DBMS > Bangdb vs. HugeGraph vs. Sqrrl vs. TerminusDB vs. VelocityDB

System Properties Comparison Bangdb vs. HugeGraph vs. Sqrrl vs. TerminusDB vs. VelocityDB

Editorial information provided by DB-Engines

Name

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

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

A fast-speed and highly-scalable Graph DBMS

Adaptable, secure NoSQL built on Apache Accumulo

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Document store
Graph DBMS
Time Series DBMS

Graph DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Graph DBMS

Graph DBMS
Object oriented DBMS

Secondary database models

Spatial DBMS

Document store
RDF store

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

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Score	0.05
Rank	#358	Overall
	#36	Graph DBMS
	#16	Object oriented DBMS

Website

bangdb.com

github.com/hugegraph
hugegraph.apache.org

sqrrl.com

terminusdb.com

velocitydb.com

Technical documentation

docs.bangdb.com

hugegraph.apache.org/docs

terminusdb.github.io/terminusdb/#

velocitydb.com/UserGuide

Developer

Sachin Sinha, BangDB

Baidu

Amazon

originally Sqrrl Data, Inc.

DataChemist Ltd.

VelocityDB Inc

Initial release

2012

2018

2012

2018

2011

Current release

BangDB 2.0, October 2021

0.9

11.0.0, January 2023

7.x

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache Version 2.0

commercial

Open Source

GPL V3

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C, C++

Java

Prolog, Rust

Server operating systems

Linux

Linux
macOS
Unix

Linux

Any that supports .NET

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

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

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

also supports composite index and range index

yes

SQL

Support of SQL

SQL like support with command line tool

SQL-like query language (WOQL)

APIs and other access methods

Proprietary protocol
RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

.Net

Supported programming languages

C
C#
C++
Java
Python

Groovy
Java
Python

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

JavaScript
Python

.Net

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes

Triggers

yes, Notifications (with Streaming only)

yes

Callbacks are triggered when data changes

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

yes

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

Sharding

making use of Hadoop

Graph Partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

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

selectable replication factor

making use of Hadoop

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Eventual Consistency

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

ACID

Atomic updates per row, document, or graph entity

ACID

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 journaling

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

yes

User concepts

Access control

yes (enterprise version only)

Users, roles and permissions

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

Role-based access control

Based on Windows Authentication

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