DBMS > Bangdb vs. Blazegraph vs. Dgraph vs. Google Cloud Bigtable

System Properties Comparison Bangdb vs. Blazegraph vs. Dgraph vs. Google Cloud Bigtable

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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

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

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

Distributed and scalable native Graph DBMS

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Primary database model

Document store
Graph DBMS
Time Series DBMS

Graph DBMS
RDF store

Graph DBMS

Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	0.07
Rank	#346	Overall
	#47	Document stores
	#35	Graph DBMS
	#32	Time Series DBMS

Score	0.74
Rank	#217	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	1.39
Rank	#155	Overall
	#15	Graph DBMS

Score	2.97
Rank	#92	Overall
	#15	Key-value stores
	#8	Wide column stores

Website

bangdb.com

blazegraph.com

dgraph.io

cloud.google.com/bigtable

Technical documentation

docs.bangdb.com

wiki.blazegraph.com

dgraph.io/docs

cloud.google.com/bigtable/docs

Developer

Sachin Sinha, BangDB

Blazegraph

Dgraph Labs, Inc.

Google

Initial release

2012

2006

2016

2015

Current release

BangDB 2.0, October 2021

2.1.5, March 2019

License

Commercial or Open Source

Open Source

BSD 3

Open Source

extended commercial license available

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

Java

Server operating systems

Linux

Linux
OS X
Windows

hosted

Data scheme

schema-free

Typing

predefined data types such as float or date

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

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

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

SPARQL is used as query language

APIs and other access methods

Proprietary protocol
RESTful HTTP API

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

Supported programming languages

C
C#
C++
Java
Python

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

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

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

Server-side scripts

Stored procedures

yes

Triggers

yes, Notifications (with Streaming only)

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

Synchronous replication via Raft

Internal replication in Colossus, and regional replication between two clusters in different zones

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Foreign keys

Referential integrity

yes

Relationships in Graphs

Transaction concepts

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

ACID

Atomic single-row operations

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes, run db with in-memory only mode

User concepts

Access control

yes (enterprise version only)

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

Planned for future releases

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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More resources

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