Given the popularity of both Java and MongoDB, it is no surprise that they work well together.
Whether you are developing on a local or on-premise MongoDB installation, or going cloud-first with MongoDB Atlas, the MongoDB Java Driver and surrounding ecosystem make it a breeze to integrate MongoDB into your development process.
In this article, we'll dive into the following:
Java is one of the most popular programming languages, and with good reason. First released in 1995, Java has maintained a considerable hold on the developer community since. It's a great fit for a number of industries, including financial services, healthcare, and even the public sector. Java is object-oriented and WORA (write once, run anywhere), so it can be run on all platforms that support the language without needing to recompile.
MongoDB holds the top spot as the most popular NoSQL database technology on several lists. It is a document database used to build highly available and scalable internet applications. Founded in 2007, MongoDB has a worldwide following in the developer community. MongoDB has always focused on providing developers with an excellent user experience, which, in addition to all its other features, has made MongoDB a favorite of developers worldwide.
If you're building something new and need a database, consider using MongoDB Atlas from the outset. Atlas will give you a fully-managed, cloud-native database service that comes with a number of features, including full-text search, charts, partner integrations, and much more. If you're building for iOS or Android, Atlas Device Sync makes mobile synchronization a snap. You can even build web applications on GraphQL directly on top of MongoDB Atlas.
Make sure that you have Java 8 (or a later version) installed, and your preferred IDE ready.
The first thing you'll want to do is declare a dependency on the latest version of the MongoDB Java Driver. Instructions for how to do this using either Gradle or Maven, along with example code, can be found here.
Once your Java project dependencies are all set up, you'll need a database.
You can create an account and create a free-tier cluster on MongoDB Atlas by following this guide. It will also show you how to insert sample data.
After the previous section, you should have a new MongoDB cluster deployed in Atlas, a new database user and password, and some sample datasets loaded into your cluster.
Now, it's time to connect to a database using your driver. In order to establish a database connection, your driver needs instructions on how to connect. These instructions are called the connection string. You can get your connection string for the cluster and username you created in the previous step by going into your Atlas account, navigating to the Database section, and clicking "Connect".
A pop-up will appear prompting you to select your driver and version. Once you've populated both of those drop-downs, you can use the resulting database connection string (it'll begin with "mongdb+srv://") to connect to your cluster. Note that you will need to swap in the values for your own username and password!
You can use this connection string to run queries on your MongoDB cluster from your application. You can find a more in-depth set of instructions as well as example code here.
Unlike SQL databases, MongoDB stores information in documents and collections.
MongoDB stores data records as BSON documents. BSON is a binary representation of JSON documents, though it contains more data types than JSON. Documents are composed of field-value pairs, with the following structure:
The values associated with fields can be any BSON data type, including other documents, arrays, and even arrays of documents. Field names themselves are strings.
Documents are much more flexible than standard rows in relational databases, as they give you a dynamic schema rather than an enforced one (though you can do schema validation with MongoDB if you want!).
MongoDB stores documents in collections. A collection is like a table in a relational database. For more details on how to create a collection, and other information about collections, you can check out the documentation here.
CRUD (create, read, update, and delete) operations are the four basic operations you might want to do with a database. In the case of MongoDB, you can use CRUD operations to insert documents, to write queries for matching documents, to update documents, and to delete documents. You can do this by using MQL, the MongoDB Query Language. Unlike SQL statements, MQL uses the same syntax as documents, making it intuitive and easy to use for even advanced querying.
A full CRUD tutorial as well as example code for each of the four operations using the Java driver can be found in the MongoDB Developer Center.
Chances are, your tech stack is more than just your IDE and your favorite version of Java. Below are just some of the many ways you can use MongoDB.
Spring Data MongoDB is part of the umbrella Spring Data project that aims to provide a familiar and consistent Spring-based programming model for new datastores, while retaining store-specific features and capabilities. It provides integration with the MongoDB database, a POJO-centric model for interacting with MongoDB collections, and more.
Hibernate was created to map object-oriented Java code to relational databases while accommodating their constraints. This allowed Java developers to focus more on writing code and less on the tedious, often error-prone process of translating their Java objects into relational persistence. The MongoDB Extension for Hibernate ORM, now in general availability, gives Java developers the freedom and flexibility to build modern applications using MongoDB’s document model and Hibernate’s user-friendly features, including Entity annotations, Hibernate Query Language (HQL) support, Criteria Queries, and more. The extension also comes with JOIN support (INNER and LEFT OUTER), optimistic locking, Spring Boot auto-configuration (no manual overrides required), and improved exception messaging and logging, making it ready for production workloads.
When it comes to public cloud providers, there are three main providers that come to mind -- Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). MongoDB Atlas supports all three main cloud service providers. It even supports multi-cloud and multi-region deployments. Learn more here.
Terraform is an open-source infrastructure as code software tool that enables you to safely and predictably create, change, and improve infrastructure. The HashiCorp Terraform MongoDB Atlas Provider is an official plugin built by HashiCorp that allows you to provision, manage, and control Atlas configurations on any cloud provider using Terraform.
Atlas can send metric data about your project to Datadog dashboards or to your Prometheus instance. You can also configure Atlas to send information to Flowdock, Microsoft Teams, OpsGenie, PagerDuty, Slack, and others. You can find more details on this here.
The best part of using MongoDB is the vibrant Java community that includes users with all levels of experience with the Java driver. The best way to get support for general questions is to use MongoDB Community Forums.
If you're running into an unexpected error, you think you've found a bug in the Java driver, or you have a feature request, please open a JIRA ticket on the JAVA project. You can find instructions on how to do this in the documentation. Bug reports for both the Java driver and the Core Server are public.
MongoDB is a data platform that is compatible with several languages, including Java. MongoDB is a great fit for applications written in Java.
We're happy to accept contributions to help improve the driver. We will guide user contributions to ensure they meet the standards of the driver codebase. Please ensure that any PRs include documentation, tests, and pass checks.
Please note that support for SQL queries is available as a Preview feature only. You can query a federated database using Atlas SQL. To run queries using the SQL interface, you can connect using the MongoDB JDBC driver available here or one of the BI Tool Named Connectors. More details on this Preview feature can be found in the article here.