Take a quick refresher on the essentials of distributed systems, including core concepts and theorems.

Deep Dive into System Design Interview

This module talks about establishing the basic principles of distributed systems. It explains the scope of their functionality by discussing what distributed systems can and can’t achieve. It also covers the basic algorithms and protocols of distributed systems through easy-to-follow examples and diagrams, which illustrate the thinking behind design decisions and expand on how they can be practiced.

Distributed Systems

One of the major benefits of distributed systems is **scalability**.
# Scalability 
Scalability lets us store and process datasets much larger than what we could with a single machine.

# Mechanism to achieve scalability
One of the primary mechanisms of achieving scalability is **partitioning**.
## Partitioning
Partitioning is the process of splitting a dataset into multiple, smaller datasets, and then assigning the responsibility of storing and processing them to different nodes of a distributed system. This allows us to add more nodes to our system and increase the size of the data it can handle.

There are two different variations of partitioning:
1. Vertical partitioning
2. Horizontal partitioning (or **sharding**)

The terms “vertical” and “horizontal” originate from the era of relational databases that established the notion of a tabular view of data.

In this view, data consists of rows and columns, where each row is a different entry in the dataset, and each column is a different attribute for every entry.
 
The following illustration contains a visual depiction of the difference between **vertical partitioning** and **horizontal partitioning**.

See how we can make our system scalable by partitioning.