Table of contents
This article mainly introduces the definition, principle, and application scenarios of consistent hashing.
1. Consistent hashing definition
Consistent Hashing is a special hashing technology mainly used to solve data distribution problems in distributed systems.
This feature makes consistent hashing widely used in distributed systems, such as load balancing, data sharding and other scenarios.
Its main feature is that when the nodes participating in the calculation change, the hash distribution results that have been prepared will be affected as little as possible.
2. Working principle
Consistent hashing works as follows:
(1) All possible hash values form a ring (this ring is called a hash ring).
(2) For each data item, calculate its hash value and place it at the corresponding position on the hash ring.
(3) For each node, its hash value is also calculated and placed at the corresponding position on the hash ring.
(4) For each data item, search clockwise starting from its position and assign it to the first node found.
In this way, when new nodes join or original nodes leave, only a small part of the data items need to be reallocated, which greatly reduces the cost of data migration.
3. Application scenarios
Consistent hashing is mainly used in the following aspects:
- Load balancing: Consistent hashing can evenly distribute requests to various servers. When the number of servers changes, only a small number of requests need to be redistributed, and the majority of request processing servers remain unchanged, which can reduce server load fluctuations.
- Distributed cache: In a distributed cache system, consistent hashing can be used to determine which cache node each data should be stored on. When cache nodes are added or reduced, only a small amount of data needs to be moved, and most of the data can be kept on the original cache nodes, which can reduce the cost of network transmission and cache invalidation.
- Data sharding: In a distributed database, consistent hashing can be used to shard data to evenly distribute data to each database node. When the database nodes are increased or decreased, only a small amount of data needs to be migrated, and most of the data can be kept on the original database nodes, which can reduce the cost of data migration.
- Distributed storage: In a distributed file system or object storage system, consistent hashing can be used to determine which storage node each file or object should be stored on. When storage nodes are increased or decreased, only a small number of files or objects need to be moved, and most files or objects can remain on the original storage nodes, which can reduce the cost of data migration.
4. Software that uses consistent hashing
Here are some open source software that use consistent hashing:
- Memcached: This is a widely used open source distributed memory object caching system that uses consistent hashing to decide which cache node to store data on.
- Cassandra: This is an open source distributed NoSQL database that uses consistent hashing for data sharding to evenly distribute data to different database nodes.
- Riak: This is an open source distributed key-value storage system that uses consistent hashing to decide which storage node to store data on.
- DynamoDB: Although not open source software, Amazon's DynamoDB is also a typical example of using consistent hashing. It uses consistent hashing for data sharding and replication.
- Akka: This is an open source toolkit for building concurrent and distributed systems. Its cluster module uses consistent hashing for load balancing and failure recovery.
- Voldemort: This is a distributed key-value storage system open sourced by LinkedIn, which uses consistent hashing for data sharding and replication.
- Open-falcon: Open-Falcon is an open source, enterprise-level, high-performance monitoring solution. It is designed with full consideration of the real-time nature, data consistency and scalability of the monitoring system. In Open-Falcon's Transfer component, a consistent hashing algorithm is used.
For the application of consistent hashing in open-falcon, here is a brief expansion.
Open-Falcon's Transfer component is responsible for receiving the data reported by the Agent and distributing the data to the back-end Graph component for storage.
In this process, the Transfer component uses a consistent hash algorithm to evenly distribute data to various Graph nodes based on the name and label of the metric.
In this way, even if the number of Graph nodes changes, only a small amount of data needs to be redistributed, and the storage location of most data can remain unchanged, thus ensuring system stability and data consistency.
The consistent hashing library used by Open-Falcon in the Go language implementation is github.com/toolkits/consistent. This library provides basic functions of consistent hashing, including operations such as adding nodes, deleting nodes, and finding nodes. At the same time, it also supports virtual nodes, which can improve the uniformity of data distribution to a certain extent.
5. Open source implementation of consistent hashing
Taking the Go language as an example, here are some open source consistent hash libraries for the Go language:
github.com/stathat/consistent: This is a simple and easy-to-use consistent hashing library that provides basic consistent hashing functions.github.com/serialx/hashring: This library provides support for consistent hashing and virtual nodes, which can be used to build distributed systems.github.com/schallert/consistent: This library provides support for consistent hashing and virtual nodes, and also provides some additional features such as node weights, etc.github.com/toolkits/consistent: This is the consistent hashing library used in the Open-Falcon project, which provides support for consistent hashing and virtual nodes.
The above libraries all provide basic functions of consistent hashing, including operations such as adding nodes, deleting nodes, and finding nodes. At the same time, they also support virtual nodes, which can improve the uniformity of data distribution to a certain extent.
6. The shortcomings of consistent hashing
Although consistent hashing has many advantages, it also has some shortcomings:
- Uneven data distribution: Ideally, consistent hashing can distribute data evenly across nodes. However, in practical applications, due to the characteristics of hash functions, uneven data distribution may occur, that is, there is too much data on some nodes and too little data on some nodes.
- Virtual node management is complex: In order to solve the problem of uneven data distribution, consistent hashing introduces the concept of virtual nodes, and each real node corresponds to multiple virtual nodes. Although this method can improve the uniformity of data distribution to a certain extent, it also increases the complexity of the system and requires more computing and management costs.
- Complete load balancing cannot be guaranteed: Although consistent hashing can keep the location of most data unchanged when nodes are added or removed, this does not guarantee that the load of all nodes is completely balanced. If the load of the system changes, you may need to manually adjust the distribution of data to achieve load balancing.
- Replication and fault-tolerance issues: Consistent hashing itself does not contain a data replication and fault-tolerance mechanism, and requires additional strategies to achieve it. For example, technologies such as chain replication or vector clocks can be used to achieve data replication and consistency maintenance.
Above, this article focuses on consistent hashing and mainly introduces its principles and application scenarios.