Exploring MongoDB 7.0 with MongoDB Compass: A Comprehensive Guide

Overview MongoDB - General Introduction MongoDB 7.0 New Features MongoDB Compass and its features Installation and set-up (tutorial starts here)Data Operations About Authors Sai Manasa Ivaturi Srinivas vaddi

Overview

In this article we will do a deep dive into how we could leverage MongoDB Compass to learn how to build queries with filters, aggregations, and pipelines and create complex queries in the pipeline with stage manager and also learn about the new features of MongoDB 7.0 such as time series collections, resumable index builds, schema analysis and more.

This tutorial is intended for anyone who has a basic knowledge of MongoDB and its terminology. Only a laptop is required to work and follow this tutorial.

In this section, we will try to get an overview of what we are going to cover in this tutorial and what we will be learning.

MongoDB - General Introduction

A database is a system that stores and manages data in a structured way. There are different types of databases, such as relational databases, which use tables with fixed schemas, and non-relational databases, which use other data models, such as key-value pairs, documents, graphs, etc. These non-relational databases are also known as NoSQL databases.

One of the most popular NoSQL databases is MongoDB, which is a document database. MongoDB stores data in collections, which are containers that hold documents. Each document is like a file that contains information about an item, and each item can have different fields and values. Unlike relational databases, documents do not have to follow a fixed structure or format. They can have different fields and values depending on the item.

For example, a collection of contacts might have documents for each person, and each document might have fields for their name, phone number, email, hobbies, etc. Some documents can have additional fields for address, state, zip code, etc.

You can learn more about the differences between SQL and NoSQL databases.

MongoDB 7.0 New Features

MongoDB 7.0 brings many changes and improvements to the database, such as:

Queryable Encryption General Availability: A new feature of MongoDB that allows organizations to encrypt data while it is being queried and in use, using an approximate method based on the t-digest algorithm. This feature enhances data security and privacy without compromising performance or functionality.

New Aggregation Operators ($median, $percentile): Two new operators in MongoDB 7.0 that calculate approximate percentile-based metrics for numeric data, using the same method as Queryable Encryption. These operators can help you analyze your data and find outliers, trends, or patterns.

Time series collections: These are special collections that store time-series data, such as sensor readings, stock prices, or weather data. Time series collections offer better performance, compression, and analysis for this type of data. You can use them to monitor changes over time, forecast future events, or detect anomalies.

Resumable index builds allow you to resume an interrupted index build operation without starting from scratch. This can save time and resources in case of network failures, power outages, or maintenance windows. You can also pause and resume index builds manually if needed.

Schema analysis: This new feature in MongoDB Compass allows you to understand the structure and distribution of your data across different fields and documents. You can use schema analysis to identify data quality issues, optimize your queries and indexes, and discover new insights from your data.

Some of the above content about new features has been directly quoted from MongoDB 7.0 release notes.

MongoDB Compass and its features

MongoDB Compass is a special utility that would allow us to visually interact with the data. Depending on what we want to do, we can do a range of things like -

🛠️ Design and test aggregation pipelines!
💻 Generate and export query pipelines to code!
📥 Import and export data from various sources and formats
🖥️ Monitor and troubleshoot your MongoDB server
🌐 Connect to different types of MongoDB deployments, such as Atlas, replica sets, and sharded clusters

Other features include

Secure your data with authentication and encryption.
Explore data schema and find anomalies
Create and run complex queries without writing code, just queries!
Optimize your indexes and improve performance!

Installation and set-up (tutorial starts here)

We will need to install MongoDB and MongoDB compass.

To avoid the complexity involved in installation and configuration we will use the official MongoDB 7.0 docker image and install the MongoDB compass as an application.

We will be using a custom dataset that has the following schemas

Data

While using datasets on Kaggle or elsewhere can be easy and helpful, we’ve decided to make our very own dataset to do better. Here is a brief about our schema which will help us understand things from a more practical perspective.

We are creating a sample inventory data set for a manufacturer. It should have the following collections with sample data. Note, schema might change in course of developing this tutorial but this is roughly going to remain same.

Suppliers