Gain insights into distributed systems, learn architectural patterns like replication and sharding, and explore Apache Spark and Druid to design and develop effective distributed applications.

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Gone are the days when monolithic applications were the norm. Most applications that we use today are too complex for that type of construction. Instead, most applications that appear as a single unit are actually built as a collection of inter-operating but independent computational components. Developers are expected to have mastery over a programming language or two. Moreover, they should also know the basics of distributed systems in order to succeed.

In this course, you’ll learn what a distributed system is, followed by the challenges unique to distributed systems. You’ll cover popular architectural patterns for achieving targets like replication and sharding. You’ll finish with a study of two popular distributed systems: Apache Spark and Apache Druid.

With the knowledge gained through this course, you’ll be able to design and develop distributed systems. You’ll be able to make optimal design and architectural choices based on the application requirements.

Introduction to Distributed Systems for Dummies

Consistent hashing is a popular algorithm and databases such as DynamoDB, Cassandra, ScyllaDB, and Riak use this algorithm to scale systems. In this lesson, we will explain the algorithm.

# What is consistent hashing?

> Consistent hashing is a hashing algorithm in which only $k/n$ keys are remapped if the number of nodes changes. Here $k$ is the number of keys and $n$ is the number of slots, i.e., nodes.

In traditional hashing algorithms, a change in the number of slots requires many keys to be remapped. In the consistent hashing algorithm, the remap is tolerable, which is why databases have adopted this strategy.

Let's discuss.

## The hash ring

In general hashing, the keys are mapped to a one-dimension space. Assuming the hash key range is $0$ to $2^{32}-1$, the keyspace is a one-dimensional array.



Consistent Hashing

Introduction

What Distributed Systems Achieve for Us

Data in Distributed Systems

Communication Between Nodes

Data Processing in Large Scale

Distributed System Architectural Patterns

Case Study 1: Apache Spark

Case Study 2: Apache Druid

Conclusion

Consistent Hashing

What is consistent hashing?