A running MongoDB database, ready in about ten seconds. Pick a sample dataset when you launch and the workspace boots with the collections already loaded, so you can connect with mongosh from VS Code and start querying right away.
Choose a dataset at launch and the database comes up with the collections imported as JSON documents. Options include Employees, USA Zip Codes, Times Person of the Year and Cats in movies.
The full cloud editor with an integrated terminal, so you can open mongosh and run queries without leaving the browser.
The instance launches in about ten seconds with the sample data already loaded, so there's nothing to install or import.
Run finds, filters and aggregation pipelines against realistic documents, or start from an empty database and shape your own collections.
MongoDB is a popular open-source NoSQL database. Instead of rows and columns, it stores data as flexible JSON-like documents grouped into collections, which makes it a natural fit for data that doesn't sit neatly in a fixed table shape. You query it with the MongoDB query language and its aggregation framework.
This workspace gives you a real MongoDB server that's already running. Rather than installing the database locally and importing files yourself, you pick a sample dataset at launch and the workspace boots with those collections in place, so you can get straight to querying.
You get a live MongoDB instance and a cloud VS Code editor with a terminal. When you launch, you choose one of the sample datasets and the database comes up with the matching collections loaded from JSON documents.
The Employees dataset has departments, employees, leaves and salaries as separate collections. USA Zip Codes is a collection of US cities with population and coordinates. Times Person of the Year holds each year's honoree with country and category. Cats in movies catalogs films featuring cats. Prefer to start clean? Pick None and you get an empty database to build in.
Open the terminal in VS Code, start mongosh against the running instance and query away. Try find() with filters on the sample collections, sort and project fields, or build an aggregation pipeline. You can also insert your own documents and create new collections. The instance is sized for learning and prototyping at 50MB across up to 20 collections, so it fits experiments rather than production loads.
Practice MongoDB queries and aggregations against realistic documents, prototype a data model for a side project, test a pipeline before running it elsewhere, or follow a MongoDB tutorial without setting up a database first.
You can launch with Employees, USA Zip Codes, Times Person of the Year, or Cats in movies. Each is imported as one or more collections of JSON documents.
Yes. Pick None at launch and you get an empty MongoDB database with no sample collections, ready for you to create your own.
Open the integrated terminal in VS Code and start mongosh against the running instance, then run your queries. It all happens inside the browser workspace.
The instance is meant for practice and prototyping, with around 50MB of storage and up to 20 collections. It's great for sample data and experiments, not production workloads.
About ten seconds. The sample data is imported during launch, so the database is ready to query as soon as the workspace opens.
No, MongoDB is a paid template and needs a paid plan. Templates that are free are marked as such. You can upgrade from the pricing page to launch it.
