Optimization practice of caching technology in distributed system applications in Golang.

As business scale becomes larger and concurrency becomes higher, performance and reliability issues in distributed system applications have attracted more and more attention. Among them, caching technology is one of the important means to solve performance bottlenecks. As an efficient language, Go language is increasingly used in distributed systems. This article will introduce the application optimization practice of caching technology in distributed systems in Golang.

1. Overview of caching technology in Golang

Cache technology is a common technology to improve performance in distributed systems. There are mainly the following implementation methods:

1. Local cache

Local cache refers to the application storing a copy of data in local memory to improve data access speed and reduce access to back-end systems. In Go language, map is used as a local cache to store and query data conveniently and quickly.

2. Distributed cache

Distributed cache is to reduce the load of a single node by building a cache cluster on multiple machines and storing data in a distributed manner according to certain rules. pressure and improve the concurrent processing capabilities of the entire system. In Go language, commonly used distributed caching technologies include Redis, Memcached, etc.

3. Responsive caching

Responsive caching is a technology that uses high-speed memory cache to store the most commonly used query results to speed up query response time. When a query request arrives, the system first queries the cache. If there is data in the cache, it returns directly. Otherwise, it queries the database and stores the query results in the cache. In Go language, you can use some open source caching middleware, such as caffeine, etc.

2. Optimization practice of caching technology in distributed systems

1. Distributed cache optimization

In distributed cache, in order to ensure the security of data For consistency and reliability, consistent hashing algorithms are generally used to achieve sharded storage of data. When using the consistent hash algorithm, the following issues need to be considered:

①Dynamic changes of nodes

When the number of cache nodes changes, the consistent hash ring needs to be rebuilt and Adjust to ensure that data can be evenly distributed on various nodes.

②Load Balancing

In order to ensure the load balance of each node, the data needs to be distributed as evenly as possible to each node in the consistent hash ring to avoid performance caused by uneven load. question.

③Node failure

When a cache node fails or goes down, the data of that node needs to be transferred to other nodes in a timely manner to avoid data loss or unavailability.

Therefore, when using distributed cache, you need to consider the above issues and select an appropriate caching strategy based on the actual business scenario.

2. Local cache optimization

When using local cache, you need to consider the following issues:

①Cache invalidation

When caching data When it is not accessed within a certain period of time, it needs to be removed from the local cache to avoid occupying too much memory space.

②Memory overflow

When using the local cache, you need to set an appropriate memory threshold. When the amount of data in the local cache exceeds the threshold, you need to take appropriate measures, such as clearing or eliminating it. The oldest data.

③Concurrency control

When multiple coroutines access the local cache at the same time, concurrency issues need to be considered to avoid resource competition and deadlock.

Therefore, when using local caching, you need to consider the above issues and select an appropriate caching strategy based on the actual business scenario.

3. Responsive cache optimization

When using responsive cache, you need to consider the following issues:

①Cache update

When When the data in the database changes, the data in the cache needs to be updated in a timely manner to ensure data consistency.

②Cache Invalidation

Like the local cache, the data in the responsive cache also needs to set an appropriate expiration time to avoid taking up too much memory space.

③Concurrency control

When multiple coroutines access the cache at the same time, concurrency issues need to be considered to avoid resource competition and deadlock.

Therefore, when using responsive caching, you need to consider the above issues and select an appropriate caching strategy based on the actual business scenario.

3. Summary

Caching technology is an important means to improve performance in distributed systems, and in the Golang language, there are many mature caching middleware to choose from. When applying caching technology, you need to select an appropriate caching strategy based on actual business scenarios, and optimize and maintain the cache to ensure data consistency and reliability.

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