Consistent Hashing | Algorithms You Should Know #1

Summary

This video explains consistent hashing, a key technique used in distributed systems like DynamoDB, Apache Cassandra, and Akamai CDN. Consistent hashing ensures that data is evenly distributed across servers, even when servers are added or removed. The video highlights the limitations of simple hashing and how consistent hashing reduces data movement by reassigning only a fraction of keys when the system changes. Virtual nodes are introduced to ensure balanced data distribution. The video also discusses real-world applications in databases, content delivery networks, and load balancers.

Takeaways

• 🔄 DynamoDB, Apache Cassandra, Discord, and Akamai CDN all use consistent hashing for data distribution.
• 🌐 Consistent hashing is key for distributing data evenly across multiple servers in a large-scale distributed system.
• 💻 In simple hashing, adding or removing servers causes massive remapping of data, leading to inefficiency.
• 🔢 Consistent hashing reduces the need for redistributing objects when servers are added or removed, minimizing data movement.
• 🌀 Consistent hashing uses a hash ring, where both object keys and servers are hashed into a circular hash space.
• ⏩ To locate a server for an object in consistent hashing, the system moves clockwise on the hash ring from the object’s hash.
• 🔑 Adding a new server only impacts the closest objects, reducing overall data remapping compared to simple hashing.
• 🧮 Virtual nodes improve data balance in consistent hashing by having servers appear multiple times on the hash ring.
• 📈 More virtual nodes lead to better load distribution, but this comes at the cost of higher metadata storage.
• 🌍 Consistent hashing is widely used in systems like DynamoDB, Apache Cassandra, Akamai CDN, and Google Load Balancer to improve performance and efficiency.

Q & A

What is consistent hashing and why is it widely used?

Consistent hashing is a technique used to distribute data across servers in such a way that minimizes data movement when servers are added or removed. It is widely used because it ensures that most data remains assigned to the same server even when the number of servers changes, which makes scaling more predictable and efficient.

How does consistent hashing differ from simple hashing in distributed systems?

In simple hashing, data is assigned to servers by applying a hash function followed by a modulo operation with the number of servers. When the number of servers changes, most of the keys need to be reassigned, causing significant disruption. In consistent hashing, both data and servers are mapped to a hash ring, and data is assigned by finding the next server in a clockwise direction, which minimizes the number of reassignments.

What is a hash ring and how does it function in consistent hashing?

A hash ring is a circular representation of the hash space in which both servers and data are mapped using a hashing function. Each data item is assigned to the nearest server going clockwise from its position on the ring, making data distribution resilient to changes in server availability.

What problem does consistent hashing solve in the context of server additions or removals?

Consistent hashing solves the issue of massive data redistribution when servers are added or removed. Instead of remapping all keys, only a small fraction of keys need to be reassigned, reducing the impact on the system and maintaining performance stability.

How are virtual nodes used in consistent hashing?

Virtual nodes are used to ensure more balanced data distribution by allowing each physical server to appear multiple times on the hash ring. This spreads data more evenly across servers, even if the servers are not evenly distributed in the hash space.

Why is data distribution often uneven in consistent hashing, and how do virtual nodes help address this?

Data distribution can be uneven if servers are mapped unevenly on the hash ring, leading to some servers handling much more data than others. Virtual nodes help by replicating each server multiple times on the ring, which helps ensure that data is more evenly distributed among all servers.

What is the trade-off involved in using virtual nodes in consistent hashing?

The trade-off of using virtual nodes is that they require more space to store metadata about each virtual node, increasing the overhead. The number of virtual nodes can be tuned to achieve a balance between more even data distribution and the additional storage overhead.

How does consistent hashing help content delivery networks like Akamai?

Consistent hashing helps content delivery networks like Akamai distribute web content evenly among edge servers, which reduces the amount of data that needs to be moved or replicated when servers are added or removed, ensuring efficient content delivery.

What is horizontal scaling in distributed systems?

Horizontal scaling refers to adding more machines (servers) to handle increased data or workload. In distributed systems, data is spread across multiple machines to ensure scalability and reliability.

What are some real-world applications of consistent hashing?

Consistent hashing is used in NoSQL databases like Amazon DynamoDB and Apache Cassandra for data partitioning, in content delivery networks like Akamai for efficient content distribution, and in load balancers like Google Load Balancer to evenly distribute connections across backend servers.