MongoDB Realm is a development platform designed for modern, data-driven applications. You can use Realm to build mobile, web, desktop, and IoT.
Bitly is a link management software, which helps organizations create and manage custom URLs to support marketing campaigns.
It's easy to connect MongoDB Realm + Bitly without coding knowledge. Start creating your own business flow.
Triggers when a new push notification is created
Triggers when a new service is created
Triggers when a new user is created
Trigger when you create a New Bitlink.
Confirm a pending user
Create a service
Creates a Trigger
Creates a User
Delete a pus notification
Delete a trigger
Delete a User
Disable a User
Enable a User
Update a trigger
Saves a Bitlink to your user history in Bitly. Returns a shortened URL.
MongoDB is a cross-platform document database that is used for storage, retrieval and manipulation of data. It can be used in any of the fplowing situations
To store data in an organized, easy to access format. To identify objects through unique keys. To manage security of data. To provide flexible queries on data stored within it.
MongoDB is written in C++ language. A command line interface has been created for developers to interact with the database. The API can be used to develop applications using Java, PHP, Ruby, Objective C, C#, Python, NodeJS etc.
MongoDB can be scaled horizontally or vertically based on the requirements. Horizontal scaling means adding new machines to the existing cluster. Vertical scaling means adding more memory or processor power to the existing server.
It supports all popular operating systems like Linux, Mac OS X and Windows.
Bitly is a link shortening service that is used for tracking links, measuring clicks and creating custom short links. Link shortening is done by storing the link in a database, appending a character string like “bit.ly/” or “http://bit.ly/” and then returning the shortened URL to the user.
Bitly provides API endpoints to access most of its features including link analytics, API support, API authentication, API customization, API reporting and API token management. API users can receive API keys to use the API with their applications. API keys are case sensitive and should be kept secret. API requests can also be made anonymously without API keys provided they are not rate limited. API calls are rate limited per IP address with an auto-response API call limiting API calls to 20 per minute. API requests made after 20 API calls per minute will return error messages containing information about the current API usage level. API usage levels are updated at regular time intervals. API requests made after 20 API calls per minute with API keys will return error messages containing information about the current API usage level instead of returning error messages containing information about rate limiting. Rate limiting is done to prevent overloading of servers because of malicious traffic from bots and other enabled tops that could cause denial of service attacks.
Integration of MongoDB Realm and Bitly can be done in a variety of ways depending on the use cases and the scale of the system being developed. Here we will discuss a few simple use cases where integration can be done easily by a developer or a team of developers.
In this method, MongoDB Realm will create a record for each URL that needs to be shortened using an API key since Bitly requires that each request made via their REST API must contain an API key in order to work properly. MongoDB Realm will add an entry into their cplection which will include details such as the shortened URL, the original URL and the date and time when it was saved in MongoDB Realm. Once this is complete, a call will be made to the Bitly REST API which will find the corresponding shortened URL from its database and return it back to MongoDB Realm. Finally, a call will be made to the Bitly API using this shortened URL which will return analytics information about the number of clicks that have been made on this URL till that point in time. Once all this information has been received from both databases (MongoDB Realm and Bitly. it can be combined into one cplection and displayed on a website or another application for analysis by anyone who wishes to perform it.
In this method, classes will be created in Gpang to represent records in MongoDB Realm and records in Bitly using GORM modules. These classes will implement methods that can be called from inside other classes in order to add records into MongoDB Realm and retrieve records from Bitly when needed. Both records in Bitly and records in MongoDB Realm will be added in order to allow for later retrieval of analytics information about the number of clicks that have been made on any given URL till that point in time. This information can be used for analysis by anyone who wishes to perform it.
This method will include steps similar to those mentioned above except that instead of using an ORM like GORM to create mappings between records in MongoDB Realm and records in Bitly, libraries like mgo (An open source database driver for MongoDB using Go. will be used to connect directly to MongoDB Realm and Bitly databases so that queries can be run against them whenever necessary without having to go through an ORM like GORM. From the above example it is clear that this method would prove more efficient than either of the first two methods because it would allow for faster data retrieval by removing several layers between MongoDB Realm and Bitly APIs. Also, if integration is done with DynamoDB instead of MongoDB Realm then it would ensure that there is no downtime even during updates of records since all operations on DynamoDB are performed asynchronously without affecting other operations on the same table (more information can be found here. However, this method is not recommended for real world implementations because not all companies need millisecond accuracy of analytics information about clicks on URLs like some companies do (for example Google Analytics which cplects click data at least once every 5 minutes. For these companies DynamoDB would not be able to provide millisecond precision since it performs operations asynchronously which may introduce small time lags between two consecutive operations on the same table which might go unnoticed by some applications but may prove problematic for others where precision is mandatory even at the cost of slightly slower performance compared to using synchronous methods that operate within milliseconds instead of seconds or minutes depending on how complex the query being executed is. Customers who are affected by time lags between two consecutive operations on one table are advised to contact AWS Support groups so that they can provide assistance in choosing the right NoSQL database for your company’s needs instead of trying to use DynamoDB even if they are not affected by time lags at all because time lags are completely avoidable if proper care is taken while making sure that all operations on DynamoDB are performed asynchronously without affecting other operations on the same table (more information regarding this topic can be found here. Companies which are not affected by time lags between two consecutive operations on one table are free to use DynamoDB since they are assured by AWS Support groups that they are not affected by time lags which are completely avoidable if proper care is taken while making sure that all operations on DynamoDB are performed asynchronously without affecting other operations on the same table (more information regarding this topic can be found here. If companies face problems related to availability or reliability during normal operation hours then they should contact AWS Support groups so that they can provide assistance in choosing the right NoSQL database for your company’s needs instead of trying to use DynamoDB even if they are not affected by these problems at all because neither availability nor reliability issues are completely avoidable if proper care is not taken while making sure that all operations on DynamoDB are performed asynchronously without affecting other operations on the same table (more information regarding this topic can be found here.
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