How to implement high-performance distributed shared cache in Go language development?

How to implement high-performance distributed shared cache in Go language development

Introduction:
In modern software systems, caching is one of the important means to improve system performance. As the system scale continues to expand and the load continues to increase, single-machine cache no longer meets the needs, and distributed shared cache has become a widely adopted solution. This article will introduce how to implement high-performance distributed shared cache in Go language development.

1. Choose a suitable cache storage engine
   There are many choices for distributed cache storage engines, such as Redis, Memcached, etc. Different engines are suitable for different scenarios and needs.
   In Go language development, Redis is a good choice. It has the characteristics of high performance, persistence, replication, scalability, etc., and provides rich data structures and operation commands to facilitate developers to use.
2. Use connection pool
   In order to improve performance and reduce the cost of establishing and releasing connections, you can use a connection pool to manage the connection to Redis. The sync.Pool package in the Go language provides the implementation of connection pooling, which is easy to use and has obvious effects.
3. Sharding based on consistent hashing algorithm
   Distributed cache needs to solve the problem of data distribution. The consistent hash algorithm is one of the commonly used sharding algorithms. By mapping data to a bit ring, the data is evenly distributed and the amount of data migration is minimized when nodes are added or deleted.
   In the Go language, you can use the third-party library go-hashring to implement consistent hashing algorithm sharding. In this way, each node can be responsible for a part of the data, improving concurrency capabilities.
4. Use cache penetration technology
   Cache penetration means that a certain key does not exist in the cache, and the value corresponding to this key does not exist in the database. In order to avoid this situation, you can make a simple judgment before obtaining the data. If it does not exist in the cache, you can return a default value to avoid frequent queries to the database. This method is called cache penetration technology.
5. Cache update strategy
   In order to ensure data consistency, when the data changes, the cache needs to be updated in a timely manner. There are two commonly used cache update strategies: active update and passive update.
   Active update means that after the data changes, the program is responsible for updating the cache, which can be achieved by subscribing to the database or message queue. Passive update means that when obtaining data, first obtain it from the cache. If it does not exist in the cache, then obtain it from the database and write the data to the cache. The next time it is obtained, it will be obtained directly from the cache.
6. Cache avalanche processing
   Cache avalanche refers to the failure of most or all of the cache, causing requests to fall directly on the database, causing the database load to be too high or even crash. In order to avoid cache avalanche, you can take the following measures:
7. Set the cache validity period to be random to avoid a large number of caches from invalidating at the same time.
8. Use a multi-level cache architecture to place requests on different cache nodes to distribute the load.
9. The circuit breaker mechanism, when the cache fails, temporarily routes the request to the backup node to ensure the stability of the system.
10. Monitoring and Alarming
    In order to detect and solve problems in a timely manner, the performance and usage of the cache need to be monitored. You can use monitoring tools such as Prometheus to perform performance indicator statistics and alarms.

Conclusion:
The Go language performs well in the development of distributed shared caches and has the characteristics of high performance and high concurrency. By selecting a suitable cache storage engine and using methods such as connection pooling, consistent hash sharding, cache penetration technology, cache update strategies, and cache avalanche processing, you can achieve high-performance distributed shared cache and improve system performance and stability.

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