Here we make a side by side comparison of MongoDB versus Cassandra. We provide examples, with commands and code, and not just a narrative explanation.

In sum, Cassandra is the modern version of the relational database, albeit where data is grouped by column instead of row, for fast retrieval. MongoDB stores records as documents in JSON format. It has a JavaScript shell and a rich set of functions which makes it easy to work with. Both systems are designed to scale enormously.

(This article is part of our MongoDB Guide. Use the right-hand menu to navigate.)

Data Structure

Cassandra is a column-oriented database. MongoDB stores records in JSON format. The MongoDB shells also support JavaScript so that you can build up queries and data conversion and manipulation in steps, saving each operation in a JavaScript variable.

A JSON data record is self-describing, because the field name and the data value is stored in the same place, i.e., inside the document. While a schema is not required with MongoDB, as JSON by definition does not need one, you can make one:

var schema = new mongoose.Schema({
cachedContents : {
largest : String,
non_null : Number,
null : Number,
top : [{
item : String,
count : Number
}],
smallest : String,
format : {
displayStyle : String,
align : String
}
}
});

In this Introduction to Cassandra, we explain that Cassandra tables use a schema, like a traditional relational database. But it does away with the notion of database normalization. Oracle administrators would say that the Cassandra schema is flat. The reason for this is storage across a network of commodity servers is cheap, so there is no reason to make joins to retrieve data, which slows down data retrieval. Instead, redundancy is OK. Also Cassandra tables do not require every field to be populated. So there is no overhead of storing empty data.

Cassandra also provides JSON support:

use json;
CREATE type json.sale ( id int, item text, amount int );
CREATE TABLE json.customers ( id int  PRIMARY KEY, name text, balance int, sales list> );
INSERT INTO json.customers (id, name, balance, sales) 
VALUES (123, 'Greenville Hardware', 700,
[{ id: 5544, item : 'tape', amount : 100},
{ id: 5545, item : 'wire', amount : 200}]) ;
select * from customers;
id  | balance | name                | sales
-----+---------+---------------------+--------------------------------------------------------------------------------
123 |     700 | Greenville Hardware | [{id: 5544, item: 'tape', amount: 100}, {id: 5545, item: 'wire', amount: 200}]

Adding Data

Cassandra:

CREATE TABLE Library.book (       
ISBN text, 
copy int, 
title text,  
PRIMARY KEY (ISBN, copy)
);
INSERT INTO  Library.book (ISBN, copy, title) VALUES('1234',1, 'Bible');

MongoDB:

db.collection.insertOne( { isbn: 100 } )
{
"acknowledged" : true,
"insertedId" : ObjectId("5c4493aa750820eae9756a15")
}