Redis vs. Memcached: Which Caching Solution to Choose and Why

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As an application developer, choosing the right caching solution can have a huge impact on your system‘s performance and scalability. The two most popular options for in-memory caching today are Redis and Memcached.

In this comprehensive guide, we‘ll dive deep into Redis vs. Memcached to help you make the best choice for your specific needs. I‘ll share my insights as a developer who has worked with both technologies extensively over the years.

Why Is Caching So Important?

Before we compare Redis and Memcached, let‘s step back and understand why caching is such a critical component of high-performance applications.

Caching enables you to avoid expensive database queries, computations, and network calls by keeping frequently accessed data readily available in memory.

Some key benefits caching provides:

Faster response times – Fetching data from RAM is orders of magnitude faster than disk or network I/O. Caching reduces request latency dramatically.
Higher throughput – More requests can be handled per second as fewer full operations need to be executed.
Reduced load – Databases and application servers see less load as fewer inbound requests reach them.
Cost savings – You can provision smaller database servers as the workload is reduced due to caching.
Improved scalability – Caching layer can easily be scaled out independently of the databases and application logic.

According to a Cloud Report by Akamai, a 100ms delay in response time can reduce conversions by 7%. So caching is critical for speed and revenue.

Now let‘s see how Redis and Memcached compare as caching technologies.

Redis Overview

Redis is an open-source, in-memory data store that supports different persistent data structures like strings, lists, sets, hashes and more. It can be used as a database, cache, message broker, and queue.

Some key capabilities of Redis:

Data structures – Supports advanced data types like sorted sets and geospatial indexes in addition to basics like strings and hashes.
Atomic operations – Commands can operate on multiple data structures together transactionally.
Persistence – Redis provides snapshotting to disk as well as append-only file logging.
Replication – Master-slave replication improves data safety and read scalability.
High availability – Sentinel and Cluster provide auto failover between nodes.
Pub/sub – Supports asynchronous messaging via channels and patterns.
Lua scripting – Transactional execution of Lua scripts across data structures.
RedisJSON – JSON document store recently added to Redis.

Redis‘ advanced and versatile data structures make it very useful for complex application data modeling beyond just caching. The built-in persistence, replication, Lua scripting, and high availability allow Redis to be used as a primary database too.

Let‘s now explore Memcached…

Memcached Overview

Memcached is an in-memory key-value cache intended solely for caching purposes, unlike Redis which can also function as a main database.

Some key aspects of Memcached:

Simple yet fast – Supports only string key-value pairs with expiration, but its simplicity makes Memcached extremely fast.
Multi-threaded – Concurrent request processing across multiple threads for high throughput and scalability.
Memory efficient – Small memory footprint and efficient memory utilization due to simpler design.
Horizontally scalable – You can add more Memcached servers easily behind a load balancer to scale out.
No persistence – In-memory only. Doesn‘t persist to disk. Data lost if a node goes down.
No replication – No native support for replication and high availability.

While Redis has a rich set of features, Memcached focuses only on being a simple, blazing fast cache store for transient data. It‘s ideal for straightforward caching use cases.

Next, we‘ll do a deep dive into their relative strengths and weaknesses.

Detailed Comparison of Redis vs. Memcached

Now that we‘ve provided an overview of Redis and Memcached, let‘s compare them in-depth across various technical factors:

	Redis	Memcached
Data Structures	Rich support for strings, hashes, lists, sets, sorted sets, bitmaps, hyperloglogs etc.	Only simple string key-value pairs supported.
Value Size Limits	String values can be up to 512 MB in size.	Maximum value size is 1 MB.
Memory Management	Single-threaded architecture. All data structures manipulated by one main thread.	Multi-threaded. Concurrent request processing across threads.
Persistence	Supports point-in-time RDB snapshots as well as AOF log-based persistence.	No persistence. Cache data only lives in memory.
Replication & HA	Master-slave replication and auto-failover with Sentinel/Cluster.	No native replication support.
Programming Languages	Well supported by most languages like Java, Python, Go, PHP, JavaScript etc.	Also very widely supported across languages.

Let‘s explore some of the key differences above in more detail:

Data Structure Support

One of Redis‘ biggest advantages is the variety of data structures it supports compared to just strings in Memcached.

Redis lets you represent logical data relationships and organize information better. For instance, you can store user profiles in Hashes, leaderboards in Sorted Sets, geolocation coordinates in Geo indexes, etc.

The atomic transactions across data structures help ensure consistency in multi-step operations. With Redis, you can model your application data more efficiently.

Memory Management Approaches

Both systems store data in RAM for speed. However, their memory management techniques differ.

Redis uses a single-threaded event loop to handle all requests sequentially. This avoids overhead from locks, synchronization and context switching present in multi-threaded systems. All data structures are manipulated by this single thread atomically.

In contrast, Memcached uses multiple threads with data partitioned across them. This allows more parallelism and concurrency in request processing. However, atomicity is not guaranteed with Memcached‘s approach.

So there is a classic tradeoff between simplicity and concurrency here. For strictly caching use cases, Memcached‘s multi-threaded approach delivers very high throughput and scalability.

Persistence, Replication & Durability

Redis gives you built-in snapshotting to disk as well as append-only logs for durability. So data can outlive server restarts or crashes.

Redis also handles replication for you automatically. Writes go to the master while reads can be parallelized across slaves. Failover is also managed automatically.

Memcached‘s simplicity comes at the cost of no built-in persistence or replication. Cached data only lives in memory – lose the cache node and all its data is gone. Any persistence, redundancy and high availability measures have to be implemented externally.

So with Redis, you get greater data safety and built-in scalability.

Use Case Fit

Both caching solutions are great for straightforward caching of rendered content like HTML fragments, API responses etc.

However, more complex data relationships like social graphs, geospatial data and leaderboards are better modeled in Redis‘ richer data structures.

Persistence, replication, transactions and message queues are also better handled by Redis.

For simple, raw speed caching of serialized objects, Memcached shines. But Redis gives you greater data modeling flexibility suitable for a wider range of use cases.

Now that we‘ve compared the core architectures and tradeoffs – let‘s look at real-world examples of Redis and Memcached deployments.

Real-World Use Cases

Redis and Memcached are used in production by many high-scalability companies. Let‘s look at some real-world examples to better understand their applied use.

Redis at Twitter

Twitter uses Redis extensively across its infrastructure for caching, queuing and its main data store.

Redis provides Twitter with ultra-fast access to constantly updating timelines and tweet data. Twitter leverages Redis Sets and Sorted Sets heavily to model its core social graph and content artifacts.

Redis is also great for Twitter‘s messaging queues and rate limiting use cases. Its Redis Search provides flexible full-text search functionality.

So Redis is a versatile Swiss Army knife powering many mission-critical parts of Twitter‘s infrastructure thanks to its rich data modeling and advanced features.

Memcached at Facebook

Facebook uses Memcached heavily to cache data from its backend databases like MySQL and HBase.

Some examples of data cached by Facebook in Memcached:

User profile information like friends, photos etc.
Popular queries and computed results
Rendered web pages

By serving cached data from memory instead of hitting the database frequently, Memcached reduces latency and improves throughput dramatically for Facebook.

The simplicity of Memcached‘s model makes it easy for Facebook engineers to apply caching easily and widely across the company‘s massive infrastructure.

When Redis and Memcached Work Together

It is common for large systems to use both Redis and Memcached together. Shopify is one such example:

Memcached caches relatively static catalog data like product details, images and descriptions.
Redis handles more rapidly changing e-commerce data like shopping cart and checkout data.
Redis also provides persistent queues for order processing.

So Shopify uses Memcached for simple content caching while Redis manages more advanced e-commerce data relationships.

Blending both solutions creates a very performant caching architecture.

Key Takeaways: Redis vs. Memcached

Based on this comprehensive feature comparison, let‘s summarize some key pointers:

Redis provides greater data modeling flexibility with support for diverse data structures.
Memcached offers maximum simplicity and raw caching speed for basic object caching.
Redis is suitable for both primary database usage and caching scenarios. Memcached is meant solely for caching.
Persistence, replication and transactions make Redis a more robust and resilient solution.
Applications requiring complex data relationships and logic are better served by Redis.
Simple transient object caching works well with Memcached‘s extreme speed and scalability.
Evaluate your specific application architecture and data models when deciding between the two.
Using Redis and Memcached together can provide complementary benefits.

When Should You Choose Redis or Memcached?

Based on this analysis, here are some recommendations on when to use Redis vs. Memcached:

Use Redis If You Need:

Complex or advanced data modeling and relationships
Persistence and redundancy
Transactions and message queues
Wide variety of native data structures
Main database functionality along with caching

Use Memcached For:

Simple transient key-value object caching
In-memory speed is the primary goal
Easy horizontal scaling is key
Data can be externalized or rebuilt if lost
A simple, fast and lean caching solution

Of course, evaluate your specific use case, data models and traffic patterns to decide which solution fits best.

Often a combination of both Redis and Memcached can be very powerful for different caching needs within one architecture. Many large-scale websites use the combined approach to great effect.

Conclusion

Both Redis and Memcached are extremely performant in-memory caching solutions. Redis provides richer data modeling capabilities and advanced features, while Memcached wins for pure speed and horizontal scaling.

Assess your application‘s data structures, traffic patterns, scaling needs and data safety requirements. This will determine whether Redis or Memcached is better suited, or if a combination makes sense.

There is no universally better choice between the two. Your particular use case and priorities will guide you to pick the right caching technology or blend of both.

I hope this detailed Redis vs. Memcached comparison has provided you deeper insight into their relative strengths. Caching is a critical piece of infrastructure – choose wisely and your applications will reap tremendous performance benefits.