Introduction

In the world of distributed systems, it's important to keep everything running smoothly, even if a server or node stops working. That's why the idea of leader election is very important.

The Need for Leader Election

Think of a well-known subscription service, like Netflix, where users are charged regularly through payment services like Stripe or PayPal. To handle transactions without sharing user credit card information with the payment service, a middleman service steps in to manage the charges.

However, using just one middleman service creates a risk. If it fails, all transactions stop, which can mess up the subscription system. To avoid this, we use several instances of the service for backup. But this leads to a new problem: we need to make sure only one instance (the leader) processes transactions at a time to prevent charging customers more than once.

The Process of Electing a Leader

Leader election is how different parts of a system agree on choosing a leader to manage tasks, like our service that handles credit card charges. If the leader stops working, they pick a new one to keep things running smoothly.

But, deciding on the new leader isn't easy. Problems like network partitions (when parts of the system can't talk to each other) and the split-brain scenario (when two parts pick different leaders, causing confusion) make the process challenging.

Achieving Consensus

Consensus algorithms make sure that all nodes in a distributed system agree on one main truth, even if there are failures or network problems. Choosing a leader is part of this process, where one node is picked to be the leader.

Popular Consensus Algorithms:

• Paxos: This algorithm is a basic way to reach agreement but is often seen as complex to understand and put into practice.

• Raft: Known for being easy to understand, Raft simplifies the process of reaching a consensus.

• Zab: This is used by Apache ZooKeeper for choosing a leader, aiming for fast and reliable communication.

Using Distributed Consensus Services

Creating consensus algorithms from the ground up is tough and usually not needed. Instead, distributed systems use existing libraries and tools that include these algorithms, offering ready-made solutions for things like choosing a leader.

Examples of Distributed Consensus Services

• ZooKeeper: Provides coordination services for distributed applications, including leader election, configuration management, and synchronization.

• etcd: A highly available, strongly consistent key-value store used for shared configuration and service discovery, using the Raft consensus algorithm.

• Consul: Offers service discovery, configuration, and orchestration capabilities, also based on the Raft algorithm.

Case Study: etcd

Etcd is a great example of a distributed key-value store that guarantees strong consistency, which is very important for choosing a leader. In our case of a subscription service, using etcd makes sure that at any moment, everyone agrees on who the main middleman service is. This stops users from being charged more than once for the same billing period.

Strong consistency in etcd means that every time you read or write data, you get the latest information. This is essential to make sure that when a new leader is chosen, the whole system knows about it right away.