How to Use Azure Functions with MongoDB Atlas in Java
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Cloud computing is one of the most discussed topics in the tech industry. Having the ability to scale your infrastructure up and down instantly is just one of the many benefits associated with serverless apps. In this article, we are going write the function as a service (FaaS) — i.e., a serverless function that will interact with data via a database, to produce meaningful results. FaaS can also be very useful in A/B testing when you want to quickly release an independent function without going into actual implementation or release.
In this article, you'll learn how to use MongoDB Atlas, a cloud database, when you're getting started with Azure functions in Java.
- A Microsoft Azure account that we will be using for running and deploying our serverless function. If you don't have one, you can sign up for free.
- A MongoDB Atlas account, which is a cloud-based document database. You can sign up for an account for free.
- IntelliJ IDEA Community Edition to aid our development activities for this tutorial. If this is not your preferred IDE, then you can use other IDEs like Eclipse, Visual Studio, etc., but the steps will be slightly different.
- A basic understanding of the Java programming language.
Getting started with the Azure serverless function is very simple, thanks to the Azure IntelliJ plugin, which offers various features — from generating boilerplate code to the deployment of the Azure function. So, before we jump into actual code, let's install the plugin.
The Azure plugin can be installed on IntelliJ in a very standard manner using the IntelliJ plugin manager. Open Plugins and then search for "Azure Toolkit for IntelliJ" in the Marketplace. Click Install.
With this, we are ready to create our first Azure function.
Now, let's create a project that will contain our function and have the necessary dependencies to execute it. Go ahead and select File > New > Project from the menu bar, select Azure functions from Generators as shown below, and hit Next.
Now we can edit the project details if needed, or you can leave them on default.
In the last step, update the name of the project and location.
With this complete, we have a bootstrapped project with a sample function implementation. Without further ado, let's run this and see it in action.
We can deploy the Azure function either locally or on the cloud. Let's start by deploying it locally. To deploy and run locally, press the play icon against the function name on line 20, as shown in the above screenshot, and select run from the dialogue.
Copy the URL shown in the console log and open it in the browser to run the Azure function.
This will prompt passing the name as a query parameter as defined in the bootstrapped function.
Update the URL by appending the query parameter
name to
http://localhost:XXXXX/api/HttpExample?name=World, which will print the desired result.
In the previous step, we created our first Azure function, which takes user input and returns a result. But real-world applications are far more complicated than this. In order to create a real-world function, which we will do in the next section, we need to
understand how to connect our function with a database, as logic operates over data and databases hold the data.
Similar to the serverless function, let's use a database that is also on the cloud and has the ability to scale up and down as needed. We'll be using MongoDB Atlas, which is a document-based cloud database.
Creating an Atlas account is very straightforward, free forever, and perfect to validate any MVP project idea, but if you need a guide, you can follow the documentation.
The Azure function uses multiple IP addresses instead of a single address, so let's add them to Atlas. To get the range of IP addresses, open your Azure account and search networking inside your Azure virtual machine. Copy the outbound addresses from outbound traffic.
One of the steps while creating an account with Atlas is to add the IP address for accepting incoming connection requests. This is essential to prevent unwanted access to our database. In our case, Atlas will get all the connection requests from the Azure function, so let's add this address.
Add these to the IP individually under Network Access.
There are various ways of interacting with Atlas. Since we are building a service using a serverless function in Java, my preference is to use MongoDB Java driver. So, let's add the dependency for the driver in the
build.gradle file.With this, our project is ready to connect and interact with our cloud database.
With all the prerequisites done, let's build our first real-world function using the MongoDB sample dataset for movies. In this project, we'll build two functions: One returns the count of the
total movies in the collection, and the other returns the movie document based on the year of release.
Let's generate the boilerplate code for the function by right-clicking on the package name and then selecting New > Azure function class. We'll call this function class
Movies.Now, let's:
- Update
@FunctionNameparameter fromMoviestogetMoviesCount. - Rename the function name from
runtogetMoviesCount. - Remove the
queryandnamevariables, as we don't have any query parameters.
Our updated code looks like this.
To connect with MongoDB Atlas using the Java driver, we first need a connection string that can be found when we press to connect to our cluster on our Atlas account. For details, you can also refer to the documentation.
Using the connection string, we can create an instance of
MongoClients that can be used to open connection from the database.We can query our database for the total number of movies in the collection, as shown below.
Updated code for
getMoviesCount function looks like this.
Copy the URL from the console output and paste it on Postman to validate the output.
Let's deploy this on the Azure cloud on a
Linux machine. Click on Azure Explore and select Functions App to create a virtual machine (VM).
Now right-click on the Azure function and select Create.
Change the platform to
Linux with Java 1.8.
If for some reason you don't want to change the platform and would like use Window OS, then add standard DNS route before making a network request.
After a few minutes, you'll notice the VM we just created under
Function App. Now, we can deploy our app onto it.
Press Run to deploy it.
Once deployment is successful, you'll find the
URL of the serverless function.
Again, we'll copy this
URL and validate using Postman.
With this, we have successfully connected our first function with
MongoDB Atlas. Now, let's take it to next level. We'll create another function that returns a movie document based on the year of release.
Let's add the boilerplate code again.
To capture the user input year that will be used to query and gather information from the collection, add this code in:
To use this information for querying, we create a
Filters object that can pass as input for find function.The updated code is:
Now let's validate this against Postman.
The last step in making our app production-ready is to secure the connection
URI, as it contains credentials and should be kept private. One way of securing it is storing it into an environment variable.Adding an environment variable in the Azure function can be done via the Azure portal and Azure IntelliJ plugin, as well. For now, we'll use the Azure IntelliJ plugin, so go ahead and open Azure Explore in IntelliJ.
Then, we select
Function App and right-click Show Properties.
This will open a tab with all existing properties. We add our property into it.
Now we can update our function code to use this variable. From
to
After redeploying the code, we are all set to use this app in production.
Thank you for reading — hopefully you find this article informative! The complete source code of the app can be found on GitHub.
If you're looking for something similar using the Node.js runtime, check out the other tutorial on the subject.
With MongoDB Atlas on Microsoft Azure, developers receive access to the most comprehensive, secure, scalable, and cloud–based developer data platform on the market. Now, with the availability of Atlas on the Azure Marketplace, it’s never been easier for users to start building with Atlas while streamlining procurement and billing processes. Get started today through the Atlas on Azure Marketplace listing.
If you have any queries or comments, you can share them on the MongoDB forum or tweet me @codeWithMohit.
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