To give you a few examples (all obtained using 64-bit instances):
Testing your use case is trivial. Use the valkey-benchmark
utility to generate random data sets then check the space used with the INFO memory
command.
64-bit systems will use considerably more memory than 32-bit systems to store the same keys, especially if the keys and values are small. This is because pointers take 8 bytes in 64-bit systems. But of course the advantage is that you can have a lot of memory in 64-bit systems, so in order to run large Valkey servers a 64-bit system is more or less required. The alternative is sharding.
In the past, developers experimented with Virtual Memory and other systems in order to allow larger than RAM datasets, but after all we are very happy if we can do one thing well: data served from memory, disk used for storage. So for now there are no plans to create an on disk backend for Valkey. Most of what Valkey is, after all, a direct result of its current design.
If your real problem is not the total RAM needed, but the fact that you need to split your data set into multiple Valkey instances, please read the partitioning page in this documentation for more info.
Yes, a common design pattern involves taking very write-heavy small data in Valkey (and data you need the Valkey data structures to model your problem in an efficient way), and big blobs of data into an SQL or eventually consistent on-disk database. Similarly sometimes Valkey is used in order to take in memory another copy of a subset of the same data stored in the on-disk database. This may look similar to caching, but actually is a more advanced model since normally the Valkey dataset is updated together with the on-disk DB dataset, and not refreshed on cache misses.
A good practice is to consider memory consumption when mapping your logical data model to the physical data model within Valkey. These considerations include using specific data types, key patterns, and normalization.
Beyond data modeling, there is more info in the Memory Optimization page.
Valkey has built-in protections allowing the users to set a max limit on memory usage, using the maxmemory
option in the configuration file to put a limit to the memory Valkey can use. If this limit is reached, Valkey will start to reply with an error to write commands (but will continue to accept read-only commands).
You can also configure Valkey to evict keys when the max memory limit is reached. See the eviction policy docs for more information on this.
Short answer: echo 1 > /proc/sys/vm/overcommit_memory
:)
And now the long one:
The Valkey background saving schema relies on the copy-on-write semantic of the fork
system call in modern operating systems: Valkey forks (creates a child process) that is an exact copy of the parent. The child process dumps the DB on disk and finally exits. In theory the child should use as much memory as the parent being a copy, but actually thanks to the copy-on-write semantic implemented by most modern operating systems the parent and child process will share the common memory pages. A page will be duplicated only when it changes in the child or in the parent. Since in theory all the pages may change while the child process is saving, Linux can’t tell in advance how much memory the child will take, so if the overcommit_memory
setting is set to zero the fork will fail unless there is as much free RAM as required to really duplicate all the parent memory pages. If you have a Valkey dataset of 3 GB and just 2 GB of free memory it will fail.
Setting overcommit_memory
to 1 tells Linux to relax and perform the fork in a more optimistic allocation fashion, and this is indeed what you want for Valkey.
You can refer to the proc(5) man page for explanations of the available values.
Yes, the Valkey background saving process is always forked when the server is outside of the execution of a command, so every command reported to be atomic in RAM is also atomic from the point of view of the disk snapshot.
It’s not very frequent that CPU becomes your bottleneck with Valkey, as usually Valkey is either memory or network bound. For instance, when using pipelining a Valkey instance running on an average Linux system can deliver 1 million requests per second, so if your application mainly uses O(N) or O(log(N)) commands, it is hardly going to use too much CPU.
However, to maximize CPU usage you can start multiple instances of Valkey in the same box and treat them as different servers. At some point a single box may not be enough anyway, so if you want to use multiple CPUs you can start thinking of some way to shard earlier.
You can find more information about using multiple Valkey instances in the Partitioning page.
As of version 4.0, Valkey has started implementing threaded actions. For now this is limited to deleting objects in the background and blocking commands implemented via Valkey modules. For subsequent releases, the plan is to make Valkey more and more threaded.
Valkey can handle up to 2^32 keys, and was tested in practice to handle at least 250 million keys per instance.
Every hash, list, set, and sorted set, can hold 2^32 elements.
In other words your limit is likely the available memory in your system.
If you use keys with limited time to live (Valkey expires) this is normal behavior. This is what happens:
INFO
output and in the DBSIZE
command.Because of this, it’s common for users with many expired keys to see fewer keys in the replicas. However, logically, the primary and replica will have the same content.
Read about the history of Valkey.