Redis and Memcached in Caching

The cache is temporary storage where data is stored so that in the future data can be accessed faster. So, caching is the process of storing data in Cache. Redis and memcached are good choices for implementing a highly available in-memory cache to decrease data access latency with disk or SSD, high throughput and ease the load of the database and application. Web page caching, Database query results caching, persistent session caching and caching of frequently used objects such as images, files, and metadata are popular examples of caching solutions with Redis.

Redis is a fast in-memory database and cache, open source under a BSD license, written in C and optimized for speed. Redis’ name comes from “REmote DIctionary Server”.

Redis is often called a data structure server because its core data types are similar to those found in programming languages like strings, lists, dictionaries (or hashes), sets, and sorted sets. It also provides many other data structures and features for approximate counting, geolocation, and stream processing.

Of the NoSQL databases Redis’ various data structures take it closest to the native data structures programmers most commonly use inside applications and algorithms. This ease of use makes it ideal for rapid development and fast applications, as core data structures are easily shared between processes and services.

By default Redis stores data in memory, with periodic disk persistence as a default. As Redis persists data to disk it can serve as a classical database for many use cases as well as a cache. When full, Redis will return an error to a client, but it can be configured as a cache to eject older and less important data as new data comes in. In both cases the size of available memory is the main constraint on its use.

memcached is a high performance, distributed memory object caching system. It is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database or API calls. Items are made up of a key, expiration time, optional flags and raw data. It does not understand data structures: you must upload data that is pre-serialized.

memcached is made up of:

Most applications benefits from caching, unless the application is very write heavy (thus the cache is invalidated all the time).

There are a number of key differences between Redis and Memcached which need to be mentioned. These differences are very specific around how Redis and Memcached handle the data they are caching, and how they are able to deal with it at scale.

Strings are a basic value to store data. Redis strings are binary safe, which means that strings can store something like a small image. String values can be up to 512MB in length.

Redis Lists are a way to store unordered data, which can then be accessed by index. Redis implements what is known as a Linked List which is a slightly different way of implementing lists than an Array. What this means is that Redis is extremely fast and consistent when adding or removing a value from the start or end of a list.

Hashes, or dictionaries as they are known in some languages, allow for the storage of tightly related data in a series of fields with their own key value relationships. Although it is very similar to common data structures in programming languages it is important to note there isn’t always a 1:1 mapping as Redis’ version is fairly “flat” (e.g. a Python dictionary can be nested and hold different types of objects and cannot be stored into a Redis Hash easily if it has these properties).

Sets are unordered collections of unique items. Sets can add, remove, and test for the existence of members. The benefit of a set over a list is that a set does not allow duplicates, meaning that for data where you are just storing a unique key, if it does not exist, you can just add the key and know that it will be unique within the set.

Sorted sets are an expansion of the Redis set, where they are a collection of unique strings. However each member of the Redis sorted set is given a score to help order the set. Sorted sets also allow for accessing ranges based on the score given by Redis.

Redis will experience improved performance as load from clients are spread across multiple clusters. Cluster aware drivers understand which node the data is on and connect directly do that node.

Using a combination of replication along with clustering allows for high availability for applications. Redis Cluster can be set up with a selection of master instances, and connected replicas. If a single master goes offline in the cluster, it can failover, and the replica will be automatically promoted to master in the cluster. When the failed instance comes online again, it will automatically sync as the replica for the new master. Replicas can be kept in separate racks for redundancy.

As Redis is memory intensive, clustering allows for horizontal scalability, where load is spread over multiple instances with smaller memory footprints, rather than a single instance with a lot of memory. In a traditional server application instance, to get better performance from the application, you could increase the system resources via vertical scaling. At some point though the system will be limited by either availability of technology to increase the size of the server or, the more likely scenario, it is no longer cost effective to increase the machine resources. The main alternative to this is called horizontal scaling, where servers with smaller resources are grouped together in a cluster. The cluster will have the same or better performance as vertical scaling at a smaller cost. Horizontal scaling can be particularly important for Redis, as memory is very expensive as the size increases.

Memcached formerly had the advantage over Redis in multithreading speed. This meant that in certain situations, Memcached could outperform Redis. However, as of Redis 6, multithreading is supported in Redis and because of this should see an improvement in multithreaded workloads.

Memcached vs Redis — Features Compared:

Memcached is, by design, very simple to set up and use. If you had an extremely simple application on only a few servers, and only required simple string interpretation for your application, then Memcached may be the correct choice for you. However, even with small workloads, it may be worth the little extra effort to set up Redis, as it has the option for expandability later on.

Redis is a very well rounded, mature product, with active ongoing development. It has a bunch of features and really gives the option for expandability into the future. You should be using Redis when:

Memcached was one of the first popular open source memory caching solutions and works well for basic key value workloads. However, if you are working in the enterprise, or are just looking for a more modern, feature rich and actively developed product, Redis is the best solution.

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