DBMS > Amazon Redshift vs. Blazegraph vs. Dolt vs. HugeGraph vs. VictoriaMetrics

System Properties Comparison Amazon Redshift vs. Blazegraph vs. Dolt vs. HugeGraph vs. VictoriaMetrics

Editorial information provided by DB-Engines

Name

Amazon has acquired Blazegraph's domain and (probably) product. It is said that Amazon Neptune is based on Blazegraph.

Description

Large scale data warehouse service for use with business intelligence tools

High-performance graph database supporting Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop3, blueprints, vertex-centric) APIs with scale-out and High Availability.

A MySQL compatible DBMS with Git-like versioning of data and schema

A fast-speed and highly-scalable Graph DBMS

A fast, cost-effective and scalable Time Series DBMS and monitoring solution

Primary database model

Secondary database models

Document store

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.75
Rank	#219	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	0.96
Rank	#193	Overall
	#90	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	1.32
Rank	#162	Overall
	#14	Time Series DBMS

Website

aws.amazon.com/redshift

blazegraph.com

github.com/dolthub/dolt
www.dolthub.com

github.com/hugegraph
hugegraph.apache.org

victoriametrics.com

Technical documentation

docs.aws.amazon.com/redshift

wiki.blazegraph.com

docs.dolthub.com

hugegraph.apache.org/docs

docs.victoriametrics.com
github.com/VictoriaMetrics/VictoriaMetrics/wiki

Developer

Amazon (based on PostgreSQL)

Blazegraph

DoltHub Inc

Baidu

VictoriaMetrics

Initial release

2012

2006

2018

Current release

2.1.5, March 2019

0.9

v1.91, May 2023

License

Commercial or Open Source

commercial

Open Source

extended commercial license available

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

hosted

Linux
OS X
Windows

Linux
macOS
Windows

Linux
macOS
Unix

FreeBSD
Linux
macOS
OpenBSD

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

RDF literal types

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

SQL

Support of SQL

yes

does not fully support an SQL-standard

SPARQL is used as query language

yes

APIs and other access methods

JDBC
ODBC

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

CLI Client
HTTP REST

Java API
RESTful HTTP API
TinkerPop Gremlin

Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

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

Groovy
Java
Python

Server-side scripts

Stored procedures

user defined functions

in Python

yes

currently in alpha release

asynchronous Gremlin script jobs

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

yes

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

Synchronous 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

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Relationships in Graphs

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

Security and Authentication via Web Application Container (Tomcat, Jetty)

Only one user is configurable, and must be specified in the config file at startup

Users, roles and permissions

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