Jill",
    age: 15
});
 
db.collection("test").update({
    _id: "445324"
}, {
    $replace: {
        job: "Frog Catcher"
    }
});
 
JSON.stringify(db.collection("test").find());

Result:

[{
    "_id": "445324",
    "job": "Frog Catcher"
}]

$overwrite

The $overwrite operator replaces a key's value with the one passed, overwriting it completely. This operates the same way that MongoDB's default update behaviour works without using the $set operator.

If you wish to fully replace a document with another one you can do so using the $replace operator instead.

The $overwrite operator is most useful when updating an array field to a new type such as an object. By default ForerunnerDB will detect an array and step into the array objects one at a time and apply the update to each object. When you use $overwrite you can replace the array instead of stepping into it.

db.collection("test").update({
    <query>
}, {
    $overwrite: {
        <field>: <value>,
        <field>: <value>,
        <field>: <value>
    }
});

In the following example the "arr" field (initially an array) is replaced by an object:

db.collection("test").insert({
    _id: "445324",
    arr: [{
        foo: 1
    }]
});
 
db.collection("test").update({
    _id: "445324"
}, {
    $overwrite: {
        arr: {
            moo: 1
        }
    }
});
 
JSON.stringify(db.collection("test").find());

Result:

[{
    "_id": "445324",
    "arr": {
        "moo": 1
    }
}]

$each

Version >= 1.3.34

$each allows you to iterate through multiple update operations on the same query result. Use $each when you wish to execute update operations in sequence or on the same query. Using $each is slightly more performant than running each update operation one after the other calling update().

Consider the following sequence of update calls that define a couple of nested arrays and then push a value to the inner-nested array:

db.collection("test").insert({
    _id: "445324",
    count: 5
});
 
db.collection("test").update({
    _id: "445324"
}, {
    $cast: {
        arr: "array",
        $data: [{}]
    }
});
 
db.collection("test").update({
    _id: "445324"
}, {
    arr: {
        $cast: {
            secondArr: "array"
        }
    }
});
 
db.collection("test").update({
    _id: "445324"
}, {
    arr: {
        $push: {
            secondArr: "moo"
        }
    }
});
 
JSON.stringify(db.collection("test").find());

Result:

[
    {
        "_id": "445324",
        "count": 5,
        "arr": [{"secondArr": ["moo"]}]
    }
]

These calls a wasteful because each update() call must query the collection for matching documents before running the update against them. With $each you can pass a sequence of update operations and they will be executed in order:

db.collection("test").insert({
    _id: "445324",
    count: 5
});
 
db.collection("test").update({
    _id: "445324"
}, {
    $each: [{
        $cast: {
            arr: "array",
            $data: [{}]
        }
    }, {
        arr: {
            $cast: {
                secondArr: "array"
            }
        }
    }, {
        arr: {
            $push: {
                secondArr: "moo"
            }
        }
    }]
});
 
JSON.stringify(db.collection("test").find());

Result:

[
    {
        "_id": "445324",
        "count": 5,
        "arr": [{"secondArr": ["moo"]}]
    }
]

As you can see the single sequenced call produces the same output as the multiple update() calls but will run slightly faster and use fewer resources.

$inc

The $inc operator increments / decrements a field value by the given number.

db.collection("test").update({
    <query>
}, {
    $inc: {
        <field>: <value>
    }
});

In the following example, the "count" field is decremented by 1 in the document that matches the id "445324":

db.collection("test").insert({
    _id: "445324",
    count: 5
});
 
db.collection("test").update({
    _id: "445324"
}, {
    $inc: {
        count: -1
    }
});
 
JSON.stringify(db.collection("test").find());

Result:

[{
    "_id": "445324",
    "count": 4
}]

Using a positive number will increment, using a negative number will decrement.

$push

The $push operator appends a specified value to an array.

db.collection("test").update({
    <query>
}, {
    $push: {
        <field>: <value>
    }
});

The following example appends "Milk" to the "shoppingList" array in the document with the id "23231":

db.collection("test").insert({
    _id: "23231",
    shoppingList: []
});
 
db.collection("test").update({
    _id: "23231"
}, {
    $push: {
        shoppingList: "Milk"
    }
});
 
JSON.stringify(db.collection("test").find());

Result:

[{
    "_id": "23231",
    "shoppingList": [
        "Milk"
    ]
}]

$splicePush

The $splicePush operator adds an item into an array at a specified index.

db.collection("test").update({
    <query>
}, {
    $splicePush: {
        <field>: <value>
        $index: <index>
    }
});

The following example inserts "Milk" to the "shoppingList" array at index 1 in the document with the id "23231":

db.collection("test").insert({
    _id: "23231",
    shoppingList: [
        "Sugar",
        "Tea",
        "Coffee"
    ]
});
 
db.collection("test").update({
    _id: "23231"
}, {
    $splicePush: {
        shoppingList: "Milk",
        $index: 1
    }
});
 
JSON.stringify(db.collection("test").find());

Result:

[
    {
        "_id": "23231",
        "shoppingList": [
            "Sugar",
            "Milk",
            "Tea",
            "Coffee"
        ]
    }
]

$addToSet

Adds an item into an array only if the item does not already exist in the array.

ForerunnerDB supports the $addToSet operator as detailed in the MongoDB documentation. Unlike MongoDB, ForerunnerDB also allows you to specify a matching field / path to check uniqueness against by using the $key property.

In the following example $addToSet is used to check uniqueness against the whole document being added:

// Create a collection document 
db.collection("test").insert({
    _id: "1",
    arr: []
});
 
// Update the document by adding an object to the "arr" array 
db.collection("test").update({
    _id: "1"
}, {
    $addToSet: {
        arr: {
            name: "Fufu",
            test: "1"
        }
    }
});
 
// Try and do it again... this will fail because a 
// matching item already exists in the array 
db.collection("test").update({
    _id: "1"
}, {
    $addToSet: {
        arr: {
            name: "Fufu",
            test: "1"
        }
    }
});

Now in the example below we specify which key to test uniqueness against:

// Create a collection document 
db.collection("test").insert({
    _id: "1",
    arr: []
});
 
// Update the document by adding an object to the "arr" array 
db.collection("test").update({
    _id: "1"
}, {
    $addToSet: {
        arr: {
            name: "Fufu",
            test: "1"
        }
    }
});
 
// Try and do it again... this will work because the 
// key "test" is different for the existing and new objects 
db.collection("test").update({
    _id: "1"
}, {
    $addToSet: {
        arr: {
            $key: "test",
            name: "Fufu",
            test: "2"
        }
    }
});

You can also specify the key to check uniqueness against as an object path such as 'moo.foo'.

$pull

The $pull operator removes a specified value or values that match an input query.

db.collection("test").update({
    <query>
}, {
    $pull: {
        <arrayField>: <value|query>
    }
});

The following example removes the "Milk" entry from the "shoppingList" array:

db.users.update({
    _id: "23231"
}, {
    $pull: {
        shoppingList: "Milk"
    }
});

If an array element is an embedded document (JavaScript object), the $pull operator applies its specified query to the element as though it were a top-level object.

$pop

The $pop operator removes an element from an array at the beginning or end. If you wish to remove an element from the end of the array pass 1 in your value. If you wish to remove an element from the beginning of an array pass -1 in your value.

db.collection("test").update({
    <query>
}, {
    $pop: {
        <field>: <value>
    }
});

The following example pops the item from the beginning of the "shoppingList" array:

db.collection("test").insert({
    _id: "23231",
    shoppingList: [{
        _id: 1,
        name: "One"
    }, {
        _id: 2,
        name: "Two"
    }, {
        _id: 3,
        name: "Three"
    }]
});
 
db.collection("test").update({
    _id: "23231"
}, {
    $pop: {
        shoppingList: -1 // -1 pops from the beginning, 1 pops from the end 
    }
});
 
JSON.stringify(db.collection("test").find());

Result:

[{
    _id: "23231",
    shoppingList: [{
        _id: 2,
        name: "Two"
    }, {
        _id: 3,
        name: "Three"
    }]
}]

$move

The $move operator moves an item that exists inside a document's array from one index to another.

db.collection("test").update({
    <query>
}, {
    $move: {
        <arrayField>: <value|query>,
        $index: <index>
    }
});

The following example moves "Milk" in the "shoppingList" array to index 1 in the document with the id "23231":

db.users.update({
    _id: "23231"
}, {
    $move: {
        shoppingList: "Milk"
        $index: 1
    }
});

$cast

Version >= 1.3.34

The $cast operator allows you to change a property's type within a document. If used to cast a property to an array or object the property is set to a new blank array or object respectively.

This example changes the type of the "val" property from a string to a number:

db.collection("test").insert({
    val: "1.2"
});
 
db.collection("test").update({}, {
    $cast: {
        val: "number"
    }
});
 
JSON.stringify(db.collection("test").find());

Result:

[{
    "_id": "1d6fbf16e080de0",
    "val": 1.2
}]

You can also use cast to ensure that an array or object exists on a property without overwriting that property if one already exists:

db.collection("test").insert({
    _id: "moo",
    arr: [{
        test: true
    }]
});
 
db.collection("test").update({
    _id: "moo"
}, {
    $cast: {
        arr: "array"
    }
});
 
JSON.stringify(db.collection("test").find());

Result:

[{
    "_id": "moo",
    "arr": [{
        "test": true
    }]
}]

Should you wish to initialise an array or object with specific data if the property is not currently of that type rather than initialising as a blank array / object, you can specify the data to use by including a $data property in your $cast operator object:

db.collection("test").insert({
    _id: "moo"
});
 
db.collection("test").update({
    _id: "moo"
}, {
    $cast: {
        orders: "array",
        $data: [{
            initial: true
        }]
    }
});
 
JSON.stringify(db.collection("test").find());

Result:

[{
    "_id": "moo",
    "orders":[{
        "initial": true
    }]
}]

$unset

The $unset operator removes a field from a document.

db.collection("test").update({
    <query>
}, {
    $unset: {
        <field>: 1
    }
});

In the following example, the "count" field is remove from the document that matches the id "445324":

db.collection("test").insert({
    _id: "445324",
    count: 5
});
 
db.collection("test").update({
    _id: "445324"
}, {
    $unset: {
        count: 1
    }
});
 
JSON.stringify(db.collection("test").find());

Result:

[{
    "_id": "445324"
}]

Array Positional in Updates (.$)

Often you want to update a sub-document stored inside an array. You can use the array positional operator to tell ForerunnerDB that you wish to update a sub-document that matches your query clause.

The following example updates the sub-document in the array "arr" with the _id "foo" so that the "name" property is set to "John":

db.collection("test").insert({
    _id: "2",
    arr: [{
        _id: "foo",
        name: "Jim"
    }]
});
 
var result = db.collection("test").update({
    _id: "2",
    "arr": {
        "_id": "foo"
    }
}, {
    "arr.$": {
        name: "John"
    }
});

Internally this operation checks the update for property's ending in ".$" and then looks at the query part of the call to see if a corresponding clause exists for it. In the example above the "arr.$" property in the update part has a corresponding "arr" in the query part which determines which sub-documents are to be updated based on if they match or not.

Get Data Item By Reference

JavaScript objects are passed around as references to the same object. By default when you query ForerunnerDB it will "decouple" the results from the internal objects stored in the collection. If you would prefer to get the reference instead of decoupled object you can specify this in the query options like so:

var result = db.collection("item").find({}, {
    $decouple: false
});

If you do not specify a decouple option, ForerunnerDB will default to true and return decoupled objects.

Keep in mind that if you switch off decoupling for a query and then modify any object returned, it will also modify the internal object held in ForerunnerDB, which could result in incorrect index data as well as other anomalies.

Primary Keys

If your data uses different primary key fields from the default "_id" then you need to tell the collection. Simply call the primaryKey() method with the name of the field your primary key is stored in:

collection.primaryKey("itemId");

When you change the primary key field name, methods like updateById will use this field automatically instead of the default one "_id".

Removing Documents

Removing is as simple as doing a normal find() call, but with the search for docs you want to remove. Remove all documents where the price is greater than or equal to 100:

collection.remove({
    price: {
        "$gte": 100
    }
});

Joins

Sometimes you want to join two or more collections when running a query and return a single document with all the data you need from those multiple collections. ForerunnerDB supports collection joins via a simple options key "$join". For instance, let's setup a second collection called "purchase" in which we will store some details about users who have ordered items from the "item" collection we initialised above:

var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    itemCollection = db.collection("item"),
    purchaseCollection = db.collection("purchase");
 
itemCollection.insert([{
    _id: 1,
    name: "Cat Litter",
    price: 200
}, {
    _id: 2,
    name: "Dog Food",
    price: 100
}, {
    _id: 3,
    price: 400,
    name: "Fish Bones"
}, {
    _id: 4,
    price: 267,
    name:"Scooby Snacks"
}, {
    _id: 5,
    price: 234,
    name: "Chicken Yum Yum"
}]);
 
purchaseCollection.insert([{
    itemId: 4,
    user: "Fred Bloggs",
    quantity: 2
}, {
    itemId: 4,
    user: "Jim Jones",
    quantity: 1
}]);

Now, when we find data from the "item" collection we can grab all the users that ordered that item as well and store them in a key called "purchasedBy":

itemCollection.find({}, {
    "$join": [{
        "purchase": {
            "itemId": "_id",
            "$as": "purchasedBy",
            "$require": false,
            "$multi": true
        }
    }]
});

The "$join" key holds an array of joins to perform, each join object has a key which denotes the collection name to pull data from, then matching criteria which in this case is to match purchase.itemId with the item._id. The three other keys are special operations (start with $) and indicate:

  • $as tells the join what object key to store the join results in when returning the document
  • $require is a boolean that denotes if the join must be successful for the item to be returned in the final find result
  • $multi indicates if we should match just one item and then return, or match multiple items as an array

The result of the call above is:

[{
    "_id":1,
    "name":"Cat Litter",
    "price":200,
    "purchasedBy":[]
},{
    "_id":2,
    "name":"Dog Food",
    "price":100,
    "purchasedBy":[]
},{
    "_id":3,
    "price":400,
    "name":"Fish Bones",
    "purchasedBy":[]
},{
    "_id":4,
    "price":267,
    "name":"Scooby Snacks",
    "purchasedBy": [{
        "itemId":4,
        "user":"Fred Bloggs",
        "quantity":2
    }, {
        "itemId":4,
        "user":"Jim Jones",
        "quantity":1
    }]
},{
    "_id":5,
    "price":234,
    "name":"Chicken Yum Yum",
    "purchasedBy":[]
}]

Advanced Joins Using $where

Version => 1.3.455

If your join has more advanced requirements than matching against foreign keys alone, you can specify a custom query that will match data from the foreign collection using the $where clause in your $join.

For instance, to achieve the same results as the join in the above example, you can specify matching data in the foreign collection using the $$ back-reference operator:

itemCollection.find({}, {
    "$join": [{
        "purchase": {
            "$where": {
                "$query": {
                    "itemId": "$$._id"
                }
            },
            "$as": "purchasedBy",
            "$require": false,
            "$multi": true
        }
    }]
});

The $$ back-reference operator allows you to reference key/value data from the document currently being evaluated by the join operation. In the example above the query in the $where operator is being run against the purchase collection and the back-reference will lookup the current _id in the itemCollection for the document currently undergoing the join.

Placing Results $as: "$root"

Suppose we have two collections "a" and "b" and we run a find() on "a" and join against "b".

$root tells the join system to place the data from "b" into the root of the source document in "a" so that it is placed as part of the return documents at root level rather than under a new key.

If you use "$as": "$root" you cannot use "$multi": true since that would simply overwrite the root keys in "a" that are copied from the foreign document over and over for each matching document in "b".

This query also copies the primary key field from matching documents in "b" to the document in "a". If you don't want this, you need to specify the fields that the query will return. You can do this by specifying an "options" section in the $where clause:

var result = a.find({}, {
    "$join": [{
        "b": {
            "$where": {
                "$query": {
                    "_id": "$$._id"
                },
                "$options": {
                    "_id": 0
                }
            },
            "$as": "$root",
            "$require": false,
            "$multi": false
        }
    }]
});

By providing the options object and specifying the "_id" field as zero we are telling ForerunnerDB to ignore and not return that field in the join data.

"id": 0

The options section also allows you to join b against other collections as well which means you can created nested joins.

Triggers

Version >= 1.3.12

ForerunnerDB currently supports triggers for inserts and updates at both the before and after operation phases. Triggers that fire on the before phase can also optionally modify the operation data and actually cancel the operation entirely allowing you to provide database-level data validation etc.

Setting up triggers is very easy.

Example 1: Cancel Operation Before Insert Trigger

Here is an example of a before insert trigger that will cancel the insert operation before the data is inserted into the database:

var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    collection = db.collection("test");
 
collection.addTrigger("myTrigger", db.TYPE_INSERT, db.PHASE_BEFORE, function (operation, oldData, newData) {
    // By returning false inside a "before" trigger we cancel the operation 
    return false;
});
 
collection.insert({test: true});

The trigger method passed to addTrigger() as parameter 4 should handle these arguments:

Argument Data Type Description
operation object Details about the operation being executed. In before update operations this also includes query and update objects which you can modify directly to alter the final update applied.
oldData object The data before the operation is executed. In insert triggers this is always a blank object. In update triggers this will represent what the document that will be updated currently looks like. You cannot modify this object.
newData object The data after the operation is executed. In insert triggers this is the new document being inserted. In update triggers this is what the document being updated will look like after the operation is run against it. You can update this object ONLY in before phase triggers.

Example 2: Modify a Document Before Update

In this example we insert a document into the collection and then update it afterwards. When the update operation is run the before update trigger is fired and the document is modified before the update is applied. This allows you to make changes to an operation before the operation is carried out.

var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    collection = db.collection("test");
 
collection.addTrigger("myTrigger", db.TYPE_UPDATE, db.PHASE_BEFORE, function (operation, oldData, newData) {
    newData.updated = String(new Date());
});
 
// Insert a document with the property "test" being true 
collection.insert({test: true});
 
// Now update that document to set "test" to false - this 
// will fire the trigger code registered above and cause the 
// final document to have a new property "updated" which 
// contains the date/time that the update occurred on that 
// document 
collection.update({test: true}, {test: false});
 
// Now inspect the document and it will show the "updated" 
// property that the trigger added! 
console.log(collection.find());

Please keep in mind that you can only modify a document's data during a before phase trigger. Modifications to the document during an after phase trigger will simply be ignored and will not be applied to the document. This applies to insert and update trigger types. Remove triggers cannot modify the document at any time.

Enabling / Disabling Triggers

Version >= 1.3.31

Enabling a Trigger

You can enable a previously disabled trigger or multiple triggers using the enableTrigger() method on a collection.

If you specify a type or type and phase and do not specify an ID the method will affect all triggers that match the type / phase.

Enable a Trigger via Trigger ID
db.collection("test").enableTrigger("myTriggerId");
Enable a Trigger via Type
db.collection("test").enableTrigger(db.TYPE_INSERT);
Enable a Trigger via Type and Phase
db.collection("test").enableTrigger(db.TYPE_INSERT, db.PHASE_BEFORE);
Enable a Trigger via ID, Type and Phase
db.collection("test").enableTrigger("myTriggerId", db.TYPE_INSERT, db.PHASE_BEFORE);

Disabling a Trigger

You can temporarily disable a trigger or multiple triggers using the disableTrigger() method on a collection.

If you specify a type or type and phase and do not specify an ID the method will affect all triggers that match the type / phase.

Disable a Trigger via Trigger ID
db.collection("test").disableTrigger("myTriggerId");
Disable a Trigger via Type
db.collection("test").disableTrigger(db.TYPE_INSERT);
Disable a Trigger via Type and Phase
db.collection("test").disableTrigger(db.TYPE_INSERT, db.PHASE_BEFORE);
Disable a Trigger via ID, Type and Phase
db.collection("test").disableTrigger("myTriggerId", db.TYPE_INSERT, db.PHASE_BEFORE);

Trigger Recursion Protection

Version >= 1.3.728

Unlike some databases, ForerunnerDB allows you to execute CRUD operations from inside trigger methods and are guaranteed safe (will not cause infinite recursion).

ForerunnerDB includes trigger recursion protection so that triggers cannot end up calling themselves over and over again in an infinite loop.

An example of a recursive trigger is one in which an INSERT trigger is created, and inside that trigger, some code inserts another record which would then fire the trigger again, over and over.

ForerunnerDB does not let this happen because only one trigger with the same type, phase and id is allowed to be executed on the trigger processing stack at any one time.

The benefit of this protection is that you can be sure that calling CRUD operations from inside a trigger method is safe. The downside is that CRUD operations from inside a trigger method will not fire any triggers that have already fired previously in the trigger stack.

A quick example is to imagine you have triggers A, B, C and D:

A -> B
B -> C
C -> D
D -> A <-- Trigger A will not fire.

The same is true here:

A -> B
B -> A <-- Trigger A will not fire.

And here:

A -> B
B -> C
C -> B <-- Trigger B will not fire.

No errors are thrown when a trigger is denied execution, however if you enable debug mode on the database or collection the trigger is added to you will see a console message informing you that the trigger attempted to fire but was denied because of potential infinite recursion.

Events

Collections emit events when they carry out CRUD operations. You can hook an event using the on() method. Events that collections currently emit are:

insert

Emitted after an insert operation has completed. The passed arguments to the listener are:

  • {Array} inserted An array of the successfully inserted documents.
  • {Array} failed An array of the documents that failed to insert (for instance because of an index violation or trigger cancelling the insert).
var coll = db.collection("myCollection");
 
coll.on("insert", function (inserted, failed) {
    console.log("Inserted:", inserted);
    console.log("Failed:", failed);
});
 
coll.insert({moo: true});

update

Emitted after an update operation has completed. The passed arguments to the listener are:

  • {Array} items An array of the documents that were updated by the update operation.
var coll = db.collection("myCollection");
coll.insert({moo: true});
 
coll.on("update", function (updated) {
    console.log("Updated:", updated);
});
 
coll.update({moo: true}, {moo: false});

remove

Emitted after a remove operation has completed. The passed arguments to the listener are:

  • {Array} items An array of the documents that were removed by the remove operation.
var coll = db.collection("myCollection");
coll.insert({moo: true});
 
coll.on("remove", function (removed) {
    console.log("Removed:", removed);
});
 
coll.remove({moo: true});

setData

Emitted after a setData operation has completed. The passed arguments to the listener are:

  • {Array} newData An array of the documents that were added to the collection by the operation.
  • {Array} oldData An array of the documents that were in the collection before the operation.
var coll = db.collection("myCollection");
coll.insert({moo: true});
 
coll.on("setData", function (newData, oldData) {
    console.log("New Data:", newData);
    console.log("Old Data:", oldData);
});
 
coll.setData({foo: -1});

truncate

Emitted BEFORE a truncate operation has completed. The passed arguments to the listener are:

  • {Array} data An array of the documents that will be truncated from the collection.
var coll = db.collection("myCollection");
coll.insert({moo: true});
 
coll.on("truncate", function (data) {
    console.log("New Data:", newData);
});
 
coll.truncate();

change

Emitted after all CRUD operations have completed.

var coll = db.collection("myCollection");
 
 
coll.on("change", function () {
    console.log("Changed");
});
 
coll.insert({moo: true});

drop

Emitted after a collection is dropped.

var coll = db.collection("myCollection");
 
 
coll.on("drop", function () {
    console.log("Dropped");
});
 
coll.drop();

Conditions / Response (If This Then That - IFTTT)

Reacting to changes in data is one of the most powerful features of ForerunnerDB and making it easy to define what you wish to observe and what you wish to do when an observed condition changes form the basis of the If This Then That concept.

ForerunnerDB includes the ability to define an intuitive condition / response mechanism that allows your application to respond to changing data elegantly and with ease.

Creating IFTTT conditions is easy using expressive language methods (when, and, then, else):

var fdb = new ForerunnerDB(),
    db = fdb.db('test'),
    coll = db.collection('stocksIOwn'),
    condition;
 
condition = coll.when({
        _id: 'TSLA',
        val: {
            $gt: 210
        }
    })
    .and({
        _id: 'SCTY',
        val: {
            $gt: 23
        }
    })
    .then(function () {
        console.log('My stocks are worth more than I paid for them! Yay!');
    })
    .else(function () {
        console.log('I\'m loosing money :(');
    });

With the IFTTT condition / response set up, let's make some stock data!

coll.insert([{
    _id: 'TSLA',
    val: 214
}, {
    _id: 'SCTY',
    val: 20
}]);

Nothing happened! That's because we have to tell the condition to start listening for changes to its clauses:

condition.start(undefined);

The result:

I'm loosing money :(

Notice that we passed undefined to the start() method? That's because we want the condition to start off without a defined state. If we don't pass undefined, the default state of a condition is false. This means that when start() is called, if the clauses you have defined via when() and and() evaluate to false, nothing has technically changed so your else() method will not be called.

The starting state allows you control what happens the first time your clauses are evaluated by the condition engine.

Now let's update Solar City's stock to a nicer value (higher than my purchase price):

coll.update({_id: 'SCTY'}, {val: 25});

The result:

My stocks are worth more than I paid for them! Yay!

Now let's stop the condition from evaluating any more changes:

condition.stop();

And finally, let's drop the condition, removing it from memory:

condition.drop();

Indices & Performance

ForerunnerDB currently supports basic indexing for performance enhancements when querying a collection. You can create an index on a collection using the ensureIndex() method. ForerunnerDB will utilise the index that most closely matches the query you are executing. In the case where a query matches multiple indexes the most relevant index is automatically determined. Let's setup some data to index:

var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    names = ["Jim", "Bob", "Bill", "Max", "Jane", "Kim", "Sally", "Sam"],
    collection = db.collection("test"),
    tempName,
    tempAge,
    i;
 
for (= 0; i < 100000; i++) {
    tempName = names[Math.ceil(Math.random() * names.length) - 1];
    tempAge = Math.ceil(Math.random() * 100);
 
    collection.insert({
        name: tempName,
        age: tempAge
    });
}

You can see that in our collection we have some random names and some random ages. If we ask Forerunner to explain the query plan for querying the name and age fields:

collection.explain({
    name: "Bill",
    age: 17
});

The result shows that the largest amount of time was taken in the "tableScan" step:

{
    "analysis": Object,
    "flag": Object,
    "index": Object,
    "log": Array[0],
    "operation": "find",
    "results": 128, // Will vary depending on your random entries inserted earlier
    "steps": Array[4] // Lists the steps Forerunner took to generate the results
        [0]: Object
            "name": "analyseQuery",
            "totalMs": 0
        [1]: Object
            "name": "checkIndexes",
            "totalMs": 0
        [2]: Object
            "name": "tableScan",
            "totalMs": 54
        [3]: Object
            "name": "decouple",
            "totalMs": 1,
    "time": Object
}

From the explain output we can see that a large amount of time was taken up doing a table scan. This means that the database had to scan through every item in the collection and determine if it matched the query you passed. Let's speed this up by creating an index on the "name" field so that lookups against that field are very fast. In the index below we are indexing against the "name" field in ascending order, which is what the 1 denotes in name: 1. If we wish to index in descending order we would use name: -1 instead.

collection.ensureIndex({
    name: 1
});

The collection now contains an ascending index against the name field. Queries that check against the name field will now be optimised:

collection.explain({
    name: "Bill",
    age: 17
});

Now the explain output has some different results:

{
    analysis: Object,
    flag: Object,
    index: Object,
    log: Array[0],
    operation: "find",
    results: 128, // Will vary depending on your random entries inserted earlier
    steps: Array[6] // Lists the steps Forerunner took to generate the results
        [0]: Object
            name: "analyseQuery",
            totalMs: 1
        [1]: Object
            name: "checkIndexes",
            totalMs: 1
        [2]: Object
            name: "checkIndexMatch: name:1",
            totalMs: 0
        [3]: Object
            name: "indexLookup",
            totalMs: 0,
        [4]: Object
            name: "tableScan",
            totalMs: 13,
        [5]: Object
            name: "decouple",
            totalMs: 1,
    time: Object
}

The query plan shows that the index was used because it has an "indexLookup" step, however we still have a "tableScan" step that took 13 milliseconds to execute. Why was this? If we delve into the query plan a little more by expanding the analysis object we can see why:

{
    analysis: Object
        hasJoin: false,
        indexMatch: Array[1]
            [0]: Object
                index: Index,
                keyData: Object
                    matchedKeyCount: 1,
                    totalKeyCount: 2,
                    matchedKeys: Object
                        age: false,
                        name: true
                lookup: Array[12353]
        joinQueries: Object,
        options: Object,
        queriesJoin: false,
        queriesOn: Array[1],
        query: Object
    flag: Object,
    index: Object,
    log: Array[0],
    operation: "find",
    results: 128, // Will vary depending on your random entries inserted earlier
    steps: Array[6] // Lists the steps Forerunner took to generate the results
    time: Object
}

In the selected index to use (indexMatch[0]) the keyData shows that the index only matched 1 out of the 2 query keys.

In the case of the index and query above, Forerunner's process will be:

  • Query the index for all records that match the name "Bill" (very fast)
  • Iterate over the records from the index and check each one for the age 17 (slow)

This means that while the index can be used, a table scan of the index is still required. We can make our index better by using a compound index:

collection.ensureIndex({
    name: 1,
    age: 1
});

With the compound index, Forerunner can now pull the matching record right out of the hash table without doing a data scan which is very very fast:

collection.explain({
    name: "Bill",
    age: 17
});

Which gives:

{
    analysis: Object,
    flag: Object,
    index: Object,
    log: Array[0],
    operation: "find",
    results: 128, // Will vary depending on your random entries inserted earlier
    steps: Array[7] // Lists the steps Forerunner took to generate the results
        [0]: Object
            name: "analyseQuery",
            totalMs: 0
        [1]: Object
            name: "checkIndexes",
            totalMs: 0
        [2]: Object
            name: "checkIndexMatch: name:1",
            totalMs: 0
        [3]: Object
            name: "checkIndexMatch: name:1_age:1",
            totalMs: 0,
        [4]: Object
            name: "findOptimalIndex",
            totalMs: 0,
        [5]: Object
            name: "indexLookup",
            totalMs: 0,
        [6]: Object
            name: "decouple",
            totalMs: 0,
    time: Object
}

Now we are able to query 100,000 records instantly, requiring zero milliseconds to return the results.

Examining the output from an explain() call will provide you with the most insight into how the query was executed and if a table scan was involved or not, helping you to plan your indices accordingly.

Keep in mind that indices require memory to maintain and there is always a trade-off between speed and memory usage.

Index Types (Choosing the Type of Index to Use)

B-Tree and Geospatial indexes are currently considered beta level and although they are passing unit tests, are provided for testing and development purposes. We cannot guarantee their functionality or performance at this time as more stringent tests and real-world usage must be done before they are considered production-ready. Please DO test them and report any bugs or issues. It is only with the help of the community that new features can get put through their paces!

CUSTOM INDEX If you are interested in developing your own custom index class for ForerunnerDB please see the wiki page on creating and registering your index class / type: Adding Custom Index to ForerunnerDB

ForerunnerDB currently defaults to a hash table index when you call ensureIndex(). There is also support for both b-tree and geospatial indexing and you can specify the type of index you wish to use via the ensureIndex() call:

Example of Creating a B-Tree Index

Version >= 1.3.691

collection.ensureIndex({
    name: 1
}, {
    type: 'btree'
});

Example of Creating a Geospatial 2d Index

Version >= 1.3.691

collection.ensureIndex({
    lngLat: 1
}, {
    type: '2d'
});

Example of Creating a Hash Table Index

collection.ensureIndex({
    name: 1
}, {
    type: 'hashed'
});

Geospatial (2d) Queries

Version >= 1.3.691

PLEASE NOTE: BETA STATUS - PASSES UNIT TESTING BUT MAY BE UNSTABLE

Geospatial indices and queries are currently considered beta and although unit tests for geospatial queries are passing we would recommend you use them with caution. Please report any bugs or inconsistencies you might find when using geospatial queries in ForerunnerDB on our GitHub issues page.

We can insert some documents with longitude / latitude co-ordinates:

var coll = db.collection('houses');
 
coll.insert([{
    lngLat: [51.50722, -0.12750],
    name: 'Central London'
}, {
    lngLat: [51.525745, -0.167550], // 2.18 miles 
    name: 'Marylebone, London'
}, {
    lngLat: [51.576981, -0.335091], // 10.54 miles 
    name: 'Harrow, London'
}, {
    lngLat: [51.769451, 0.086509], // 20.33 miles 
    name: 'Harlow, Essex'
}]);

To query this data using a geospatial operator we need to set up a 2d index against it:

coll.ensureIndex({
    lngLat: 1
}, {
    type: '2d'
});

Now we can run a query with the geospatial operator "$near" to return results ordered by the distance from the centre point we provide:

// Query index by distance 
// $near queries are sorted by distance from centre point by default 
result = coll.find({
    lngLat: {
        $near: {
            $point: [51.50722, -0.12750],
            $maxDistance: 3,
            $distanceUnits: 'miles'
        }
    }
});

The result is:

[{
    "lngLat": [51.50722, -0.1275],
    "name": "Central London",
    "_id": "1f56c0b5885de40"
}, {
    "lngLat": [51.525745, -0.16755],
    "name": "Marylebone, London",
    "_id": "372a34d9f17fbe0"
}]

These documents have lngLat co-ordinates that are within 3 miles from the $point co-ordinate 51.50722, -0.12750 (Central London, UK). The results are ordered by distance from the centre point ascending.

Data Persistence (Save and Load Between Pages)

Data Persistence In Browser

Data persistence allows your database to survive the browser being closed, page reloads and navigation away from the current url. When you return to the page your data can be reloaded.

Persistence calls are async so a callback should be passed to ensure the operation has completed before relying on data either being saved or loaded.

Persistence is handled by a very simple interface in the Collection class. You can save the current state of any collection by calling:

collection.save(function (err) {
    if (!err) {
        // Save was successful 
    }
});

You can then load the collection's data back again via:

collection.load(function (err, tableStats, metaStats) {
    if (!err) {
        // Load was successful 
    }
});

If you call collection.load() when your application starts and collection.save() when you make changes to your collection you can ensure that your application always has up-to-date data.

An eager-saving mode is currently being worked on to automatically save changes to collections, please see #41 for more information.

In the load() method callback the tableStats and metaStats objects contain information about what (if anything) was loaded for the collection and the collection's meta-data. You can inspect these objects to determine if the collection actually loaded any data or if the persistent storage for the collection was empty.

Here is an example stats object (tableStats and metaStats contain the same keys with different data for the collection's data and the collection's meta-data):

{
    "foundData": true,
    "rowCount": 1
}

Keep in mind that the foundData key can be true at the same time as rowCount is zero. This is because foundData is true if any previously persisted data exists, even if there are no rows in the data file. Therefore if you wish to check if previous data exists and contains rows, you should do:

...
if (tableStats.foundData && tableStats.rowCount > 0) { ... }

Manually Specifying Storage Engine

If you would like to manually specify the storage engine that ForerunnerDB will use you can call the driver() method:

IndexedDB
var fdb = new ForerunnerDB(),
    db = fdb.db("test");
db.persist.driver("IndexedDB");
WebSQL
var fdb = new ForerunnerDB(),
    db = fdb.db("test");
db.persist.driver("WebSQL");
LocalStorage
var fdb = new ForerunnerDB(),
    db = fdb.db("test");
db.persist.driver("LocalStorage");

Data Persistence In Node.js

Version >= 1.3.300

Persistence in Node.js is currently handled via the NodePersist.js class and is included automatically when you require ForerunnerDB in your project.

To use persistence in Node.js you must first tell the persistence plugin where you wish to load and save data files to. You can do this via the dataDir() call:

var fdb = new ForerunnerDB(),
    db = fdb.db("test");
    
db.persist.dataDir("./configData");

In the example above we set the data directory to be relative to the current working directory as "./configData".

You can specify any directory path you wish but you must ensure you have permissions to access and read/write to that directory. If the directory does not exist, ForerunnerDB will attempt to create it for you as soon as you make the call to dataDir().

Once you have your dataDir() setup, you can save and load data as shown below.

Persistence calls are async so a callback should be passed to ensure the operation has completed before relying on data either being saved or loaded.

Persistence is handled by a very simple interface in the Collection class. You can save the current state of any collection by calling:

collection.save(function (err) {
    if (!err) {
        // Save was successful 
    }
});

You can then load the collection's data back again via:

collection.load(function (err) {
    if (!err) {
        // Load was successful 
    }
});

If you call collection.load() when your application starts and collection.save() when you make changes to your collection you can ensure that your application always has up-to-date data.

An eager-saving mode is currently being worked on to automatically save changes to collections, please see #41 for more information.

Both Browser and Node.js

Removing Persisted Data

When a database instance is dropped, the persistent storage that belongs to that instance is automatically removed as well.

Please see Dropping and Persistent Storage for more information.

Plugins

Version >= 1.3.235

The persistent storage module supports adding plugins to the transcoder. The transcoder is the part of the module that encodes data for saving to persistent storage when .save() is called, and decodes data currently stored in persistent storage when .load() is called.

The transcoder is made up of steps, each step can modify the data and pass it on to the next step. By default there is only one step in the transcoder which either stringifies JSON data (for saving) or parses it (for loading).

By adding a plugin as a transcoder step the plugin is able to make its own modifications to the data before it is saved or loaded. Plugins must ensure that the final data they provide in their callback is a string as we must allow support for LocalStorage and are currently only able to store string data against keys in LocalStorage.

Data Compression and Encryption

Version >= 1.3.235

ForerunnerDB includes compression and encryption plugins that integrate with the persistent storage module. When compression or encryption (or both) are enabled, extra steps are executed in the persistent storage transcoder that modify the final stored data.

Please keep in mind that the order that you add transcoder steps is the order they are executed in so adding compression after encryption will store data that has first been encrypted, then compressed.

The compression and encryption plugins register themselves in the db's shared plugins repository available via:

db.shared.plugins.FdbCompress
db.shared.plugins.FdbCrypto

The plugins are meant to be instantiated before use as shown in the examples below.

Compression

The compression plugin takes data from the previous transcoder step and performs a zip operation on it. If the compressed data is smaller in size to the original data then the compressed data is used. If the compressed data is not smaller, no changes are made to the original data and it is stored uncompressed.

To enable the compression plugin in the persistent storage module you must add it as a transcoder step:

db.persist.addStep(new db.shared.plugins.FdbCompress());
Encryption

The encryption plugin takes data from the previous transcoder step and encrypts / decrypts it based on the pass-phrase that the plugin is instantiated with. By default the plugin uses AES-256 as the encryption cypher algorithm.

To enable the encryption plugin in the persistent storage module you must add it as a transcoder step:

db.persist.addStep(new db.shared.plugins.FdbCrypto({
    pass: "testing"
}));

The plugin accepts an options object as the first argument during instantiation and supports the following keys:

  • pass: The pass-phrase that will be used to encrypt / decrypt data.
  • algo: The algorithm to use. Currently defaults to "AES". Supports: "AES", "DES", "TripleDES", "Rabbit", "RC4" and "RC4Drop".

If you need to change the encryption pass-phrase on the fly after the instantiation of the plugin you can hold a reference to the plugin and use its pass() method:

var crypto = new db.shared.plugins.FdbCrypto({
    pass: "testing"
});
 
db.persist.addStep(crypto);
 
// At a later time, change the pass-phrase 
crypto.pass("myNewPassPhrase");

Storing Arbitrary Key/Value Data

Sometimes it can be useful to store key/value data on a class instance such as the core db class or a collection or view instance. This can later be retrieved somewhere else in your code to provide a quick and easy data-store across your application that is outside of the main storage system of ForerunnerDB, does not persist, is not indexed or maintained and will be destroyed when the supporting instance is dropped.

To use the store, simply call the store() method on a collection or view:

var fdb = new ForerunnerDB(),
    db = fdb.db("test");
    
db.collection("myColl").store("myKey", "myVal");

You can then lookup the value at a later time:

var value = db.collection("myColl").store("myKey");
console.log(value); // Will output "myVal" 

You can also remove a key/value from the store via the unStore() method:

db.collection("myColl").unStore("myKey");

Collection Groups

ForerunnerDB supports aggregating collection data from multiple collections into a single CRUD-enabled entity called a collection group. Collection groups are useful when you have multiple collections that contain similar data and want to query the data as a whole rather than one collection at a time.

This allows you to query and sort a super-set of data from multiple collections in a single operation and return that data as a single array of documents.

var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll1 = db.collection("test1"),
    coll2 = db.collection("test2"),
    group = db.collectionGroup("testGroup");
    
group.addCollection(coll1);
group.addCollection(coll2);
 
coll1.insert({
    name: "Jim"
});
 
coll2.insert({
    name: "Bob"
});
 
group.find();

Result:

[{"name": "Jim"}, {"name": "Bob"}]

Adding and Removing Collections From a Group

Collection groups work by adding collections as data sources. You can add a collection to a group via the addCollection() method which accepts a collection instance as the first argument.

var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("test"),
    group = db.collectionGroup("test");
 
group.addCollection(coll);

You can remove a collection from a collection group via the removeCollection() method:

group.removeCollection(coll);

Dropping Database Instances

All database instances have a drop() method which removes the instance from memory.

You can individually drop databases, collections, views, overviews etc.

For instance, if you wish to drop an entire database:

var fdb = new ForerunnerDB(),
    db = fdb.db('test'),
    coll;
 
// Create a collection called testColl 
coll = db.collection('testColl');
 
// Insert a record 
coll.insert({
    _id: 1,
    name: 'Test'
});
 
// Ask for a list of collections 
console.log('Before drop', JSON.stringify(db.collections()));
 
// Drop the entire database 
db.drop();
 
// Now grab the database again (note that previous references will no longer work) 
db = fdb.db('test');
 
// Ask for a list of collections 
console.log('After drop', db.collections());

Output:

Before drop [{"name":"testColl","count":1,"linked":false}]
After drop []

Dropping a database automatically drops all instances connected with that database.

Dropping and Persistent Storage

When dropping a database or collection the persistent storage related to that instance will be dropped as well. If you wish to keep the persistent storage you must specify that when you call the drop() method. Passing false as the first argument to drop() will tell ForerunnerDB not to drop the persistent storage for the instance being dropped.

For example, to drop a collection without removing its persistent storage:

db.collection('test').drop(false);

The same is true when dropping an entire database. If you pass false in the first argument then no instances stored in the database will drop their persistent storage:

db.drop(false);

Grid / Table Output

Data Binding: Enabled

ForerunnerDB 1.3 includes a grid / table module that allows you to output data from a collection or view to an HTML table that can be sorted and is data-bound so the table will react to changes in the underlying data inside the collection / view.

Prerequisites

  • The AutoBind module must be loaded

Grid Template

Grids work via a jsRender template that describes how your grid should be rendered to the browser. An example template called "gridTable" looks like this:

<script type="text/x-jsrender" id="gridTable">
    <table class="gridTable">
        <thead class="gridHead">
            <tr>
                <td data-grid-sort="firstName">First Name</td>
                <td data-grid-sort="lastName">Last Name</td>
                <td data-grid-sort="age">Age</td>
            </tr>
        </thead>
        <tbody class="gridBody">
            {^{for gridRow}}
            <tr data-link="id{:_id}">
                <td>{^{:firstName}}</td>
                <td>{^{:lastName}}</td>
                <td>{^{:age}}</td>
            </tr>
            {^{/for}}
        </tbody>
        <tfoot>
            <tr>
                <td></td>
                <td></td>
                <td></td>
            </tr>
        </tfoot>
    </table>
</script> 

You'll note that the main body section of the table has a for-loop looping over the special gridRow array. This array is the data inside your collection / view that the grid has been told to read from and is automatically passed to your template by the grid module. Use this array to loop over and output the row data for each row in your collection.

Creating a Grid

First you need to identify a target element that will contain the rendered grid:

<div id="myGridContainer"></div>

You can create a grid on screen via the .grid() method, passing it your target jQuery selector as a string:

// Create our instances 
var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    coll = db.collection("testGrid"),
    grid;
 
// Insert some data into our collection 
coll.insert({
    firstName: "Fred",
    lastName: "Jones",
    age: 15
});
 
// Create a grid from the collection using the template we defined earlier 
coll.grid("#myGridContainer", "#gridTable");

Auto-Sorting Tools

The table can automatically handle sort requests when a column header is tapped/clicked on. To enable this functionality simply add the data-grid-sort="{column name}" attribute to elements you wish to use as sort elements. A good example is to use the table column header for sorting and you can see the correct usage above in the HTML of the table template.

Views

Data Binding: Enabled

A view is a queried subset of a collection that is automatically updated whenever the underlying collection is altered. Views are accessed in the same way as a collection and contain all the main CRUD functionality that a collection does. Inserting or updating on a view will alter the underlying collection.

For a detailed insight into how data propagates from an underlying data source to a view see the section on (View Data Propagation and Synchronisation)[#notes_on_view_data_propagation_and_synchronisation].

Instantiating a View

Views are instantiated the same way collections are:

var myView = db.view("myView");

Specify an Underlying Data Source

You must tell a view where to get it's data from using the from() method. Views can use collections and other views as data sources:

var fdb = new ForerunnerDB(),
    db = fdb.db("test"),
    myCollection = db.collection("myCollection");
 
myCollection.insert([{
    name: "Bob",
    age: 20
}, {
    name: "Jim",
    age: 25
}, {
    name: "Bill",
    age: 30
}]);
 
myView.from(myCollection);

Setting a View's Query

Since views represent live queried data / subsets of the underlying data source they usually take a query:

myView.query({
    age: {
        $gt: 24
    }
});

Using the collection data as defined in myCollection above, a call to the view's find() method will result in returning only records in myCollection whose age property is greater than 24:

myView.find();

Result:

[{
    "name": "Jim",
    "age": 25,
    "_id": "2aee6ba38542220"
}, {
    "name": "Bill",
    "age": 30,
    "_id": "2d3bb2f43da7aa0"
}]

A view query can also take an options object. If you wish to provide a query and an options object together, call .query(, <options) e.g:

myView.query({
    age: {
        $gt: 24
    }
}, {
    $orderBy: {
        age: -1
    }
});

Prior to version 1.3.567 you had to use queryData() instead of query() to pass both a query and options object in the same call.

Overviews

Data Binding: Enabled

The Overview class provides the facility to run custom logic against the data from multiple data sources (collections and views for example) and return a single object / value. This is especially useful for scenarios where a summary of data is required such as a shopping basket order summary that is updated in realtime as items are added to the underlying cart collection, a count of some values etc.

Consider a page with a shopping cart system and a cart summary which shows the number of items in the cart and the total cart value. Let's start by defining our cart collection:

var cart = db.collection("cart");

Now we add some data to the cart:

cart.insert([{
    name: "Cat Food",
    price: 12.99,
    quantity: 2
}, {
    name: "Dog Food",
    price: 18.99,
    quantity: 3
}]);

Now we want to display a cart summary with number of items and the total cart price, so we create an overview:

var cartSummary = db.overview("cartSummary");

We need to tell the overview where to read data from:

cartSummary.from(cart);

Now we give the overview some custom logic that will do our calculations against the data in the cart collection and return an object with our item count and price total:

cartSummary.reduce(function () {
    var obj = {},
        items = this.find(), // .find() on an overview runs find() against underlying collection 
        total = 0,
        i;
 
    for (= 0; i < items.length; i++) {
        total += items[i].price * items[i].quantity;
    }
 
    obj.count = items.length;
    obj.total = total;
 
    return obj;
});

You can execute the overview's reduce() method and get the result via the exec() method:

cartSummary.exec();

Result:

{"count": 2, "total": 31.979999999999997}

Data Binding

Data binding is an optional module that is included via the fdb-autobind.min.js file. If you wish to use data-binding please ensure you include that file in your page after the main fdb-all.min.js file.

The database includes a useful data-binding system that allows your HTML to be automatically updated when data in the collection changes.

Binding a template to a collection will render the template once for each document in the collection. If you need an array of the entire collection passed to a single template see the section below on wrapping data.

Here is a simple example of a data-bind that will keep the list of items up-to-date if you modify the collection:

Prerequisites

  • Data-binding requires jQuery to be loaded
  • The AutoBind module must be loaded

HTML

<ul id="myList">
</ul>
<script