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As an open source memory data storage system, Redis is deeply loved by the industry because of its fast and efficient features. However, its performance problems when storing and accessing large amounts of data have also attracted widespread attention. In this case, how to optimize the performance of Redis has become an important issue faced by users.

This article will analyze the performance bottleneck of Redis in detail, summarize the currently available Redis performance optimization solutions, and give optimization cases in combination with actual usage scenarios, hoping to be useful to you.

Analysis process of Redis performance bottleneck

Redis performance bottleneck refers to: the inefficiency of Redis when processing data, or the slowdown of service response time.

This situation may be caused by the bottleneck of Redis itself or the external environment.

What are the factors that determine performance bottlenecks?

CPU: CPU is the most important resource of Redis, and the execution speed of Redis is directly affected by CPU.
Network: Redis is usually used as a memory cache, and the delay is very small compared to the hard disk. However, in the case of a complex network environment, Redis' network communication may also become a performance bottleneck.
Memory: If the machine where Redis is located does not have enough memory for Redis, or if Redis uses too much memory and there are a lot of memory leaks, it will become a bottleneck.
Storage method: The bottleneck of Redis may come from improper use of internal data structures, such as using too many keys to cause too much memory usage.
Redis configuration: Redis configuration parameters have a great impact on operating efficiency, and the settings need to be optimized according to the actual situation.
Large data set processing: The ability and efficiency of Redis to process large data sets is a problem, and the balance between processing performance and memory usage.
Redis's own limitations: Redis has various limitations such as physical I/O, network I/O, host I/O, etc. within the framework.

What are the performance bottleneck diagnosis and troubleshooting tools?

info command: You can use the info command of Redis to view the running status of Redis, including CPU, memory and other performance parameters.
top command: Check the operating system level tasks, such as the memory usage of the Redis process, etc.
redis-benchmark command: Based on the redis-benchmark tool, you can simulate a redis official benchmark test.
redis-cli monitor: for real-time checking of database installation monitors.
slowlog command: Users can find out which queries take longer to execute according to the slow query log, and they require more resources, copy the slow query to the program log or directly use the GUI for analysis.
Dstat tool: Used to mark higher-level issues such as sources of redis process resource usage and I/O capacity in any Linux environment, and regularly record Redis runtime status.

What are the Redis performance optimization solutions?

Basic Performance Optimization Solution

Set the appropriate memory size: Configure the memory size according to actual needs, and it is recommended to reserve about 30% of the memory for the operating system to prevent Redis from using too much memory to affect system performance or accidents.
Enable data persistence: Enable Redis data persistence to avoid data loss due to unexpected reasons.
Configure the appropriate number of concurrency: According to the actual load, set the number of concurrent sockets that Redis listens to.
Avoid using the SELECT command: It is better to have a separate Redis instance for each application and not use Redis' SELECT command to switch databases.
Avoid heavy command pipelines: they are memory intensive, use low-level APIs like emit, which interact more directly with sockets.
Disable command debugging: Redis prioritizes response time.

Data structure and key design optimization

Use Redis wildcards and scan functions as little as possible: HVALS and SCAN traverse the entire database to find all key values, have poor performance, and weaken network security capabilities.
Clean up data: When the data is expired or unnecessary, the performance of Redis operation can be guaranteed by regularly cleaning up expired or redundant data.
Lock or queue: need to use its data structure reasonably.

Redis tuning experience and introduction of related tools

Use of redis-cli: Use redis-cli to easily perform data operations, monitor cache hit rate, and simulate test data.
Use of the slow log command: the slow query log can output all Redis commands whose duration is longer than the specified milliseconds, which is convenient for locating performance problems.
Use of the redis-benchmark command: This command is used for performance and stress testing, and is generally used for Cluster testing.

It should be emphasized that the core method of Redis performance optimization lies in the adoption of flexible strategies and the combined application of a series of solutions. Whether it is a basic optimization scheme or a more complex high-availability cluster scheme, it is necessary to apply these methods and techniques with an in-depth understanding of the principles of Redis.

Redis actual combat optimization case sharing

Common Scenario Redis Performance Problem Case Analysis

Cache avalanche problem: When a large number of keys in the cache expire at the same time, or Redis directly crashes, a large number of query requests arrive at the database, resulting in a sudden increase in the query pressure of the database, or even a direct hang up.
Cache penetration problem: Refers to the query of data that does not exist in the cache or the database. As a result, every time the data is queried, it will go through the cache, directly check the database, and finally return empty. When a user frantically initiates a query request using this piece of non-existent data, the pressure on the database is very high, and it may even hang up directly.
Cache breakdown problem: When a hot data in the cache expires, a large number of query requests pass through the cache and directly query the database before the hot data is reloaded into the cache. This situation will cause the pressure on the database to increase suddenly, causing a large number of requests to be blocked, or even hang up directly.

Optimization scheme for the above problems

For the cache avalanche problem: for the former, it is only necessary to disperse the expiration time of each key so that their failure points are distributed as evenly as possible; for the latter, Redis provides a solution for high availability architecture, for example: use Redis Cluster to Cache nodes are deployed horizontally.
For the problem of cache penetration: There are generally two methods, the first is to cache empty objects, and the second is to use Bloom filters (if the query volume is large, the second is recommended).
For the cache breakdown problem: there are two optimization methods, the first is to set the key to never expire; the second is to use distributed locks to ensure that only one query request reloads hot data into the cache at the same time, so that, Other threads only need to wait for the thread to finish running before getting data from Redis again.

at last

When optimizing Redis performance, many factors need to be considered, such as the optimization of Redis Cache Theme architecture, the configuration optimization of Redis applications, the optimization of cluster Redis disaster recovery and load balancing solutions, and the influence of client development code factors, etc. Through comprehensive consideration of the above factors, the memory usage efficiency of Redis and the throughput of Redis operations can be comprehensively improved through multi-directional optimization, making it suitable for more scenarios.

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[Summary] In-depth analysis of Redis performance problems and optimization solutions