How to optimize Redis memory usage?

How to optimize Redis memory usage: Choose the right data structure, such as sorted set is better than list. Optimize key design and use simple and short keys. When the data volume is too large, consider using appropriate serialization methods, compressing data, setting out expiration strategies or sub-banking. Use code to check Redis memory usage, such as the info command. Choose the appropriate optimization strategy according to the specific situation.

How to optimize Redis memory usage? This question is well asked, and all budget-conscious programmers must pay attention to this issue. Although Redis is fast, it is used up with memory, and no matter how fast it is, it is useless. In this article, let’s talk about how to make Redis more memory-saving, and share some of the pitfalls I’ve stepped on over the years.

The main reasons for using Redis memory are just a few: the data structure is selected wrongly, the key design is unreasonable, and the data itself is too large.

Let’s talk about the data structure first. Redis provides a variety of data structures, each with its own advantages and disadvantages, and the memory usage is far inferior. For example, if you use list to store a large amount of data, the memory usage will be much higher than using sorted set. Why? Because list is a linear structure, memory is continuously allocated, and sorted set is implemented by jumping tables, memory allocation is more flexible and space utilization is higher. Therefore, when choosing a data structure, you must follow the actual situation, not to save trouble, and use string or list all your brains. Remember, if you use the right structure, the memory saved will allow you to drink a few less cups of coffee.

Let’s take a look at the key’s design. Poor key design will cause Redis memory usage to expand dramatically. For example, if you use too long keys, or if the key contains too much useless information, it will increase the memory burden. I've made this mistake before, the key was designed in a mess, but the Redis memory usage increased several times, almost crashing my server. Therefore, the key design should be concise and clear, and try to be as short as possible. If you can use numbers, don’t use strings. If you can use short strings, don’t use long strings. Don't forget that the key itself also takes up memory.

The data itself is too large, which is also a big problem. If your data volume is huge and cannot be compressed, the memory usage will naturally be high. At this time, you can consider some optimization strategies, such as:

• Use a suitable serialization method: Although JSON is easy to use, the serialized data volume is usually relatively large. You can try using a more compact serialization method, such as protobuf or MessagePack. In this regard, I personally prefer protobuf, which is highly efficient and small in size.
• Compressed data: Redis itself does not support compression, but we can use external tools to compress the data and then store it in Redis. Of course, this adds some extra computational overhead and requires trade-offs.
• Use the appropriate expiration strategy: set the expiration time of the data, and clear the no longer needed data in time and free up memory space. This trick is simple and effective, highly recommended.
• Sub-store and table: If the amount of data is too large, you can consider sub-store and tables, scatter the data on multiple Redis instances, and reduce the memory pressure of a single instance. It's like breaking a large warehouse into several small warehouses, which is more convenient and safer to manage.

Finally, I will share a piece of code I often use to check Redis memory usage:

 <code class="python">import redis r = redis.Redis(host='localhost', port=6379, db=0) info = r.info() used_memory = info['used_memory'] used_memory_rss = info['used_memory_rss'] print(f"Redis used memory: {used_memory} bytes") print(f"Redis used memory (RSS): {used_memory_rss} bytes") # 可以根据实际情况添加更复杂的内存监控和报警机制</code>

Remember, there is no one-time solution to optimize Redis memory usage. You need to choose the appropriate strategy based on the actual situation. Only by practicing and summarizing more can you become an expert in Redis memory optimization. Don’t forget that the code should be written elegantly and the annotations should be written clearly for future maintenance. This is not only responsible for oneself, but also for the team.

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