Book Image

Mastering MongoDB 6.x - Third Edition

By : Alex Giamas
Book Image

Mastering MongoDB 6.x - Third Edition

By: Alex Giamas

Overview of this book

MongoDB is a leading non-relational database. This book covers all the major features of MongoDB including the latest version 6. MongoDB 6.x adds many new features and expands on existing ones such as aggregation, indexing, replication, sharding and MongoDB Atlas tools. Some of the MongoDB Atlas tools that you will master include Atlas dedicated clusters and Serverless, Atlas Search, Charts, Realm Application Services/Sync, Compass, Cloud Manager and Data Lake. By getting hands-on working with code using realistic use cases, you will master the art of modeling, shaping and querying your data and become the MongoDB oracle for the business. You will focus on broadly used and niche areas such as optimizing queries, configuring large-scale clusters, configuring your cluster for high performance and availability and many more. Later, you will become proficient in auditing, monitoring, and securing your clusters using a structured and organized approach. By the end of this book, you will have grasped all the practical understanding needed to design, develop, administer and scale MongoDB-based database applications both on premises and on the cloud.
Table of Contents (22 chapters)
1
Part 1 – Basic MongoDB – Design Goals and Architecture
4
Part 2 – Querying Effectively
11
Part 3 – Administration and Data Management
16
Part 4 – Scaling and High Availability

Time series collections

A time series collection is a special type of collection that is used to collect data measurements over a period of time.

For example, time series collection use cases can include storing Internet of Things (IoT) sensor readings, weather readings, and stock price data.

A time series collection needs to be created as such, and we cannot change a collection type into a time series one. Migrating data from a generic purpose collection to a time series one can be done using a custom script or MongoDB’s own Kafka connector for performance and stability.

To create a time series collection, we need to specify the following fields. In this context, a data point might refer to a sensor reading or the stock price at a specific point in time:

  • timeField: This field is mandatory and is the field that stores the timestamp of the data point. It must be a Date() object.
  • metaField: This field is optional and is used to store metadata for the data...