Welcome! In this unit, we’ll take a deeper look at how indexes work in MongoDB. My name is Jordan, I am an education engineer here at MongoDB, and I will guide you through this unit.

Before we begin, let's recap what indexes are.

An index is a data structure that stores a subset of the data from a collection in a format that makes it easier for the database to efficiently locate and retrieve documents from disk.

Using indexes in MongoDB can optimize query performance by reducing the time it takes the database to apply the query’s filter and sort conditions, which will make your application operations complete faster.

In this unit, we’ll first start off by learning how MongoDB indexes use a B-tree data structure to store and retrieve indexed values.

Next, we’ll use the explain helper method to better understand if and how operations are supported by indexes.

Then, we’ll go through an in depth example of how we can optimize a compound index by leveraging the explain method.

After that, we’ll learn about some more index types, such as wildcard indexes which are special indexes that support queries against unknown or arbitrary fields.

Next, we’ll cover partial indexes which only index documents from a collection that match a filter expression.

We’ll also discuss sparse indexes which only contain entries for documents that have the indexed field.

Finally, we will cover clustered indexes, and see them in action with our Time Series collection.

Once we’re done learning about some of our different index types, we'll cover how to monitor index usage.

Before taking this unit, you should have knowledge of the document model, MongoDB, and a desire to take your database management skills to the next level. We also advise that you have taken our other unit on indexes.

Let's get started!