DBMS > Amazon Aurora vs. QuestDB vs. TimescaleDB

System Properties Comparison Amazon Aurora vs. QuestDB vs. TimescaleDB

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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

A high performance open source SQL database for time series data

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Relational DBMS

Time Series DBMS

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Relational DBMS

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

Score	7.33
Rank	#46	Overall
	#29	Relational DBMS

Score	3.12
Rank	#81	Overall
	#6	Time Series DBMS

Score	3.59
Rank	#72	Overall
	#5	Time Series DBMS

Website

aws.amazon.com/rds/aurora

questdb.io

www.timescale.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

questdb.io/docs

docs.timescale.com

Social network pages

Developer

Amazon

QuestDB Technology Inc

Timescale

Initial release

2015

2014

2017

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

2.15.0, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

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

Java (Zero-GC), C++, Rust

Server operating systems

Amazon Linux

Linux
macOS
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free via InfluxDB Line Protocol

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

SQL

Support of SQL

yes, MySQL and PostgreSQL dialects

SQL with time-series extensions

yes

full PostgreSQL SQL syntax

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

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

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

PostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust

over HTTP

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

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

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

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)

horizontal partitioning (by timestamps)

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

Full replication
Logical replication
Snapshot replication
Transactional replication

Source-replica replication with eventual consistency

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate/Strong Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID, Multi-statement

ACID for single-table writes

ACID

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

through memory mapped files

User concepts

Access control

Role based permissions
Row level security

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

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3 August 2015, Matthias Gelbmann

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Recent citations in the news

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