Cassandra vs Redis

Overview

Redis

Stacks61.9K

Followers46.5K

Votes3.9K

GitHub Stars42

Forks6

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

Cassandra vs Redis: What are the differences?

Introduction

Cassandra and Redis are both popular NoSQL databases known for their high performance and scalability. However, they have distinct differences in terms of data model, data persistence, data types, use cases, and data consistency.

Data Model: Cassandra is a wide-column store database that uses a tabular format with rows and columns to store data. It supports flexible schema design and is suitable for handling large amounts of structured and unstructured data. In contrast, Redis is a key-value store database that uses a simple key-value data model, which is optimized for quick access and data retrieval based on the key.
Data Persistence: Cassandra provides durable storage by persisting data on disk, allowing for high availability and fault tolerance. It also offers tunable consistency levels for reads and writes. On the other hand, Redis primarily stores data in memory for faster access, making it highly scalable and suitable for use cases that require low-latency data access. However, Redis also supports persistence to disk for data durability.
Data Types: Cassandra supports a wide range of data types, including primitive types (e.g., integers, boolean), collections (e.g., lists, sets, maps), and user-defined types. This flexibility facilitates complex data modeling and query operations. In contrast, Redis has a limited set of data types, including strings, lists, sets, sorted sets, and hashes. While the simplicity of these types allows for efficient storage and retrieval, it may require additional application logic for handling complex data structures.
Use Cases: Due to its scalability, fault-tolerance, and support for large data sets, Cassandra is well-suited for use cases that involve storing and processing massive amounts of data, such as big data analytics, content management systems, and time-series data. On the other hand, Redis is commonly used for caching, real-time analytics, pub/sub (publish/subscribe) messaging, and session management, where low-latency data access is crucial.
Data Consistency: Cassandra uses a tunable consistency model that provides eventual consistency by default, allowing for high availability and fault tolerance. It supports configurable consistency levels for both read and write operations. Redis, on the other hand, emphasizes data consistency and provides strong consistency guarantees in a single-node configuration. However, in a distributed setup, Redis also offers eventual consistency like Cassandra.
Scalability: Cassandra is designed for linear scalability, allowing it to handle large workloads by distributing data across multiple nodes. It employs a distributed architecture with built-in mechanisms for automatic data partitioning and replication. Redis, while also capable of horizontal scaling through clustering, is primarily optimized for vertical scaling on a single server by utilizing high-speed RAM and multi-core processors.

In summary, Cassandra excels in handling massive amounts of structured and unstructured data, providing high availability and fault tolerance, while Redis specializes in fast data access and low-latency use cases such as caching and real-time analytics. Both databases offer different trade-offs in terms of data model, persistence, data types, use cases, data consistency, and scalability.

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Advice on Redis, Cassandra

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.

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Comments

Detailed Comparison

Redis	Cassandra
Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.	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.
Statistics
GitHub Stars 42	GitHub Stars 9.5K
GitHub Forks 6	GitHub Forks 3.8K
Stacks 61.9K	Stacks 3.6K
Followers 46.5K	Followers 3.5K
Votes 3.9K	Votes 507
Pros & Cons
Pros 888 Performance 542 Super fast 514 Ease of use 444 In-memory cache 324 Advanced key-value cache Cons 15 Cannot query objects directly 3 No secondary indexes for non-numeric data types 1 No WAL	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 Redis, Cassandra?

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 Redis: What are the differences?

Introduction

Data Model: Cassandra is a wide-column store database that uses a tabular format with rows and columns to store data. It supports flexible schema design and is suitable for handling large amounts of structured and unstructured data. In contrast, Redis is a key-value store database that uses a simple key-value data model, which is optimized for quick access and data retrieval based on the key.
Data Persistence: Cassandra provides durable storage by persisting data on disk, allowing for high availability and fault tolerance. It also offers tunable consistency levels for reads and writes. On the other hand, Redis primarily stores data in memory for faster access, making it highly scalable and suitable for use cases that require low-latency data access. However, Redis also supports persistence to disk for data durability.
Data Types: Cassandra supports a wide range of data types, including primitive types (e.g., integers, boolean), collections (e.g., lists, sets, maps), and user-defined types. This flexibility facilitates complex data modeling and query operations. In contrast, Redis has a limited set of data types, including strings, lists, sets, sorted sets, and hashes. While the simplicity of these types allows for efficient storage and retrieval, it may require additional application logic for handling complex data structures.
Use Cases: Due to its scalability, fault-tolerance, and support for large data sets, Cassandra is well-suited for use cases that involve storing and processing massive amounts of data, such as big data analytics, content management systems, and time-series data. On the other hand, Redis is commonly used for caching, real-time analytics, pub/sub (publish/subscribe) messaging, and session management, where low-latency data access is crucial.
Data Consistency: Cassandra uses a tunable consistency model that provides eventual consistency by default, allowing for high availability and fault tolerance. It supports configurable consistency levels for both read and write operations. Redis, on the other hand, emphasizes data consistency and provides strong consistency guarantees in a single-node configuration. However, in a distributed setup, Redis also offers eventual consistency like Cassandra.
Scalability: Cassandra is designed for linear scalability, allowing it to handle large workloads by distributing data across multiple nodes. It employs a distributed architecture with built-in mechanisms for automatic data partitioning and replication. Redis, while also capable of horizontal scaling through clustering, is primarily optimized for vertical scaling on a single server by utilizing high-speed RAM and multi-core processors.

Cassandra vs Redis

Overview

Cassandra vs Redis: What are the differences?