Difference between In-Memory cache and In-Memory Database

Question

I was wondering if I could get an explanation between the differences between In-Memory cache(redis, memcached), In-Memory data grids (gemfire) and In-Memory database (VoltDB). I'm having a hard time distinguishing the key characteristics between the 3.

Parnell

Answer 1

Cache - By definition means it is stored in memory. Any data stored in memory (RAM) for faster access is called cache. Examples: Ehcache, Memcache Typically you put an object in cache with String as Key and access the cache using the Key. It is very straight forward. It depends on the application when to access the cahce vs database and no complex processing happens in the Cache. If the cache spans multiple machines, then it is called distributed cache. For example, Netflix uses EVCAche which is built on top of Memcache to store the users movie recommendations that you see on the home screen.

In Memory Database - It has all the features of a Cache plus come processing/querying capabilities. Redis falls under this category. Redis supports multiple data structures and you can query the data in the Redis ( examples like get last 10 accessed items, get the most used item etc). It can span multiple machine and is usually very high performant and also support persistence to disk if needed. For example, Twitter uses Redis database to store the timeline information.

Stopcock

Answer 2

I don't know about gemfire and VoltDB, but even memcached and redis are very different. Memcached is really simple caching, a place to store variables in a very uncomplex fashion, and then retrieve them so you don't have to go to a file or database lookup every time you need that data. The types of variable are very simple. Redis on the other hand is actually an in memory database, with a very interesting selection of data types. It has a wonderful data type for doing sorted lists, which works great for applications such as leader boards. You add your new record to the data, and it gets sorted automagically.

So I wouldn't get too hung up on the categories. You really need to examine each tool differently to see what it can do for you, and the application you're building. It's kind of like trying to draw comparisons on nosql databases - they are all very different, and do different things well.

Adrial

Answer 3

I would add that things in the "database" category tend to have more features to protect and replicate your data than a simple "cache". Cache is temporary (usually) where as database data should be persistent. Many cache solutions I've seen do not persist to disk, so if you lost power to your whole cluster, you'd lose everything in cache.

But there are some cache solutions that have persistence and replication features too, so the line is blurry.

Bb

What would be the difference between In-Memory data grid and In-Memory DB? – Parnell

Labels get tricky. But in general, anything called a "DB" (DataBase) is going to have more features to ensure data integrity, data security, and data recovery. A "grid" might be more focused on speed and clustering rather that data integrity. Also the term "grid" definitely implies multiple nodes, probably masterless, where as an in-memory database could be entirely on one node. – Bb

Answer 4

An in-memory Cache is a common query store therefore relieves DB of read Workloads. Common examples of in-memory cache are Redis cache. An example could be Web site storing popular searches made by clients thereby relieving the DB of some load.

In-memory Cache provides query functionality on top of caching (storing session data in RAM (temporary storage)).

Memcache falls in the temp store caching category.

Bursa

Answer 5

At very high level:

In-memory Cache is simple Map/Dictionary like structure where you add data and fetch/lookup by key. In addition, it usually has logic to handle the purging data based on expiration criteria. Its confined to single process or node.

In-memory Grid is distributed cache where multiple instances communicate with each other and work together as a cluster. It can handle cases like when an entry added to cache on one node, you can fetch/lookup same entry from a different node as all nodes are connected and talks to each other.

In-memory DB provides sql query capabilities which is lot more flexible than simple fetch by key. It handle multiple types of entries and usage of distinct top, group by, order by etc. Some in-memory DBs support like Transaction (ACID) semantics as well.

Obviously, there will be range of features and lot more nuances involved when you pick a specific tool.

Haskell

did you use any generative AI in the writing of this answer or directly reference other material without paraphrase? – Geis