Practical application of Redis under large-scale concurrency

Practical application of Redis under large-scale concurrency

With the continuous development of Internet technology, there are more and more large-scale concurrency application scenarios. In these application scenarios, caching technology is an indispensable part. As a high-performance open source caching system, Redis is used by more and more enterprises.

However, Redis will also face some challenges when facing large-scale concurrency. This article will introduce some practical experience of Redis application under large-scale concurrency, hoping to provide some useful reference for readers.

1. Configuration Optimization

The default configuration of Redis is not necessarily suitable for all application scenarios, so some configuration optimization is required in actual use. The following points require special attention:

• Selection of the maxmemory-policy option: This option is used to specify what policy should be used to clean the cache when the memory exceeds the limit. Different application scenarios may require the use of different strategies, such as least recently used (LRU), least visited (LFU), random (random), etc. Need to be adjusted according to actual situation.
• Setting of TCP parameters: In high concurrency situations, TCP parameters also need to be adjusted to better support concurrent connections. Parameters that require special attention include syncookies, tcp_tw_recycle, tcp_tw_reuse, etc.
• Redis persistence: In Redis, data can be persisted through RDB (snapshot) or AOF (append). It is necessary to choose the appropriate method according to the actual situation and configure it accordingly.

2. Master-slave replication

In high concurrency scenarios, the performance of a single Redis instance may not meet the needs. At this time, you can consider using master-slave replication to distribute the load to multiple instances and implement failover. The following is some practical experience in master-slave replication:

• Time errors between different Redis instances may cause delays in data synchronization. You need to configure the NTP server to ensure time consistency between different instances.
• Master-slave replication also needs to consider network bandwidth, replication delay and other factors. It is recommended to conduct sufficient testing in the actual production environment and adjust parameters such as the replication interval according to the actual situation.
• When the main Redis goes down, you need to quickly switch from Redis to the main Redis. In actual implementation, tools such as Redis Sentinel can be used to achieve automatic switching and failure recovery.

3. Selection of data structure

Redis supports a variety of different data structures, and different data structures have different advantages and disadvantages. When using Redis for caching, you need to select an appropriate data structure based on actual needs and perform corresponding optimization.

• String (string): suitable for storing smaller data and short-term cache.
• List (list): suitable for storing some larger data collections, such as queues, etc.
• Set: Suitable for storing non-duplicate data sets, supporting fast intersection, union and other operations.
• Ordered set (sorted set): Similar to a set, but you can specify a score for each element and support operations such as sorting by score.
• Hash table (hash): suitable for storing some structured data, such as a large amount of key-value data.

4. Current limiting strategy

In high concurrency scenarios, a large number of requests accessing the cache system at the same time may cause system crashes or performance degradation. Therefore, some current limiting strategies need to be adopted to curb request concurrency.

The following are some commonly used current limiting strategies:

• Speed ​​limiting: Adopt speed limiting strategies at the cache level, for example, by setting request frequency, traffic limit, etc.
• Distributed current limiting: Use gateways or scheduling systems to implement current limiting among multiple Redis nodes, effectively reducing the pressure on the cache system.
• Asynchronous processing: In scenarios where requests are slow, you can use an asynchronous processing solution, put the request in the queue, and process the request asynchronously to improve concurrency.

Summary

The actual application of Redis in large-scale concurrency scenarios needs to consider many factors, including configuration optimization, master-slave replication, data structure selection and current limiting strategy, etc. . It is necessary to select an appropriate solution based on the actual situation and conduct sufficient testing and optimization. I hope this article can provide readers with some useful practical experience and reference.

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