Redis service governance and service grid in cloud native applications

Redis, as an open source in-memory key-value database system, has become an indispensable part of modern cloud-native applications. In the architectural design of cloud native applications, service governance and service grid are very important components. This article will discuss the service governance and service grid of Redis in cloud native applications, and explore the application scenarios and advantages of Redis in these aspects.

1. How does Redis support service governance?

In cloud native applications, service governance can help us manage and monitor the running status and status of service instances. Redis can support service governance by providing multiple functions such as distributed locks, publish/subscribe, and queues. Let’s take a closer look below.

1.1. Distributed lock

In distributed systems, distributed locks are a very common technology that can coordinate concurrent access between different services. Redis provides a lightweight distributed lock mechanism to ensure that access to a resource is mutually exclusive under concurrent conditions.

The implementation principle of distributed locks mainly relies on the setnx instruction of Redis (ie set if not exists), which can ensure that only when the Key does not exist in Redis, data can be written and 1 will be returned, otherwise it will be returned 0.

1.2. Publish/Subscribe

In a distributed system, real-time message delivery is very important. Redis provides a publish/subscribe model, which allows real-time message transmission between different services to achieve inter-service communication. Redis' publish/subscribe function can be used when implementing distributed transactions, publish/subscribe, and broadcast functions.

1.3. Queue

In cloud native applications, the queue is a very important part, which allows messages to be transferred between different services. Redis provides a variety of queue implementation methods, such as list, Sorted set, etc. By using the queue function of Redis, functions such as asynchronous task processing, delayed tasks, and flow control can be realized.

2. How does Redis support service grid?

Service mesh is a solution for managing the interaction between different services in cloud native applications. Redis can support service mesh by providing functions such as distributed data structures and pipelines. Let’s take a closer look below.

2.1. Distributed data structure

In the service grid, communication between services is very frequent and various types of data need to be transferred. Redis provides a variety of distributed data structures, such as hash tables, linked lists, sets, and Sorted sets, etc. These data structures can be shared between different services.

By using the distributed data structure of Redis, data can be shared between different services, thereby achieving data sharing and inter-service communication. For example, when managing user status, you can use Redis's hash table structure to record the user's login status, account information, permissions, etc.

2.2. Pipeline

In the service grid, the pipeline is a very important part. It can establish streaming processing pipelines between different services to realize data transfer and processing. Redis can realize data transfer and processing between multiple services by providing pipeline functions.

By using the pipeline function of Redis, a variety of scenarios can be realized, such as message queue, event-driven, and data processing functions. When processing anti-crawler verification codes, you can use Redis's pipeline function to coordinate and manage data transfer and processing between multiple services.

3. Summary

In cloud native applications, Redis, as an in-memory database system, can provide functions such as distributed locks, publish/subscribe, queues, distributed data structures, and pipelines. , to support service governance and service grids. By using Redis to coordinate communication and processing between services, highly available and highly scalable cloud-native applications can be achieved.

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