DBMS > Amazon Redshift vs. Bangdb vs. openGauss vs. Quasardb vs. TimescaleDB

System Properties Comparison Amazon Redshift vs. Bangdb vs. openGauss vs. Quasardb vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Converged and high performance database for device data, events, time series, document and graph

An enterprise-class RDBMS compatible with high-performance, high-availability and high-performance originally developed by Huawei

Distributed, high-performance timeseries database

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

Primary database model

Relational DBMS

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

Relational DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	1.06
Rank	#184	Overall
	#84	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Website

aws.amazon.com/redshift

bangdb.com

gitee.com/opengauss
opengauss.org

quasar.ai

www.timescale.com

Technical documentation

docs.aws.amazon.com/redshift

docs.bangdb.com

docs.opengauss.org/en
gitee.com/opengauss/docs

doc.quasar.ai/master

docs.timescale.com

Developer

Amazon (based on PostgreSQL)

Sachin Sinha, BangDB

Huawei and openGauss community

quasardb

Timescale

Initial release

2012

2019

2009

2017

Current release

BangDB 2.0, October 2021

3.0, March 2022

3.14.1, January 2024

2.15.0, May 2024

License

Commercial or Open Source

commercial

Open Source

BSD 3

Open Source

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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

C, C++

C, C++, Java

C++

Server operating systems

hosted

Linux

BSD
Linux
OS X
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: string, long, double, int, geospatial, stream, events

yes

integer and binary

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

restricted

yes

secondary, composite, nested, reverse, geospatial

yes

with tags

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL like support with command line tool

ANSI SQL 2011

SQL-like query language

yes

full PostgreSQL SQL syntax

APIs and other access methods

JDBC
ODBC

Proprietary protocol
RESTful HTTP API

JDBC
ODBC

HTTP API

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

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Java
Python

C
C++
Java

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

in Python

yes

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

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

horizontal partitioning (by range, list and hash)

Sharding

consistent hashing

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor, Knob for CAP (enterprise version only)

Source-replica replication

Source-replica replication with selectable replication factor

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

by using LevelDB

yes

In-memory capabilities

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

yes

yes, run db with in-memory only mode

yes

Transient mode

User concepts

Access control

fine grained access rights according to SQL-standard

yes (enterprise version only)

Access rights for users, groups and roles according to SQL-standard

Cryptographically strong user authentication and audit trail

fine grained access rights according to SQL-standard

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