DBMS > Amazon Redshift vs. HugeGraph vs. Quasardb vs. SAP HANA vs. TimescaleDB

System Properties Comparison Amazon Redshift vs. HugeGraph vs. Quasardb vs. SAP HANA vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

A fast-speed and highly-scalable Graph DBMS

Distributed, high-performance timeseries database

In-memory, column based data store. Available as appliance or cloud service

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

Primary database model

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
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	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Website

aws.amazon.com/redshift

github.com/hugegraph
hugegraph.apache.org

quasar.ai

www.sap.com/products/hana.html

www.timescale.com

Technical documentation

docs.aws.amazon.com/redshift

hugegraph.apache.org/docs

doc.quasar.ai/master

help.sap.com/hana

docs.timescale.com

Developer

Amazon (based on PostgreSQL)

Baidu

quasardb

SAP

Timescale

Initial release

2012

2018

2009

2010

2017

Current release

0.9

3.14.1, January 2024

2.0 SPS07 (April 4, 2023), April 2023

2.15.0, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

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

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

Server operating systems

hosted

Linux
macOS
Unix

BSD
Linux
OS X
Windows

Appliance or cloud-service

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

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

restricted

yes

also supports composite index and range index

yes

with tags

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

yes

full PostgreSQL SQL syntax

APIs and other access methods

JDBC
ODBC

Java API
RESTful HTTP API
TinkerPop Gremlin

HTTP API

JDBC
ODBC

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

Supported programming languages

All languages supporting JDBC/ODBC

Groovy
Java
Python

.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

asynchronous Gremlin script jobs

SQLScript, R

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

consistent hashing

yes

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

Source-replica replication with selectable replication factor

yes

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

edges in graph

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

Transient mode

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Users, roles and permissions

Cryptographically strong user authentication and audit trail

yes

fine grained access rights according to SQL-standard

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