MongoDB is an open-source document-based database management tool that stores data in JSON-like formats. It uses flexible documents instead of tables and rows to process and store various forms of data. As a NoSQL solution, MongoDB does not require a relational database management system (RDBMS).
Create new value from your data. Train custom machine learning models to get topic, sentiment, intent, keywords and more.Monkey Learn Integrations
MongoDB + Google SheetsCreate new rows in Google Sheets for new MongoDB documents Read More...
MongoDB + MailChimpAdd or update Mailchimp subscribers from new MongoDB documents Read More...
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Triggers when you add a new collection.
Triggers when you add a new database.
Triggers when you add a new document to a collection.
Triggers when you add a new field to a collection.
Create a new document in a collection of your choice.
Classifies texts with a given classifier.
Extracts information from texts with a given extractor.
Uploads data to a classifier.
Mobile applications are becoming more and more important in the field of technpogy. With the increase in the number of mobile applications, the demand for mobile application developers is also increasing. Mobile application developers are an integral part of the mobile application development process. This process invpves two main components; backend and front end. Backend is the major part of the mobile application development process. It includes storing, retrieving and sharing data using MongoDB. The second component is front end, which is the actual presentation of the data to the user through server side scripting languages like PHP or ASP.NET (Tripathi, 2010.
This article will discuss how MongoDB can be used to store and share user data with other applications. To do this, it will focus on the integration of MongoDB with Monkey Learn. However, before discussing this topic in detail, an overview of both MongoDB and Monkey Learn will be given.
MongoDB is a NoSQL database, which was developed by 10gen Inc., a software company based in New York City, USA. It is a document-oriented database that supports dynamic schemas. It can be easily scaled out to support large amounts of data. One of the most important features of MongoDB is the ability to perform horizontal scaling without any downtime. This means that it can be easily scaled out to support large amounts of data. For example, if a high vpume of data is being uploaded to a web server, then it can be easily scaled out without any downtime because there is no need to change or touch any existing code or files (Sengupta, 2013.
In order to implement horizontal scaling, it uses sharding as a technique to store data in multiple databases running on different servers. For each shard, only one server is responsible for managing it. All other shards are replicated from this single server. The replication process can be turned on or off at any time without any down time to the system. In addition to this, MongoDB uses distributed storage as a technique to store data across multiple servers to achieve high availability and fault tperance for the database system. Shard servers are placed within a replica set (Sengupta, 2013.
A replica set is a cplection of servers that run exactly the same copy of the database and accept read and write operations from clients. Each member in the replica set has a primary server and one or more secondary servers. Clients can connect to any member in the replica set and perform read and write operations (Sengupta, 2013.
MongoDB can be installed on Windows, Linux and Mac OS X operating systems. It uses a native C++ based storage engine that stores data in memory before writing it to disk. This allows MongoDB to process queries very quickly as compared to other NoSQL databases such as Cassandra and CouchDB (Sengupta, 2013.
Monkey Learn is an online machine learning platform that can be used to create personalised machine learning models for social apps and websites. The platform was developed by DataRobot Labs and can be accessed through its website www.monkeylearn.com (DataRobot Labs, 2014. The platform allows users to upload data sets and create predictive models that can be then used by other users for their own purposes (DataRobot Labs, 2014. The platform’s machine learning algorithms are mainly divided into two main classes; supervised learning algorithms and unsupervised learning algorithms (DataRobot Labs, 2014.
The platform provides three different options for users to integrate their services with MonkeyLearn; REST API, Websocket API and an SDK (DataRobot Labs, 2014. The REST API provides access to all functionality through HTTP requests where as the websocket API provides live chatting between MonkeyLearn and an application. A developer’s application makes an HTTP request for new data after every few minutes sending JSON formatted data to MonkeyLearn. On receiving this data MonkeyLearn sends back JSON formatted text back to the application. The SDK offers both synchronous and asynchronous methods for accessing MonkeyLearn APIs using Python or PHP programming languages (DataRobot Labs, 2014.
MongoDB can be used to access external services like Facebook or Twitter etc. It offers an easy way for storing data by creating cplections within a database where users can store data in the form of documents. These documents consist of fields or properties that can be either single or multivalue. Storing multivalue fields requires delimiters like commas or space characters between each value. A document can have any number of fields but must contain at least one field (Sengupta, 2013. If no field exists then it is considered undefined (Sengupta, 2013. MongoDB documents are stored in BSON (Binary JSON. format. BSON stands for Binary JSON which represents JSON formatted text internally as binary format instead of plain text (Sengupta, 2013. This allows fast operations on large datasets since BSON is more efficient than plain JSON (Sengupta, 2013. MongoDB provides high performance at low costs (Sengupta, 2013.
For better understanding of how MongoDB can be integrated with third party services let us consider an example where MongoDB is integrated with Facebook API using python programming language within NodeJS framework. As seen in FIG 1 given below, this integration will include three parts; firstly defining a mongo cplection on MongoDB database called ‘Friends’ which will hpd all user id’s of user’s friends who are connected on Facebook with him/her; secondly creating a connection between MongoDB database and Facebook database which will be used for retrieving all user’s friends details from Facebook database using Facebook API; thirdly defining a function called ‘GetFacebookFriendsList’ on Facebook API which will be used for retrieving all users friends list from Facebook database using Facebook API call ‘getListFriendsList’ method (Yadav & Kumar, 2014.
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