DBMS > Amazon Aurora vs. Apache Cassandra vs. GreptimeDB

System Properties Comparison Amazon Aurora vs. Apache Cassandra vs. GreptimeDB

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Editorial information provided by DB-Engines

Name

Description

Fully managed relational database service built for the cloud providing unparalleled high performance and availability at global scale for MySQL, PostgreSQL, and DSQL

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

An open source Time Series DBMS built for increased scalability, high performance and efficiency

Primary database model

Relational DBMS

Wide column store

Time Series DBMS

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Vector DBMS

starting with release V5

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	7.99
Rank	#44	Overall
	#28	Relational DBMS

Score	106.65
Rank	#11	Overall
	#1	Wide column stores

Score	0.13
Rank	#336	Overall
	#29	Time Series DBMS

Website

aws.amazon.com/rds/aurora

cassandra.apache.org

greptime.com

Technical documentation

docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_AuroraOverview.html
docs.aws.amazon.com/aurora-dsql/latest/userguide/what-is-aurora-dsql.html

cassandra.apache.org/doc/latest

docs.greptime.com

Social network pages

Developer

Amazon

Apache Software Foundation

Apache top level project, originally developped by Facebook

Greptime Inc.

Initial release

2015

2008

2022

Current release

Aurora DSQL, November 2024 (preview), PostgreSQL: docs.aws.amazon.com/AmazonRDS/latest/AuroraPostgreSQLReleaseNotes/AuroraPostgreSQL.Updates.html , MySQL: docs.aws.amazon.com/AmazonRDS/latest/AuroraMySQLReleaseNotes/AuroraMySQL.release-calendars.html

5.0-rc1, July 2024

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

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Database as a Service

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Implementation language

PostgreSQL/MySQL: C, C++, Aurora DSQL: Rust

Java

Rust

Server operating systems

Amazon Linux

BSD
Linux
OS X
Windows

Android
Docker
FreeBSD
Linux
macOS
Windows

Data scheme

yes

schema-free

schema-free, schema definition possible

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

restricted

only equality queries, not always the best performing solution

yes

SQL

Support of SQL

yes, MySQL and PostgreSQL dialects

SQL-like SELECT, DML and DDL statements (CQL)

yes

APIs and other access methods

DSQL API: docs.aws.amazon.com/aurora-dsql/latest/userguide/CHAP_api_reference.html
MySQL wire protocol
PostgreSQL wire protocol
RDS Data API: docs.aws.amazon.com/AmazonRDS/latest/UserGuide/ProgrammingGuide.html

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

gRPC
HTTP API
JDBC

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Rust
Scheme
Tcl

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

C++
Erlang
Go
Java
JavaScript

Server-side scripts

Stored procedures

PL/pgSQL, MySQL stored program language, APG trusted language extensions

Python

Triggers

Aurora MySQL: Yes. Aurora PostgreSQL: Yes. Aurora DSQL: No.

yes

Partitioning methods

Methods for storing different data on different nodes

DBMS table partitioning, Horizontal storage partitioning, Sharding, Distributed horizontal partitioning (DSQL)

Sharding

no "single point of failure"

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Full replication
Logical replication
Snapshot replication
Transactional replication

selectable replication factor

Representation of geographical distribution of servers is possible

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate/Strong Consistency

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID, Multi-statement

Atomicity and isolation are supported for single operations

Concurrency

Support for concurrent manipulation of data

Multiversion concurrency control (MVCC) with fully compatible MySQL and PostgreSQL options. Optimistic concurrency control (OCC) in Aurora DSQL.

yes

Durability

Support for making data persistent

Data is replicated across 3 Availability Zones (AZs) within a single AWS Region. Active-active multi-Region high availability across 3 Regions (2 active, 1 witness) and automated failover recovery with Aurora DSQL. Automated, continuous backup of data with point in time restore. Fault-tolerant, self-healing storage system.

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Role based permissions
Row level security

Access rights for users can be defined per object

Simple rights management via user accounts

More information provided by the system vendor

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