DBMS > Amazon Redshift vs. HugeGraph vs. Infobright vs. SurrealDB

System Properties Comparison Amazon Redshift vs. HugeGraph vs. Infobright vs. SurrealDB

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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

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

A fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

Relational DBMS

Graph DBMS

Relational DBMS

Document store
Graph 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	1.02
Rank	#192	Overall
	#90	Relational DBMS

Score	1.02
Rank	#190	Overall
	#33	Document stores
	#18	Graph DBMS

Website

aws.amazon.com/redshift

github.com/hugegraph
hugegraph.apache.org

ignitetech.com/softwarelibrary/infobrightdb

surrealdb.com

Technical documentation

docs.aws.amazon.com/redshift

hugegraph.apache.org/docs

surrealdb.com/docs

Developer

Amazon (based on PostgreSQL)

Baidu

Ignite Technologies Inc.; formerly InfoBright Inc.

SurrealDB Ltd

Initial release

2012

2018

2005

2022

Current release

0.9

v1.5.0, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

Open Source

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

Java

Rust

Server operating systems

hosted

Linux
macOS
Unix

Linux
Windows

Linux
macOS
Windows

Data scheme

yes

schema-free

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.

Secondary indexes

restricted

yes

also supports composite index and range index

Knowledge Grid Technology used instead

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
JDBC
ODBC

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC

Groovy
Java
Python

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

user defined functions

in Python

asynchronous Gremlin script jobs

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

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

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

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Users, roles and permissions

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

yes, based on authentication and database rules

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