Ep #16: Cracking Consistent Hashing: How It Works and Where It's Used
Techniques to evenly distribute data and traffic across nodes.
Ep #16: Breaking the complex System Design Components
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In Part 1, we explored the pitfalls of traditional hashing and why consistent hashing is crucial for distributed systems. Now, let’s dive deeper into how consistent hashing works, and how systems like Cassandra, Memcached, and CDNs apply it to achieve load balancing and scalability.
How Does Consistent Hashing Work?
Let’s dive into the mechanics of consistent hashing with a simple analogy and a step-by-step explanation.
The Hash Ring Analogy
Imagine a circular ring, like a clock face, where the circumference represents a range of numbers (e.g., 0 to 2³²–1). This is called the hash ring. Both data (or requests) and nodes are placed on this ring based on their hash values. The key idea is to map data to the nearest node on the ring in a consistent way.
Here’s how it works:
Hashing Nodes and Data:
Each node in the system (e.g., a server) is assigned a position on the hash ring based on its hash value. A hash function (e.g., MD5 or SHA-1) takes the node’s identifier (e.g., its IP address) and produces a number that places it on the ring.
Similarly, each piece of data (or request) is hashed using the same hash function, which places it on the ring.
Assigning Data to Nodes:
To assign a piece of data to a node, you find its position on the hash ring and move clockwise (or counterclockwise, depending on the implementation) until you encounter the first node. That node is responsible for the data.
Handling Node Additions and Removals:
When a new node is added, it’s hashed and placed on the ring. Only the data that falls between the new node and the next node in the clockwise direction needs to be reassigned.
When a node is removed, the data it was responsible for is reassigned to the next node in the clockwise direction.
This approach ensures that only a small fraction of data is affected when the system changes, unlike traditional hashing, where most data would need to be remapped.
A Step-by-Step Example
Let’s walk through a practical example:
Initialize the Hash Ring:
Suppose you have three nodes: Node A, Node B, and Node C.
Using a hash function, their positions on the ring are calculated as:
Node A: hash("Node A") = 100
Node B: hash("Node B") = 200
Node C: hash("Node C") = 300
These nodes are placed on the ring at positions 100, 200, and 300.
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