Excerpt
Consistent hashing minimizes data movement when resizing hash tables. This post explains how it works and its advantages, along with applications in scalable server architecture.
Hashing is a core concept in computer science used in many applications like databases, caches, and load balancing. Traditional hashing methods have certain limitations that consistent hashing aims to address. In this post, we will understand what consistent hashing is, how it works, its advantages over traditional hashing, and its relevance in designing server architecture for load balancing.
Introduction
Hashing refers to generating a fixed-size value or hash code from a key or input using a hash function. It provides a simple way to map data to locations in a hash table for quick insertion and lookup.
Traditional hashing schemes work well in static environments but they have drawbacks when dealing with frequently changing data sets and distributed systems. Consistent hashing provides an alternative approach that is more scalable and fault-tolerant.
What is Hashing?
Hashing is the process of converting a key or identifier into a numeric hash code using a hash function. Some properties of hash functions:
- Deterministic - Same input always yields the same hash output.
- Efficient to compute.
- Maps data seemingly randomly and uniformly.
Hashing enables quick indexing and retrieval from hash tables where data is stored based on the hash code.
Traditional Hashing and Its Limitations
In traditional hashing, output range of hash functions is fixed. Data is mapped to slots in a hash table based on the modulo of the hash code.
This works well in static environments but has limitations when the number of slots need to change dynamically:
- Adding or removing slots changes mapping of existing data.
- Causes reshuffling of almost all data on each change.
- Fails to preserve data locality.
To address this, consistent hashing provides an elegant solution.
Introduction to Consistent Hashing
Consistent hashing minimizes churn and data movement when hash table size changes. It does this by using a hash ring rather than fixed slots. Key properties:
- Hash ring acts as a circular space for mapping keys.
- Keys are mapped to locations on the ring via hashing.
- Hash ring is segmented into slices allocated to servers.
- Only immediate neighbors affected on additions/removals.
This provides superior scalability and availability with minimal reorganization of data mappings.
How Does Consistent Hashing Work?
Consistent hashing works as follows:
Keys are hashed to values between 0 to 2^32 using a hash function like MD5.
The output range forms a ring (0 to 2^32).
The ring is partitioned into slices with each slice owned by a server.
Keys are assigned to slices based on closest hash value.
Virtual nodes are used to allocate multiple slots to each physical server.
This smooths out data distribution and also handles server additions/removals gracefully.
Application of Consistent Hashing to Servers
Consistent hashing is commonly used in server-side systems for distributed caching and load balancing. Benefits include:
- Adding or removing servers only affects local keys.
- Evenly distributes load and keys across servers.
- Avoid hotspots and bottlenecks.
- Easily scale horizontally by adding nodes.
- High tolerance to server failures.
It simplifies load balancing and reconfiguration in large-scale distributed systems.
Real-World Usage Examples
Many major internet companies use consistent hashing:
- Amazon’s DynamoDB for key-value storage.
- Google for distributed lookup service Chubby.
- Facebook for photo storage using Haystack.
- CloudFlare for distributed DNS resolution.
Pros and Cons of Consistent Hashing
Pros:
- Even distribution of data.
- Minimal reorganization during changes.
- Decentralized, highly available.
- Easily scalable.
Cons:
- Additional complexity to implement.
- Non-uniform server loads possible.
- Hotspot keys can overload nodes.
Conclusion
Consistent hashing provides a simple yet powerful approach to building resilient distributed systems and scalable server architecture. Its applications in modern internet-scale services highlight the relevance of consistent hashing in today’s cloud-based environments. By understanding consistent hashing algorithms, developers can build highly available and fault-tolerant systems.
