Which Caching Solution to Choose and Why

Which Caching Solution to Choose and Why

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Caching solutions help you speed up your application performance. When searching for the best caching solutions, Redis and Memcached come out as two of the most popular choices.

But how do you decide which one to choose when you’re comparing Redis vs. Memcached? This guide will help you understand what both of them have to offer and lead you to an informed decision.

Whether you’re looking to boost your application speed, reduce database load, or seamlessly scale your system, understanding caching solutions is important.

While Redis and Memcached can be used as application caching solutions, they have their own features. On the one hand, both offer you sub-millisecond latencies and high throughput; on the other hand, they differ regarding features like support for data structures and storage.

But before diving into Redis vs. Memcached, let’s look at caching.

What Is Caching, and Why Do You Need It?

When you’re performing any operation in your application, it uses up your system resources. It can be your processing when you’re running a CPU-intensive task. Or it can be a network-intensive task like reading from a file or a database.

Your system performance is impacted when you’re performing the same CPU or network-intensive task repeatedly. Consider the scenario where you’re looking up some data from the database, and your query is complex. Not only will it take time to compute and fetch the data from the database, but having a complex query uses up more of your database resources.

Now, imagine if there’s no change in the query result between two consecutive requests. However, you’re still ending up running the same expensive query every time you need the data. Well, what if there’s an alternative? This is where caching comes into play.

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At its core, caching involves storing frequently accessed data in high-speed memory. This allows you to quickly retrieve it when the data is requested again. Additionally, you eliminate the need to repeat resource-intensive operations – such as database queries or complex calculations. Thus, you improve your system performance, efficiency, and responsiveness.

Here are the benefits you get from caching:

  • Your latencies are reduced
  • Your websites and applications load and respond faster
  • Unnecessary resource utilization is reduced
  • You end up with less strain on backend servers
  • Your database is safeguarded from frequently running heavy queries

Redis: An Overview

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Redis is an open-source data store that works in memory. You store data in key-value pairs. Besides using it as a caching solution, you can use Redis as a database, message broker, or queue.

Because Redis runs in-memory, you get sub-millisecond query latencies. Fetching data from the memory is much faster than from the disk. Thus, you get very high throughput and can perform a lot of reads and writes per second.

When you’re comparing Redis vs. Memcached, Redis supports a wide range of data structures. You can store any text or binary data in the form of Strings (512 MB max size). If you need to store objects, then you can use the field-value pairs called Hashes. If you have to store a collection of Strings, you can use Lists, Sets, or Sorted Sets.

You get client library support for major programming languages – Java, Python, Go, NodeJs, C# (C-Sharp), and .Net (Dot Net). Additionally, when you install Redis, you get an in-built command line utility tool called redis-cli. This CLI tool lets you quickly check if your Redis server is working properly. You can also send commands to read, write, or modify data.

Benefits of Redis

  • Redis is open-source and available for free
  • There are official clients for major programming languages
  • You can use it as a caching solution, message broker, database, or a queue
  • It offers very high performance with sub-millisecond latencies
  • You get several built-in data structures like Strings, Lists, Sets, and Sorted Sets
  • With primary-replica architecture, you get high availability and scalability

Next, we will discuss Memcached in detail.

Memcached: An Overview

Memcached is a free and open-source distributed memory object caching system. It offers very high performance. You get an in-memory key-value store for small chunks of arbitrary data. But before comparing Redis vs. Memcached, let’s see what Memcached exactly does.

Memcached

The memory in your computer system is not evenly distributed. Free memory is present in arbitrary chunks and different sizes. Memcached allows you to access available memory from different parts of your system. Thus, you take memory from where there’s excess and use it where it’s needed.

Being a simple key-value store, Memcached servers don’t understand what the data represents. You store raw pre-serialized data along with a key, an expiration time, and optional flags. Furthermore, you don’t get any support for in-built data structures either.

In comparison to Redis, Memcached servers don’t talk to each other. You don’t get synchronization, replication, or broadcasting. Thus, cache availability and invalidation are simplified. As a client, you delete or overwrite data directly on the server that owns it.

Because of this extremely simple architecture, Memcached gives you very high performance. On a fast machine with high-speed networking, you can easily have Memcached handle 200,000+ requests per second.

Benefits of Memcached

  • Because it uses in-memory data, the retrieval is faster compared to traditional disk
  • There are APIs available for the most popular programming languages
  • Memcached reduces the need for repeated data lookup by caching results
  • You can cache database results, API responses, or even rendered web pages
  • It helps you reclaim fragmented memory
  • It uses Least Recently Used caching, and items expire after a specified time
  • You can build your own abstractions as Memcached

Next, we will explore the difference between Redis and Memcached.

Redis vs. Memcached: Comparison Table

Features Redis Memcached
Data Structures Has support for in-built data structures like Strings, Lists, Sets, Sorted Sets No support for in-built data structures. Raw pre-serialized data is stored
Data Size Can store values up to 512 MB in size Can store values up to 1 MB in size
Disk Storage Support Natively supports disk storage using Redis Database File (RDB) or Append-Only Files (AOF) There’s no native support for writing to disk. Third-party tools like libmemcached-tools are available
Threading It’s single-threaded It’s multi-threaded
Replication Has a primary-replica architecture that allows it to have replication Does not support replication
Cache Eviction Uses Least Recently Used policy for cache eviction. Other policies can be configured Uses Least Recently Used policy for cache eviction
Programming Languages Supports major programming languages Supports major programming languages

Now, let’s look at some of the use cases that are solved by Redis and Memcached.

Use Cases for Redis and Memcached

#1. Serving E-Commerce at Scale: Shopify

ecom

If you’ve ever looked for selling products online, then chances are that you’ve come across Shopify. It’s a multi-channel e-commerce platform that lets you easily create an online store for your business. At its peak, Shopify serves 80K requests per second, powering its 600K merchants. However, handling such traffic with minimum latencies is a challenge.

To tackle this, Shopify uses both Memcached and Redis in its tech stack. At its core, Shopify has a fairly simple architecture that uses MySQL for the database, Memcached as the key-value store, and Redis for the queue. Sometimes, you don’t need to look at Redis vs. Memcached. Rather, you can use both of them in your application architecture.

When you’re looking at any e-commerce platform, there is a lot of static data that doesn’t change that often. This includes things like item images, descriptions, and store information. Rather than querying all these data for every request, storing them in a key-value store makes the system fast and improves its performance.

Using a caching solution like Memcached to serve static content reduces the load from your backend servers and databases.

#2. Distributed Application Data Caching: Pinterest

Pinterest-1

Pinterest has grown to become the largest space where people come to find inspiration. You can go on Pinterest and look at all the pins and boards that other people have created. Behind the scenes, a lot is going on. Every request coming in goes through several different services and performs a heavy amount of computation. This involves looking at pins and recommendations as well.

In order to protect the backend services and avoid repeating computations, Pinterest employs a distributed cache layer. This stores the results of repeated computations. Thus, the requests do not reach the compute-expensive or storage-expensive services and databases. Rather, the cache absorbs a significant share of the traffic. Pinterest’s caching layer spans over thousands of machines and serves more than 150 million requests per second.

If you look at how Pinterest manages its caching layer, you’ll find it runs Memcached and mcrouter as its backbone. Because of its asynchronous event-driven nature and multithreaded processing, Memcached is extremely efficient. Add to it the fact that it has a very simple architecture. You can build your own abstractions and horizontally scale them. That’s how Pinterest handles its traffic.

#3. Handling Data Security at Scale: CloudSponge

If you’re looking for a software-as-a-service (SaaS) that lets your users quickly send out invitations, coupons, or greeting cards, then CloudSponge is the answer. It helps its users import all major address books so that users don’t have to manually type the contact email addresses.

In a single year, CloudSponge processes nearly 2 trillion email addresses through its servers. This comes with its own security challenge, as any vulnerability in the system can give hackers the chance to steal the data.

You can use Redis as more than just a caching solution. This is exactly what CloudSponge does. Redis holds all the contact data in memory. The data is held just long enough for customers to retrieve it. After that, it gets deleted. None of the data is persisted to disk, even though Redis provides that option.

#4. Other Common Uses for Caching Solutions

Here are some of the other common scenarios where caching solutions are used:

❇️ Chat Messaging Systems

If you’re creating your own chat messaging system, then caching solutions are an invaluable component that you need in your architecture. You can store frequently accessed data like user profiles, contact lists, and recent messages. This, in turn, helps reduce the strain on the database and boosts system responsiveness.

Real-time features like typing indicators and presence notifications also benefit from caching. If you’re using a distributed cache, then you can scale your backend servers without the need to scale up your caches.

❇️ Location Services

If you open an app and look at location-based services like estimating distance, arrival times, and nearby recommendations, you’re relying on caches tuned for location data. Location data is stored in the form of a geospatial index.

With a geospatial index, it’s possible to store the location of any object in your data store. Redis supports geospatial indexes by default. Owing to it being served from memory, location information can be effectively shared in real time.

❇️ Real-time Analytics

Online gaming has grown to be a massive industry. You might be familiar with co-op mobile games or fantasy sports apps where players rely on sub-second latencies to make a decision or make changes.

Using a cache, you can have real-time analytics like maintaining a leaderboard. For apps like fantasy sports, you can store the player stats and information in your cache layer to boost app performance.

Which One Should You Choose?

Redis stands out as a versatile option with support for various data structures, making it suitable for your applications that require more than just basic caching. Its sub-millisecond query latencies and high throughput, along with client library support for major programming languages, make it a strong contender for systems demanding both speed and flexibility. You also get the native support of persisting the data to disk if you require it.

On the other hand, Memcached excels when it comes to simplicity and high-speed caching. It’s an ideal choice for you when your use case is caching raw, pre-serialized data. It efficiently utilizes available memory across different parts of your system. Memcached’s straightforward architecture results in very high performance, particularly on fast machines with high-speed networking.

To make the right choice between Redis and Memcached, consider factors such as your application’s complexity, data structure requirements, and scalability needs. Redis is great for situations where you require diverse data structures and more advanced features, while Memcached provides you with the lowest latencies and highest throughput in straightforward, high-performance caching scenarios.

When it comes to Redis vs. Memcached, the decision ultimately should align with your project’s specific goals and constraints. Sometimes, application data caching might not be enough.

Next, check out how to setup a local DNS caching server on Linux.



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