How to troubleshoot Redis memory issues?

Troubleshooting Redis memory problems: Analyze Redis memory structure and understand the memory usage differences between different data structures. Use the redis-cli INFO memory command to monitor memory usage. Use the MEMORY STATS command to locate the problem data type. Pay attention to the used_memory_peak and used_memory_rss indicators to determine whether there is memory peak or fragmentation. Consider using memory phasing strategies or restarting Redis to resolve memory fragmentation. Check the persistence mechanism to avoid excessive space occupied by AOF or RDB files. Analyze the code for memory leaks and release no longer needed resources in a timely manner.

How to troubleshoot Redis memory issues? I have seen too many developers scratch their heads about this problem. After all, Redis memory problems are like detectives solving cases, which require careful observation and analysis, rather than brute force. After reading this article, you can not only master the investigation methods, but also understand the principles behind it, so as to avoid falling into the same pit in the future.

Let’s talk about the core first: Redis memory problem, which ultimately comes from running out of memory. But there are many ways to "use it out", and this is the key. We have to find the real murderer like Sherlock Holmes.

First, you need to understand the memory composition of Redis. It is not simply stuffing data into it. Redis uses multiple data structures to store data, and the memory usage of each structure varies. For example, strings are simple, while hash tables, collections, and ordered collections are much more complex. Memory usage also depends on the size of the data itself. A huge string, obviously more memory-intensive than a bunch of small strings. Only by understanding this can you be targeted.

Then, let's look at the tools. redis-cli is your good helper, which provides a range of commands to monitor memory usage. The INFO memory command can give you a comprehensive memory usage report, including used memory, fragmentation degree, etc. If you observe the changes in these indicators carefully, you will find out the problem. For example, the used_memory_rss metric reflects the system memory actually occupied by Redis, while used_memory metric reflects the memory used internally by Redis. The gap between these two indicators reflects the degree of memory fragmentation. The serious fragmentation indicates that Redis's memory utilization rate is not high and needs optimization.

To go a little further, the MEMORY STATS command can provide more detailed memory statistics, such as the memory usage of each data structure. This can help you locate the problem data type. If you find that a data structure has an abnormal memory usage, you must carefully check the relevant data.

Code example? In fact, there is no complicated code, the key is how to interpret the output of redis-cli . For example, if used_memory_peak is found to be much larger than used_memory , it means that there has been a memory peak before, which may be caused by a brief traffic peak or data writing. But that doesn't necessarily mean there is a memory leak.

But if used_memory_rss continues to grow, and used_memory grows relatively small, then you must be wary of memory fragmentation. At this time, you can consider using CONFIG SET maxmemory-policy allkeys-lru or other policies to control memory usage, or restart Redis to defragment memory. Remember, choosing the right memory ed out strategy is crucial, and choosing the wrong one can lead to data loss.

Another common misunderstanding is the ignorance of the impact of persistence mechanisms. AOF and RDB persistence will take up a lot of disk space, which indirectly affects memory usage. If the persistent file is too large, consider adjusting the persistence strategy, such as reducing snapshot frequency or using a smaller AOF file size.

Finally, and the most overlooked: code bugs. Your application code may have memory leaks, and you are constantly writing data to Redis without deleting it in time. This requires you to carefully check the code to make sure the Redis client is used correctly and to release resources that are no longer needed in a timely manner. Using memory analysis tools, such as Valgrind, can help you find the source of memory leaks. Don't forget that writing elegant and efficient code is itself a best practice to avoid memory problems.

In short, troubleshooting Redis memory problems requires combining tools and experience. Don’t panic, take it step by step and analyze it carefully, and you will definitely find the root cause of the problem. Remember, prevention is better than treatment, writing good code, choosing the right configuration, and monitoring regularly is the king.

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