valkey-lru-cache - Man Page

The maxmemory configuration directive configures Valkey to use a specified amount of memory for the data set. You can set the configuration directive using the valkey.conf file, or later using the CONFIG SET command at runtime.

For example, to configure a memory limit of 100 megabytes, you can use the following directive inside the valkey.conf file:

maxmemory 100mb

Setting maxmemory to zero results into no memory limits. This is the default behavior for 64 bit systems, while 32 bit systems use an implicit memory limit of 3GB.

When the specified amount of memory is reached, how eviction policies are configured determines the default behavior. Valkey can return errors for commands that could result in more memory being used, or it can evict some old data to return back to the specified limit every time new data is added.

The exact behavior Valkey follows when the maxmemory limit is reached is configured using the maxmemory-policy configuration directive.

The following policies are available:

• noeviction: New values aren’t saved when memory limit is reached. When a database uses replication, this applies to the primary database
• allkeys-lru: Keeps most recently used keys; removes least recently used (LRU) keys
• allkeys-lfu: Keeps frequently used keys; removes least frequently used (LFU) keys
• volatile-lru: Removes least recently used keys with a time-to-live (TTL) set.
• volatile-lfu: Removes least frequently used keys with a TTL set.
• allkeys-random: Randomly removes keys to make space for the new data added.
• volatile-random: Randomly removes keys with a TTL set.
• volatile-ttl: Removes keys with a TTL set, the keys with the shortest remaining time-to-live value first.

The policies volatile-lru, volatile-lfu, volatile-random, and volatile-ttl behave like noeviction if there are no keys to evict matching the prerequisites.

LRU, LFU and volatile-ttl are implemented using approximated randomized algorithms.

Picking the right eviction policy is important depending on the access pattern of your application, however you can reconfigure the policy at runtime while the application is running, and monitor the number of cache misses and hits using the Valkey INFO output to tune your setup.

In general as a rule of thumb:

• Use the allkeys-lru policy when you expect a power-law distribution in the popularity of your requests. That is, you expect a subset of elements will be accessed far more often than the rest. This is a good pick if you are unsure.
• Use the allkeys-random if you have a cyclic access where all the keys are scanned continuously, or when you expect the distribution to be uniform.
• Use the volatile-ttl if you want to be able to provide hints to Valkey about what are good candidate for expiration by using different TTL values when you create your cache objects.

The volatile-lru and volatile-random policies are mainly useful when you want to use a single instance for both caching and to have a set of persistent keys. However it is usually a better idea to run two Valkey instances to solve such a problem.

It is also worth noting that setting a TTL value to a key costs memory, so using a policy like allkeys-lru is more memory efficient since there is no need for a TTL configuration for the key to be evicted under memory pressure.

It is important to understand that the eviction process works like this:

• A client runs a new command, resulting in more data added.
• Valkey checks the memory usage, and if it is greater than the maxmemory limit , it evicts keys according to the policy.
• A new command is executed, and so forth.

So we continuously cross the boundaries of the memory limit, by going over it, and then by evicting keys to return back under the limits.

If a command results in a lot of memory being used (like a big set intersection stored into a new key) for some time, the memory limit can be surpassed by a noticeable amount.

Valkey LRU algorithm is not an exact implementation. This means that Valkey is not able to pick the best candidate for eviction, that is, the key that was accessed the furthest in the past. Instead it will try to run an approximation of the LRU algorithm, by sampling a small number of keys, and evicting the one that is the best (with the oldest access time) among the sampled keys, while also managing a pool of good candidates for eviction. This algorithm consumes less memory than an exact LRU algorithm.

What is important about the Valkey LRU algorithm is that you are able to tune the precision of the algorithm by changing the number of samples to check for every eviction. This parameter is controlled by the following configuration directive:

maxmemory-samples 5

The reason Valkey does not use a true LRU implementation is because it costs more memory. However, the approximation is virtually equivalent for an application using Valkey. This figure compares the LRU approximation used by Valkey with true LRU. [IMAGE: LRU comparison] LRU comparison

The test to generate the above graphs filled a server with a given number of keys. The keys were accessed from the first to the last. The first keys are the best candidates for eviction using an LRU algorithm. Later more 50% of keys are added, in order to force half of the old keys to be evicted.

You can see three kind of dots in the graphs, forming three distinct bands.

• The light gray band are objects that were evicted.
• The gray band are objects that were not evicted.
• The green band are objects that were added.

In a theoretical LRU implementation we expect that, among the old keys, the first half will be evicted. The Valkey LRU algorithm will instead only probabilistically evicts the older keys.

As you can see, Redis OSS 3.0 does a reasonable job with 5 samples. Using a sample size of 10, the approximation is very close to an exact LRU implementation. (The LRU algorithm hasn’t changed considerably since this test was performed, so the performance of Valkey is similar in this regard.)

Note that LRU is just a model to predict how likely a given key will be accessed in the future. Moreover, if your data access pattern closely resembles the power law; most of the accesses will be in the set of keys the LRU approximated algorithm can handle well.

In simulations we found that using a power law access pattern, the difference between true LRU and Valkey approximation were minimal or non-existent.

However you can raise the sample size to 10 at the cost of some additional CPU usage to closely approximate true LRU, and check if this makes a difference in your cache misses rate.

To experiment in production with different values for the sample size by using the CONFIG SET maxmemory-samples <count> command, is very simple.

The Least Frequently Used eviction mode\c  is available as an alternative to LRU. This mode may work better (provide a better hits/misses ratio) in certain cases. In LFU mode, Valkey will try to track the frequency of access of items, so the ones used rarely are evicted. This means the keys used often have a higher chance of remaining in memory.

To configure the LFU mode, the following policies are available:

• volatile-lfu Evict using approximated LFU among the keys with a time-to-live (TTL) set.
• allkeys-lfu Evict any key using approximated LFU.

LFU is approximated like LRU: it uses a probabilistic counter, called a Morris counter\c  to estimate the object access frequency using just a few bits per object, combined with a decay period so that the counter is reduced over time. At some point we no longer want to consider keys as frequently accessed, even if they were in the past, so that the algorithm can adapt to a shift in the access pattern.

That information is sampled similarly to what happens for LRU (as explained in the previous section of this documentation) to select a candidate for eviction.

However unlike LRU, LFU has certain tunable parameters: for example, how fast should a frequent item lower in rank if it gets no longer accessed? It is also possible to tune the Morris counters range to better adapt the algorithm to specific use cases.

By default Valkey is configured to:

• Saturate the counter at, around, one million requests.
• Decay the counter every one minute.

Those should be reasonable values and were tested experimentally, but the user may want to play with these configuration settings to pick optimal values.

Instructions about how to tune these parameters can be found inside the example valkey.conf file in the source distribution. Briefly, they are:

lfu-log-factor 10
lfu-decay-time 1

The decay time is the obvious one, it is the amount of minutes a counter should be decayed, when sampled and found to be older than that value. A special value of 0 means: we will never decay the counter.

The counter logarithm factor changes how many hits are needed to saturate the frequency counter, which is just in the range 0-255. The higher the factor, the more accesses are needed to reach the maximum. The lower the factor, the better is the resolution of the counter for low accesses, according to the following table:

+--------+------------+------------+------------+------------+------------+
| factor | 100 hits   | 1000 hits  | 100K hits  | 1M hits    | 10M hits   |
+--------+------------+------------+------------+------------+------------+
| 0      | 104        | 255        | 255        | 255        | 255        |
+--------+------------+------------+------------+------------+------------+
| 1      | 18         | 49         | 255        | 255        | 255        |
+--------+------------+------------+------------+------------+------------+
| 10     | 10         | 18         | 142        | 255        | 255        |
+--------+------------+------------+------------+------------+------------+
| 100    | 8          | 11         | 49         | 143        | 255        |
+--------+------------+------------+------------+------------+------------+

So basically the factor is a trade off between better distinguishing items with low accesses VS distinguishing items with high accesses. More information is available in the example valkey.conf file.