What are some best practices and challenges of implementing consistent hashing in a distributed system?

Consistent hashing is a technique that allows you to distribute data or requests across a dynamic set of nodes, such as servers, caches, or databases, without having to rehash everything when a node joins or leaves. It can improve the performance, scalability, and availability of your distributed system, but it also comes with some challenges and trade-offs. In this article, you will learn some best practices and challenges of implementing consistent hashing in a distributed system.

1 How does consistent hashing work?

Consistent hashing maps each data item or request to a point on a circular hash space, and assigns each node to a range of points on the same space. To find the node responsible for a given item or request, you simply need to find the closest point in the clockwise direction. This way, when a node joins or leaves, only a small fraction of items or requests need to be reassigned to a different node, minimizing the disruption and overhead.

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Vishal Kumar Bhogal

Professional 2 at Capgemini Engineering | Ex Nucleus Software | Ex Genpact | Ex Encyclopedia
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Question is Why we require Consistent Hashing? Answer is simple in a distributed system when ever we are scaling our Database or a caching system it is necessary to pick right node at right time ,because we often scale up and scale down our system. depending upon the traffic. If we do not pick right node we might end up with situation where Data conflicts and Data Starvation is present. Now Consistent hashing helps us to identify that right node by maintaining circular hash map ,It follow same Circular Linked List methodology in cyclic order internally during Scale up and Scale down which means number of nodes removed and number of nodes added . Here for Key calculation we need to write hash function possibly encrypted SHA-1 of Node.

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2 What are some benefits of consistent hashing?

Consistent hashing can offer several advantages to your distributed system, such as improved load balancing, caching, and scalability. Load balancing can be achieved by evenly distributing data or requests across nodes, avoiding hotspots and improving resource utilization. Caching can be improved by assigning cache keys to cache servers, reducing cache misses and increasing the hit ratio. Replication or redundancy can also be used to increase cache availability and resilience. Moreover, consistent hashing allows for easy addition or removal of nodes without reconfiguration or redistribution, as well as adjusting the load distribution with virtual nodes to accommodate heterogeneous nodes.

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3 What are some challenges of consistent hashing?

Consistent hashing is not a silver bullet and poses some challenges and limitations for a distributed system. Consistency is one such issue, as you may need to deal with stale data, conflicts, or failures when nodes join or leave. Additionally, routing can be costly and complex to update and propagate, while network latency, congestion, or partitioning issues could arise when routing data or requests to the nodes. Furthermore, consistent hashing may not guarantee optimal performance; load balancing, caching, or hashing algorithms could be used to improve the performance and fairness of the system.

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Ahmed Mamdouh

Senior/Staff Software Engineer
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Consistency is a crucial issue in consistent hashing when we decide to choose availability over consistency in the CAP theorem, but we can balance between consistency and availability by using a quorum-based approach based on your use case. Data conflicts are also a challenge, especially when using some replication approaches such as the peer-to-peer approach, and we can mitigate such problems with data versioning by using the causality consistency model and implementing some logical clock such as the vector clock to track the causal dependencies between events.

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4 How to implement consistent hashing in your system?

There is no one-size-fits-all solution for implementing consistent hashing in your system, and you may need to customize it according to your specific requirements and constraints. However, some general steps and tips can help you get started. Consider choosing a suitable hashing algorithm that produces uniform and random outputs, such as MD5, SHA-1, or CRC32. You can also define the hash space size and granularity, such as the number of bits or points. Assign each node to one or more points or ranges on the hash space, either statically or dynamically. To maintain a consistent view of the hash space and the node assignments across your system, you can use techniques such as gossip protocols, consistent hashing rings, or distributed hash tables. Finally, route each data item or request to the node that corresponds to the closest point on the hash space in the clockwise direction. Techniques such as replication, redundancy, or fallback can handle node failures or unavailability.

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5 What are some examples of consistent hashing in practice?

Consistent hashing is a popular technique used in many distributed systems and applications, such as web caching, distributed databases, and load balancing. For example, Akamai, Cloudflare, Memcached, Cassandra, DynamoDB, Riak, Nginx, HAProxy, and Maglev all rely on consistent hashing to distribute cache keys or route requests across servers.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are some best practices and challenges of implementing consistent hashing in a distributed system?

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1 How does consistent hashing work?

2 What are some benefits of consistent hashing?

3 What are some challenges of consistent hashing?

4 How to implement consistent hashing in your system?

5 What are some examples of consistent hashing in practice?

6 Here’s what else to consider

Algorithms

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What are some best practices and challenges of implementing consistent hashing in a distributed system?

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1 How does consistent hashing work?

2 What are some benefits of consistent hashing?

3 What are some challenges of consistent hashing?

4 How to implement consistent hashing in your system?

5 What are some examples of consistent hashing in practice?

6 Here’s what else to consider

Algorithms

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