Sharding the table to scale the database

Replication puts the same copy of the table in different RethinkDB instances in the cluster, while sharding splits the data and puts it in a different cluster. As we have studied in Chapter 1, RethinkDB Architecture and Data Model, RethinkDB uses the range sharding algorithm to perform the splitting of records.

You can refer to that chapter for more details on the algorithm; in this section, we will be doing sharding in our cluster.

So let's take our cluster again and perform sharding of a table. To do so, again we have two options; either do it via a web console or ReQL. I am going to use a web console for the same.

So, as you can see in the following image, we have about 900 documents in the table with random IDs (remember the mock data we generated in Chapter 3, Data Exploration Using RethinkDB?). The reason I am mentioning ID here is that the range sharding algorithm is going to partition our data on the basis of IDs.

For ease of understanding, let...