Cassandra vs MongoDB

Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

Cassandra vs MongoDB: What are the differences?

Introduction: In the realm of NoSQL databases, Cassandra and MongoDB are two popular options known for their scalability and high performance. Understanding the key differences between these databases is essential for making an informed decision when choosing the right one for a particular use case.

Data Model: One of the key differences between Cassandra and MongoDB lies in their data models. Cassandra follows a column-family data model, where data is organized into rows and columns. On the other hand, MongoDB uses a document-based data model, where data is stored in flexible, JSON-like documents.
Consistency: Another crucial difference is how Cassandra and MongoDB handle consistency. Cassandra uses a tunable consistency model that allows users to choose between strong or eventual consistency. MongoDB, on the other hand, offers strong consistency by default, ensuring that all reads reflect the latest write operation.
Horizontal Scalability: When it comes to scaling, Cassandra and MongoDB have different approaches. Cassandra is designed for seamless horizontal scalability, making it ideal for handling large amounts of data across multiple nodes. MongoDB also supports scaling, but it may require more complex configuration compared to Cassandra.
Query Language: The query language used in Cassandra and MongoDB sets them apart. Cassandra uses CQL (Cassandra Query Language), a SQL-like language for interacting with the database. MongoDB, on the other hand, uses a query language that is more focused on document-based queries and manipulation.
Data Distribution: The way data is distributed across nodes in a cluster differs between Cassandra and MongoDB. Cassandra employs a partitioning strategy known as consistent hashing to distribute data evenly across nodes, ensuring high availability and fault tolerance. MongoDB uses sharding to horizontally partition data, but it requires careful consideration of shard keys for optimal performance.

In Summary, understanding the key differences in data model, consistency, scalability, query language, and data distribution between Cassandra and MongoDB is crucial for selecting the right NoSQL database for a specific use case.

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Advice on MongoDB, Cassandra

George

Student

Mar 18, 2020

Needs adviceon

PostgreSQL

Python

Django

Hello everyone,

Well, I want to build a large-scale project, but I do not know which ORDBMS to choose. The app should handle real-time operations, not chatting, but things like future scheduling or reminders. It should be also really secure, fast and easy to use. And last but not least, should I use them both. I mean PostgreSQL with Python / Django and MongoDB with Node.js? Or would it be better to use PostgreSQL with Node.js?

*The project is going to use React for the front-end and GraphQL is going to be used for the API.

Thank you all. Any answer or advice would be really helpful!

620k views620k

Comments

Ido

Mar 6, 2020

Decided

My data was inherently hierarchical, but there was not enough content in each level of the hierarchy to justify a relational DB (SQL) with a one-to-many approach. It was also far easier to share data between the frontend (Angular), backend (Node.js) and DB (MongoDB) as they all pass around JSON natively. This allowed me to skip the translation layer from relational to hierarchical. You do need to think about correct indexes in MongoDB, and make sure the objects have finite size. For instance, an object in your DB shouldn't have a property which is an array that grows over time, without limit. In addition, I did use MySQL for other types of data, such as a catalog of products which (a) has a lot of data, (b) flat and not hierarchical, (c) needed very fast queries.

575k views575k

Comments

Mike

Mar 20, 2020

Needs advice

We Have thousands of .pdf docs generated from the same form but with lots of variability. We need to extract data from open text and more important - from tables inside the docs. The output of Couchbase/Mongo will be one row per document for backend processing. ADOBE renders the tables in an unusable form.

241k views241k

Comments

Detailed Comparison

MongoDB	Cassandra
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.	Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	-
Statistics
GitHub Stars 27.7K	GitHub Stars 9.5K
GitHub Forks 5.7K	GitHub Forks 3.8K
Stacks 96.6K	Stacks 3.6K
Followers 82.0K	Followers 3.5K
Votes 4.1K	Votes 507
Pros & Cons
Pros 829 Document-oriented storage 594 No sql 554 Ease of use 465 Fast 410 High performance Cons 6 Very slowly for connected models that require joins 3 Not acid compliant 2 Proprietary query language	Pros 119 Distributed 98 High performance 81 High availability 74 Easy scalability 53 Replication Cons 3 Reliability of replication 1 Size 1 Updates

What are some alternatives to MongoDB, Cassandra?

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

CouchDB

Apache CouchDB is a database that uses JSON for documents, JavaScript for MapReduce indexes, and regular HTTP for its API. CouchDB is a database that completely embraces the web. Store your data with JSON documents. Access your documents and query your indexes with your web browser, via HTTP. Index, combine, and transform your documents with JavaScript.

Related Comparisons

Cassandra vs MongoDB: What are the differences?

Data Model: One of the key differences between Cassandra and MongoDB lies in their data models. Cassandra follows a column-family data model, where data is organized into rows and columns. On the other hand, MongoDB uses a document-based data model, where data is stored in flexible, JSON-like documents.
Consistency: Another crucial difference is how Cassandra and MongoDB handle consistency. Cassandra uses a tunable consistency model that allows users to choose between strong or eventual consistency. MongoDB, on the other hand, offers strong consistency by default, ensuring that all reads reflect the latest write operation.
Horizontal Scalability: When it comes to scaling, Cassandra and MongoDB have different approaches. Cassandra is designed for seamless horizontal scalability, making it ideal for handling large amounts of data across multiple nodes. MongoDB also supports scaling, but it may require more complex configuration compared to Cassandra.
Query Language: The query language used in Cassandra and MongoDB sets them apart. Cassandra uses CQL (Cassandra Query Language), a SQL-like language for interacting with the database. MongoDB, on the other hand, uses a query language that is more focused on document-based queries and manipulation.
Data Distribution: The way data is distributed across nodes in a cluster differs between Cassandra and MongoDB. Cassandra employs a partitioning strategy known as consistent hashing to distribute data evenly across nodes, ensuring high availability and fault tolerance. MongoDB uses sharding to horizontally partition data, but it requires careful consideration of shard keys for optimal performance.

Cassandra vs MongoDB

Overview