Cassandra vs FaunaDB

Overview

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

Fauna

Stacks112

Followers153

Votes27

Cassandra vs FaunaDB: What are the differences?

Introduction: In the realm of databases, Cassandra and FaunaDB are two popular choices. Despite serving similar purposes, they differ in various aspects which play a crucial role in database selection for an organization or project.

Data Model: Cassandra follows a wide-column store data model, storing data in rows and columns akin to a traditional relational database. On the other hand, FaunaDB utilizes a document-oriented data model, organizing data in hierarchical structures facilitating easy retrieval and querying.
Consistency: When it comes to consistency, Cassandra offers tunable consistency levels, allowing users to trade off between consistency and availability. FaunaDB, however, guarantees strict consistency globally across all operations ensuring that reads reflect the most recent write.
Transaction Support: Cassandra lacks comprehensive transaction support, requiring developers to implement complex application-side logic to maintain data integrity in case of multiple operations. In contrast, FaunaDB supports asset transactions out of the box, simplifying the development process and ensuring data consistency.
Query Language: Cassandra employs CQL (Cassandra Query Language) as its query language, which resembles SQL. FaunaDB utilizes its own query language, FQL (Fauna Query Language), which is specifically designed to work with its document-oriented data model, providing powerful querying capabilities.
Scalability: In terms of scalability, while both Cassandra and FaunaDB support horizontal scalability by adding more nodes to the cluster, FaunaDB offers built-in multi-region replication which simplifies data distribution and ensures low-latency access for global applications compared to Cassandra.
Consistency Model: Cassandra employs an eventually consistent model, where inconsistencies may exist temporarily during network partitions but eventually resolve. In contrast, FaunaDB uses a strong consistency model by default, ensuring that reads reflect the latest write across all operations, offering robust data integrity guarantees.

In Summary, Cassandra and FaunaDB differ in their data models, consistency approaches, transaction support, query languages, scalability options, and consistency models, making them distinctive choices for various database requirements.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on Cassandra, Fauna

Vinay

Head of Engineering

Sep 19, 2019

Needs advice

The problem I have is - we need to process & change(update/insert) 55M Data every 2 min and this updated data to be available for Rest API for Filtering / Selection. Response time for Rest API should be less than 1 sec.

The most important factors for me are processing and storing time of 2 min. There need to be 2 views of Data One is for Selection & 2. Changed data.

174k views174k

Comments

Detailed Comparison

Cassandra	Fauna
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.	Escape the boundaries imposed by legacy databases with a data API that is simple to adopt, highly productive to use, and offers the capabilities that your business needs, without the operational pain typically associated with databases.
-	Native support for GraphQL and others. Easily access any data with any API. No middleware necessary.; Access all data via a data model that best suits your needs - relational, document, graph or composite.; A unique approach to indexing makes it simpler to write efficient queries that scale with your application.; Build SaaS apps more easily with native multi-tenancy and query-level QoS controls to prevent workload collisions.; Eliminate data anomalies with multi-region ACID transactions that don't limit number of keys or documents.; Data-driven RBAC that combines with SSL to offers reliable protection, and yet is simple to understand and codify.; Travel back in time with temporal querying. Run queries at a point-in-time or as change feeds. Track how your data evolved.; Dynamically replicates your data to global locations, so that your queries run fast no matter where your users are.; Easily deploy a FaunaDB cluster on your workstation accompanied by a powerful shell and tools to simplify your workflow.;
Statistics
GitHub Stars 9.5K	GitHub Stars -
GitHub Forks 3.8K	GitHub Forks -
Stacks 3.6K	Stacks 112
Followers 3.5K	Followers 153
Votes 507	Votes 27
Pros & Cons
Pros 119 Distributed 98 High performance 81 High availability 74 Easy scalability 53 Replication Cons 3 Reliability of replication 1 Size 1 Updates	Pros 5 100% ACID 4 Generous free tier 4 Removes server provisioning or maintenance 3 No more n+1 problems (+ GraphQL) 3 Low latency global CDN's Cons 1 Log stack traces to avoid improper exception handling 1 Susceptible to DDoS (& others) use timeouts throttling 1 Must keep app secrets encrypted

What are some alternatives to Cassandra, Fauna?

MongoDB

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.

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.

Related Comparisons

Cassandra vs FaunaDB: What are the differences?

Data Model: Cassandra follows a wide-column store data model, storing data in rows and columns akin to a traditional relational database. On the other hand, FaunaDB utilizes a document-oriented data model, organizing data in hierarchical structures facilitating easy retrieval and querying.
Consistency: When it comes to consistency, Cassandra offers tunable consistency levels, allowing users to trade off between consistency and availability. FaunaDB, however, guarantees strict consistency globally across all operations ensuring that reads reflect the most recent write.
Transaction Support: Cassandra lacks comprehensive transaction support, requiring developers to implement complex application-side logic to maintain data integrity in case of multiple operations. In contrast, FaunaDB supports asset transactions out of the box, simplifying the development process and ensuring data consistency.
Query Language: Cassandra employs CQL (Cassandra Query Language) as its query language, which resembles SQL. FaunaDB utilizes its own query language, FQL (Fauna Query Language), which is specifically designed to work with its document-oriented data model, providing powerful querying capabilities.
Scalability: In terms of scalability, while both Cassandra and FaunaDB support horizontal scalability by adding more nodes to the cluster, FaunaDB offers built-in multi-region replication which simplifies data distribution and ensures low-latency access for global applications compared to Cassandra.
Consistency Model: Cassandra employs an eventually consistent model, where inconsistencies may exist temporarily during network partitions but eventually resolve. In contrast, FaunaDB uses a strong consistency model by default, ensuring that reads reflect the latest write across all operations, offering robust data integrity guarantees.

Cassandra vs FaunaDB

Overview