Amazon RDS for Aurora vs Cassandra

Overview

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

Amazon Aurora

Stacks807

Followers745

Votes55

Amazon RDS for Aurora vs Cassandra: What are the differences?

Introduction:

In this comparison, we will highlight the key differences between Amazon RDS for Aurora and Cassandra. Both databases are used for different purposes and have distinct features that set them apart. Below are the unique characteristics of each database.

1. Performance and Scalability:

Amazon RDS for Aurora is a relational database engine built for the cloud that offers high performance and scalability. It is designed to handle heavy workloads efficiently, utilizing a distributed architecture that allows for automatic scaling of storage and compute resources. On the other hand, Cassandra is a highly scalable and distributed NoSQL database optimized for write-heavy workloads, making it ideal for applications requiring high throughput and low latency.

2. Data Model:

Amazon RDS for Aurora supports the traditional relational data model and offers compatibility with MySQL and PostgreSQL. It provides ACID-compliant transactions and supports advanced SQL features. Cassandra, on the other hand, follows a distributed key-value data model, where data is organized into tables with a flexible schema. It does not support joins or ACID transactions but offers tunable consistency levels and automatic partitioning.

3. Architecture:

Amazon RDS for Aurora uses a cluster of replicated storage volumes for data durability and high availability. It employs a master-slave replication model with automated failover capabilities. Cassandra, on the other hand, employs a decentralized architecture where data is distributed across a cluster of nodes. It uses a peer-to-peer replication model with no single point of failure, providing high availability and fault tolerance.

4. Data Replication:

In Amazon RDS for Aurora, data replication is handled automatically through multiple redundant copies of data across different availability zones. It provides quick and automated failover to the standby replicas in the event of a failure. Cassandra, on the other hand, uses a peer-to-peer replication mechanism utilizing consistent hashing. It allows for custom replication strategies and replication factor, giving more control over data replication.

5. Consistency Model:

Amazon RDS for Aurora provides strong consistency with support for ACID transactions. It ensures that all reads see the most recent committed data. Cassandra, on the other hand, provides tunable consistency allowing developers to choose between strong consistency, eventual consistency, or a combination of both. This flexibility allows for optimizations in terms of latency and availability.

6. Query Language:

Amazon RDS for Aurora supports the SQL query language, making it easier for developers with existing SQL knowledge to work with. It can leverage the power of advanced SQL features like joins, subqueries, and complex aggregations. In contrast, Cassandra uses the CQL (Cassandra Query Language) which is similar to SQL but differs in some aspects. It does not support joins or complex aggregations, focusing more on simple key-value lookups and denormalized data models.

In Summary, Amazon RDS for Aurora is a high-performance relational database engine with ACID compliance and SQL support, while Cassandra is a highly scalable NoSQL database optimized for write-heavy workloads with a distributed key-value data model and tunable consistency.

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Advice on Cassandra, Amazon Aurora

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

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Detailed Comparison

Cassandra	Amazon Aurora
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.	Amazon Aurora is a MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides up to five times better performance than MySQL at a price point one tenth that of a commercial database while delivering similar performance and availability.
-	High Throughput with Low Jitter;Push-button Compute Scaling;Storage Auto-scaling;Amazon Aurora Replicas;Instance Monitoring and Repair;Fault-tolerant and Self-healing Storage;Automatic, Continuous, Incremental Backups and Point-in-time Restore;Database Snapshots;Resource-level Permissions;Easy Migration;Monitoring and Metrics
Statistics
GitHub Stars 9.5K	GitHub Stars -
GitHub Forks 3.8K	GitHub Forks -
Stacks 3.6K	Stacks 807
Followers 3.5K	Followers 745
Votes 507	Votes 55
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 14 MySQL compatibility 12 Better performance 10 Easy read scalability 9 Speed 7 Low latency read replica Cons 2 Vendor locking 1 Rigid schema
Integrations
No integrations available	PostgreSQL MySQL

What are some alternatives to Cassandra, Amazon Aurora?

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.

Amazon RDS

Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.

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.

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