If you’re convinced that Redis is the right tool for caching I whole heartedly agree, it’s amazing! Here are four essential optimizations for leveraging Redis as a cache in your infrastructure. Most of these optimizations are still helpful even if you are using a hosted solution.
Use a Dedicated Cache Instance
Unlike Memcached, which is multi-threaded, Redis only runs a single thread per process. Considering the brute speed of Redis a single process seems like plenty for many workloads. That’s until your platform traffic starts rising, background jobs are firing continuously, pub/sub channels are relaying thousands of payloads over the network and the cache is being hit continuously. Each request to Redis is blocking, which can throw off the timing of background jobs or be an outright bottleneck for a set of load balanced servers.
Configure multiple separate instances of Redis to alleviate pressure on a single process. Separate instances by workload: one for background jobs, another for pub/sub and another dedicated to caching. Don’t rely on partitioning data into multiple Redis databases! Each of those databases is still backed by a single process so all of the same caveats apply.
- Do use dedicated Redis instances for distinct workloads.
- Do not use databases (
/2) to partition workloads.
You should care about persistence and replication, two features only available in Redis. Even if your goal is to build a cache it helps that after an upgrade or a reboot your data are [sic] still there.
Each Redis instance has its own configuration file and can be tuned according to the use-case. Caching servers, for example, can be configured to use RDB persistence to periodically save a single backup instead of AOF persistence logs. By only taking periodic snapshots of the database RDB maximizes performance at the expense of up-to-the-second consistency. For a hybrid Redis instance that may be storing business critical background jobs data consistency is paramount. With a cache it is alright to lose some data in the event of a disaster, after reboot most of the cache will be warm and intact.
- Do optimize cache persistence speed by favoring RDB over AOF.
- Do set
noto prevent all writes from failing when snapshotting fails. This requires proper monitoring and alerts for failures, which you are doing anyhow, right?
- Do not disable persistence entirely, it is valuable for warming the cache after an upgrade or restart.
Manage Memory Effectively
Once you have a Redis instance dedicated to caching you can start to optimize memory management in ways that don’t make sense for a hybrid database. When ephemeral and long-lived data is co-mingled it is imperative that ephemeral keys have a TTL and Redis is free to clean up expired keys.
Redis can manage memory in a variety of ways. The management
policies vary from never evicting keys (
noeviction) to randomly evicting a key
when memory is full (
allkeys-random). Hybridized databases typically use
volatile-* policies, which require the presence of expiration values or they
behave identically to
noeviction. There is another policy that works better
for cache data,
allkeys-lru policy attempts to remove the
less recently used (LRU) keys first in order to make space for the new data
It is also worth to note that setting an expire to a key costs memory, so using a policy like
allkeys-lruis more memory efficient since there is no need to set an expire for the key to be evicted under memory pressure.
Redis uses an approximated LRU algorithm instead of an exact algorithm. What
this means is that you can conserve memory in favor of inaccuracy by tuning the
number of samples to check with each eviction. Set
maxmemory-samples to a low
level, say around 5, for “good enough” eviction with a low memory footprint.
Lastly, and most importantly, set a
maxmemory limit to a comfortable amount of
RAM. Without a limit Redis can’t function properly as a LRU cache and will start
replying with errors when memory consuming commands start failing.
- Do set a
- Do use
allkeys-lrupolicies for dedicated cache instances. Let Redis manage key eviction by itself.
- Do not set
expirefor keys, it adds additional memory overhead per key.
- Do tune the precision of the LRU algorithm to favor speed over accuracy. Redis does not pick the best candidate for eviction, it samples a small number of keys and chooses the entry with the oldest access time.
Utilize Intelligent Caching
Because of Redis data structures, the usual pattern used with memcached of destroying objects when the cache is invalidated, to recreate it from the DB later, is a primitive way of using Redis.
Only storing serialized HTML or JSON as strings, the standard way of caching for web applications, doesn’t fully utilize Redis as a cache. One of the great strengths of Redis over Memcached is the rich set of data structures available. Ordered lists, structured hashes, and sorted sets are particularly useful caching tools only available through Redis. Caching is more than stuffing everything into strings.
Let’s look at the Hash type for a specific example.
Small hashes are encoded in a very small space, so you should try representing your data using hashes every time it is possible. For instance if you have objects representing users in a web application, instead of using different keys for name, surname, email, password, use a single hash with all the required fields.
Instead of storing objects as a serialized string you can store the object as fields and values available through a single key. Using a Hash saves web servers the work of fetching an entire serialized value, de-serializing it, updating it, re-serializing it, and finally writing it back to the cache. Eliminating that flow for every minor update pushes the work into Redis and out of your applications, where it is supposed to be.
- Do use the native Redis types wherever possible (
- Do not use the
stringtype for structured data, reach for a
Happy optimizing. Go forth and cache!