Overview of Redis key eviction policies (LRU, LFU, etc.)
When Redis is used as a cache, it is often convenient to let it automatically evict old data as you add new data. This behavior is well known in the developer community, since it is the default behavior for the popular memcached system.
This page covers the more general topic of the Redis
maxmemory directive used to limit the memory usage to a fixed amount. It also extensively covers the LRU eviction algorithm used by Redis, which is actually an approximation of
the exact LRU.
Maxmemory configuration directive
maxmemory configuration directive configures Redis
to use a specified amount of memory for the data set. You can
set the configuration directive using the
redis.conf file, or later using
CONFIG SET command at runtime.
For example, to configure a memory limit of 100 megabytes, you can use the
following directive inside the
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. Redis 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 Redis 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 the
expirefield set to
- volatile-lfu: Removes least frequently used keys with the
expirefield set to
- allkeys-random: Randomly removes keys to make space for the new data added.
- volatile-random: Randomly removes keys with
expirefield set to
- volatile-ttl: Removes keys with
expirefield set to
trueand the shortest remaining time-to-live (TTL) value.
The policies volatile-lru, volatile-lfu, volatile-random, and volatile-ttl behave like noeviction if there are no keys to evict matching the prerequisites.
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 Redis
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 Redis 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 Redis instances to solve such a problem.
It is also worth noting that setting an
expire value to a key costs memory, so using a policy like allkeys-lru is more memory efficient since there is no need for an
expire configuration for the key to be evicted under memory pressure.
How the eviction process works
It is important to understand that the eviction process works like this:
- A client runs a new command, resulting in more data added.
- Redis checks the memory usage, and if it is greater than the
maxmemorylimit , 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.
Approximated LRU algorithm
Redis LRU algorithm is not an exact implementation. This means that Redis 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.
However, since Redis 3.0 the algorithm was improved to also take a pool of good candidates for eviction. This improved the performance of the algorithm, making it able to approximate more closely the behavior of a real LRU algorithm.
What is important about the Redis 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:
The reason Redis does not use a true LRU implementation is because it costs more memory. However, the approximation is virtually equivalent for an application using Redis. This figure compares the LRU approximation used by Redis with true LRU.
The test to generate the above graphs filled a Redis 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 expired. The Redis LRU algorithm will instead only probabilistically expire the older keys.
As you can see Redis 3.0 does a better job with 5 samples compared to Redis 2.8, however most objects that are among the latest accessed are still retained by Redis 2.8. Using a sample size of 10 in Redis 3.0 the approximation is very close to the theoretical performance of Redis 3.0.
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 Redis 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
CONFIG SET maxmemory-samples <count> command, is very simple.
The new LFU mode
Starting with Redis 4.0, the Least Frequently Used eviction mode is available. This mode may work better (provide a better hits/misses ratio) in certain cases. In LFU mode, Redis 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-lfuEvict using approximated LFU among the keys with an expire set.
allkeys-lfuEvict any key using approximated LFU.
LFU is approximated like LRU: it uses a probabilistic counter, called a Morris counter 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 Redis 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 experimental, 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
redis.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