#Mongodb compass date greater than how to#
#Mongodb compass date greater than install#

#Mongodb compass date greater than install#

If you want to use this approach instead, you’ll need to install the MongoDB Database Tools and consult the online documentation.

#Mongodb compass date greater than how to#

I’m going to talk you through how to achieve this using MongoDB Compass, but you can also import data from the command line using the mongoimport tool. In this section, we will create a new database and import some sample data that we can work with. Now that we understand how MongoDB data is stored and how it is formatted, let’s proceed to get some sample data imported into our development environment so that we can try out some queries. In addition to the standard JavaScript data types, the BSON format supports additional types such as ObjectID, Integer, and Decimal128. The above document features a mixture of data types, including String, Array, Date, and an embedded object. However, underneath, the data is stored in a special BSON format to allow for efficient parsing and indexing of the data. Īs you can see, the document is essentially JSON. Document exampleīelow is an example of a MongoDB document. The Schema Validation feature allows a specific schema to be enforced when inserting or updating documents. MongoDB is very flexible by default, but there are ways of locking things down when needed. If you’re worried about the fact that documents may not be consistent, don’t worry.

MongoDB provides ways of taking this into account when querying data to cope with optional fields and other differences between documents. However, some of these documents may contain additional fields that are not found in other documents.

If you’re dealing with existing MongoDB databases, most of the time, you’ll find that documents in the same collection will use the same field names. However, it does usually make sense to keep the schema as consistent as possible for documents that are stored within the same collection. MongoDB documents do not necessarily need to have the same schema. When describing the schema of related documents in the previous sub-section, note that I used the word ‘similar’, as opposed to ‘same’. Dynamic schemaĭocuments can have a dynamic schema, so instead of predefined table columns, MongoDB documents contain fields that are key-value pairs of data. Instead of the rows that exist within relational tables, MongoDB has ‘documents’ that are organised within collections. Unlike relational databases, instead of tables, MongoDB features ‘collections’.Ĭollections act as containers to organise related documents together that have a similar schema. Let’s cover a couple of basic MongoDB concepts before we start executing some queries, specifically how MongoDB differs from relational databases. Head over to to the HumongouS.io MongoDB Playground where you can input custom database contents and run queries to learn how MongoDB works online. I recommend that you check out the prior post so that you are fully up to speed on MongoDB and have an environment ready that will allow you to execute the queries that I’ll be showing you.Īlternatively, perhaps you don’t want to set up MongoDB right now and you just want to try it out to see if it’s for you. In my Setting up a MongoDB development environment blog post I cover in detail the background behind MongoDB and how to set things up. MongoDB is an open-source NoSQL database and its history goes back as far as 2007 when it was first developed by a company called 10gen. My aim in this article is to clear up the mystery and show you how to get started quickly with querying MongoDB databases.

Working with MongoDB can sometimes seem like wizardry to developers coming from a relational background. This is particularly true in regards to database queries since MongoDB features its own unique query API that is completely different from using a traditional query language like SQL. As a document-oriented database, MongoDB is very different from relational databases such as SQL Server or PostgreSQL.