For extensive Read and write operation MongoDB vs Cassandra

Question

I have used MongoDB but new to Cassandra. I have worked on applications which are using MongoDB and are not very large applications. Read and Write operations are not very much intensive. MongoDB worked well for me in that scenario. Now I am building a new application(w/ some feature like Stack Overflow[voting, totals views, suggestions, comments etc.]) with lots of Concurrent write operations on the same item into the database(in future!). So according to the information, I gathered via online, MongoDB is not the best choice (but Cassandra is). But the problem I am finding in Cassandra is Picking the right data model.

Construct Models around your queries. Not around relations and objects.

I also looked at the solution of using Mongo + Redis. Is it efficient to update Mongo database first and then updating Redis DB for all multiple write requests for the same data item?

I want to verify which one will be the best to solve this issue Mongo + redis or Cassandra?

Any help would be highly appreciated.

Answer 1

Picking a database is very subjective. I'd say that modern MongoDB 3.2+ using the new WiredTiger Storage Engine handles concurrency pretty well.

When selecting a distributed NoSQL (or SQL) datastore, you can generally only pick two of these three:

• Consistency (all nodes see the same data at the same time)
• Availability (every request receives a response about whether it succeeded or failed)
• Partition tolerance (the system continues to operate despite arbitrary partitioning due to network failures)

This is called the CAP Theorem .

MongoDB has C and P, Cassandra has A and P. Cassandra is also a Column-Oriented Database, and will take a bit of a different approach to storing and retrieving data than, say, MongoDB does (which is a Document-Oriented Database). The reality is that either database should be able to scale to your needs easily. I would worry about how well the data storage and retrieval semantics fit your application's data model, and how useful the features provided are.

Deciding which database is best for your app is highly subjective, and borders on an "opinion-based question" on Stack Overflow.

Using Redis as an LRU cache is definitely a component of an effective scaling strategy. The typical model is, when reading cacheable data, to first check if the data exists in the cache (Redis), and if it does not, to query it from the database, store the result in the cache, and return it. While maybe appropriate in some cases, it's not common to just write everything to both Redis and the database. You need to figure out what's cacheable and how long each cached item should live, and either cache it at read time as I explained above, or at write time.

Answer 2

It only depends on what your application is for. For extensive write apps it is way better to go with Cassandra