DBMS > Amazon Neptune vs. Blazegraph vs. GBase vs. KeyDB vs. SWC-DB

System Properties Comparison Amazon Neptune vs. Blazegraph vs. GBase vs. KeyDB vs. SWC-DB

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

Fast, reliable graph database built for the cloud

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

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

A high performance, scalable Wide Column DBMS

Primary database model

Secondary database models

Time Series DBMS

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

Score	2.29
Rank	#113	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	0.81
Rank	#213	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	0.70
Rank	#229	Overall
	#32	Key-value stores

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

aws.amazon.com/neptune

blazegraph.com

www.gbase.cn

github.com/Snapchat/KeyDB
keydb.dev

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

aws.amazon.com/neptune/developer-resources

wiki.blazegraph.com

docs.keydb.dev

Developer

Amazon

Blazegraph

General Data Technology Co., Ltd.

EQ Alpha Technology Ltd.

Alex Kashirin

Initial release

2017

2006

2004

2019

2020

Current release

2.1.5, March 2019

GBase 8a, GBase 8s, GBase 8c

0.5, April 2021

License

Commercial or Open Source

commercial

Open Source

extended commercial license available

commercial

Open Source

BSD-3

Open Source

GPL V3

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

C, Java, Python

C++

Server operating systems

hosted

Linux
OS X
Windows

Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

RDF literal types

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

by using the Redis Search module

SQL

Support of SQL

SPARQL is used as query language

Standard with numerous extensions

SQL-like query language

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

ADO.NET
C API
JDBC
ODBC

Proprietary protocol

RESP - REdis Serialization Protoco

Proprietary protocol
Thrift

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

C++

Server-side scripts

Stored procedures

yes

user defined functions

Lua

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency
Strong eventual consistency with CRDTs

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Relationships in Graphs

yes

Transaction concepts

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

ACID

Optimistic locking, atomic execution of commands blocks and scripts

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

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

yes

simple password-based access control and ACL

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