# Redis VS Memcached for cache

### **Redis :**

Redis is an open-source, in-memory data structure store, used as a database, cache, and message broker.

### Memcached:

Memcached is a distributed, high-performance, in-memory caching system. It is used to temporarily store and serve frequently requested data from memory.

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### The similarity between Redis and Memcached

* Both are key-value storage
    
* Both system store data in memory or RAM
    
* Both Redis and Memcached are capable of running on multiple servers
    

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### Data structure

* **Redis:** This supports various ranges of data types based on the additional module and some built-in support. Some data types: strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperlog, and geospatial indexes with radius queries
    
* **Memcached:** This support only string as the value in key-value entry
    

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### Transactions

* **Memcached** doesn't support transactions, although its operations are atomic.
    
* **Redis** provides out-of-the-box support for transactions to execute commands.
    

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### Architecture

* **Redis** uses a single-core architecture.
    
* **Memcached** implements a multi-threaded architecture by utilizing multiple cores.
    

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### Is there any speciality in Redis?

I know this portion is a little bit out of topic. But I have seen, in many blogs or youtube videos, shows redis is the key-value store only to just cache or temporary database.

So let's know a little bit about the full potential of Redis.

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1675356819084/691ce637-126b-463b-8ee8-495082b9f6c1.png align="center")

By enabling different modules, we can achieve the full potential

* **RedisJSON** to store JSON database directly in Redis.
    
* **RedisGraph** is a graph database built on Redis. This graph database uses GraphBlas under the hood for its sparse adjacency matrix graph representation.
    
* **RedisSearch** is a high-performance, full-text search engine that is built as a Redis module, offering efficient indexing and querying capabilities for text-based data within Redis.
    
* **RedisTimeSeries** is a Redis module for efficiently storing and querying time-series data, providing functionality for querying and analyzing time-stamped data, such as metrics, events, and logs.
    
* **RedisBloom** is a Redis module that provides scalable probabilistic data structures, such as Bloom filters, counting filters, and others, to support high-performance and memory-efficient data analysis and storage operations.
    

After seeing the modules and capability of Redis, we can only think of this

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1675357094759/fac08815-c784-4341-a500-7a3edb3b8ef2.png align="center")

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### Now, what is best for caching?

Here we need to think about the data structure.

**The best case to use Memcached over Redis**

* The cached data format is plain string data to cache like HTML pages, CSS, JS or anything other than that.
    
* The dataset size is going to be very large in future
    

If these two conditions are satisfied then it will be best to go with **Memcached**.

In other cases, you can go with Redis.

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We have reached the end of this blog.

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1675358079530/928c421a-7765-4f1d-a520-7ab3d5da1182.png align="center")