Chapter 6. Understanding the Concept of Sharding

After learning about replication and replica sets, we can jump into the more complex concepts of MongoDB. In this chapter, we will talk about one of the most valuable MongoDB features, that is, sharding. Using this feature, you can spread your data across different servers to distribute operation pressure between different machines and provide better performance.

In this chapter, we will learn the basic terms and concepts of sharding. In the next chapter, we will configure and set up a server from scratch.

Understanding scaling

When you are using MongoDB in a production environment and under high throughput, you may face a lack of server resources. Storing large datasets on a single machine can be the biggest problem. In addition, responding to a lot of read/write requests from different clients can be a concern because these kinds of processes are resource-consuming and can exhaust the server's resources.

To address these problems, scale your database system to support more clients. Basically, there are two ways to scale a system, vertical scaling and horizontal scaling.

In the vertical scaling approach, the system administrator will add more resources and capacities to an existing server. However, adding more capacity to an existing system has some limitations, making this approach less useful. Adding more CPU cores or RAM capacity to a machine can be more expensive than setting up a new smaller machine. Meanwhile, some cloud-based service providers don't offer very large capacity machines...

Learning about sharding

The good news is that MongoDB has come up with a clever solution to scale your database system easily. Sharding is a technique used to scale a database horizontally.

Using sharding, the entire database is divided into separate single databases on different machines. Finally, using a query router, all the separate nodes make a single logical database.

Let's look at an example of sharding. Suppose we have a database sized 1 TB. Storing such a large dataset on a single server is a big challenge, so we use sharding to divide the entire database into smaller instances called shards.

In order to reduce the size of the entire database, we divide it into databases sized 256 GB. During the sharding procedure, each part of the database is divided using a shard key and other options that configure settings for shards such as the size of the dataset.

The following diagram illustrates the example of a 1 TB database being sharded across four instances:

The benefits of using sharding...

Summary

In this chapter, you learned the basic terms, concepts, and components of creating a sharded cluster and using the sharding technique in MongoDB. First, we discussed the different methods of scaling a database system, vertical and horizontal scaling. Then, we introduced the sharding method, which is the horizontal scaling solution for MongoDB.

Also, you learned about the different parts and components of sharding, shards, query routers, and configuration servers. Next, you learned one of the important parts of the sharding procedure, the shard key. Then, all the techniques available to define a shard key were introduced and in the next section, we talked about performance difference between methods. Furthermore, we discussed splitting and balancing processes, which help with maintenance and retention of the balance of data size and chunks in the shards.

In the next chapter, we will configure and establish a sharded cluster from scratch using terms and concepts that we have learned...