How do computers elect leaders? | Consensus and Leader Election Explained

Summary

This video explores leader election and consensus in distributed systems, using a photo-sharing app as a practical example. It explains how multiple databases can stay synchronized without conflicts through leader election, focusing on the RAFT algorithm widely used in systems like Kafka, etcd, and MongoDB. The video details how RAFT elects a leader, maintains heartbeats, and replicates data reliably to achieve consensus, ensuring system reliability even with failures. Viewers also learn the difference between safety and liveness properties, practical applications in data replication, and how these concepts underpin many critical systems in our daily digital lives.

Takeaways

• 😀 Over 150 elections occur worldwide every year, but computers run even more elections daily, ensuring the reliability of systems like payment methods and photo sharing apps.
• 😀 Leader Election is the process where distributed systems select a leader to coordinate activities like resource allocation, task scheduling, and algorithm management.
• 😀 In distributed systems, having multiple databases ensures better traffic distribution and reliability, but coordination is needed to prevent data conflicts.
• 😀 RAFT is a leader election algorithm used in systems like Kafka, etcd, and MongoDB, ensuring coordination even when some nodes fail.
• 😀 In RAFT, nodes start as followers and can transition to candidates if no leader is elected. A new leader is chosen if a majority agrees on the candidate's term.
• 😀 Consensus is vital in distributed systems to ensure all nodes agree on critical decisions like committing transactions or choosing configurations.
• 😀 The properties of Safety and Liveness in consensus guarantee that no bad things happen (Safety) and that good things will eventually happen (Liveness).
• 😀 RAFT uses data replication to ensure consistency across nodes. A leader manages the process, ensuring logs are replicated across all follower nodes in the same order.
• 😀 In case of failure, the system can tolerate up to half the followers failing, as long as there are enough remaining to elect a new leader and continue replication.
• 😀 Systems like RAFT help ensure data availability in apps (e.g., photo sharing apps) by making sure data is stored and replicated across multiple nodes.
• 😀 While leader election and consensus may seem complex, many systems, like key-value stores, can handle coordination automatically, and tools like CodeCrafters can help you practice these concepts.

Q & A

What is leader election in distributed systems?

Leader election is the process by which a group of processes or nodes in a distributed system select a single leader among them to coordinate and manage the system's activities, such as resource allocation and task scheduling.

Why is leader election important in distributed databases?

Leader election ensures that there is a single node responsible for coordinating actions, preventing conflicts, and ensuring efficient data storage and updates across multiple database nodes.

What is the RAFT algorithm and where is it used?

RAFT is a consensus and leader election algorithm used in distributed systems to ensure reliable data replication. It is used in systems like Kafka, etcd, and MongoDB.

How does RAFT elect a leader?

In RAFT, all nodes start as followers. If there is no leader, any node can become a candidate by announcing its name and term. Nodes vote, and if a majority agree, the candidate becomes the leader. Heartbeats maintain the leadership, and new elections are triggered if heartbeats stop.

What are the Follower, Candidate, and Leader states in RAFT?

Follower is the default state of nodes. Candidate is a state when a node is trying to become a leader. Leader is the node that has been elected to coordinate the system and maintain consensus among nodes.

What is consensus in distributed systems?

Consensus is the process by which nodes in a distributed system agree on a single value or decision, ensuring all nodes maintain a consistent state despite failures or inconsistencies.

What are the Safety and Liveness properties in consensus algorithms?

Safety ensures that nodes do not agree on conflicting values (bad things do not happen), while Liveness ensures that the system eventually reaches a decision (good things happen).

How does RAFT handle data replication?

RAFT replicates data by maintaining a log of operations on each node. The leader adds entries to its log, sends them to followers via AppendEntries messages, waits for majority acknowledgments, and then commits the entries to ensure all nodes have the same consistent state.

What happens if the leader or a follower fails in RAFT?

If the leader fails, a new election is triggered, and the new leader continues replication from the highest log index to prevent data loss. If a follower fails, the leader continues sending log entries until the follower acknowledges them.

Can consensus and leader election be avoided in distributed systems?

Yes, in some cases, coordination-free algorithms can be used, as highlighted by the CALM theorem. Additionally, simple leader actions can sometimes be managed using leases or locks with TTL and atomic operations.

Why is understanding leader election and consensus important even if existing systems handle them?

Understanding these concepts is important because they underpin the reliability and robustness of distributed systems that run daily operations, including database replication and coordination in apps like photo sharing platforms.

How does leader election and consensus make distributed applications more robust and scalable?

By having a leader coordinate writes and using consensus to replicate data, distributed applications ensure consistent state across nodes, tolerate failures of some nodes, and allow read scaling from replica nodes.