DBMS > Bangdb vs. Hyprcubd vs. mSQL vs. OrigoDB vs. Splice Machine

System Properties Comparison Bangdb vs. Hyprcubd vs. mSQL vs. OrigoDB vs. Splice Machine

Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

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

Serverless Time Series DBMS

mSQL (Mini SQL) is a simple and lightweight RDBMS

A fully ACID in-memory object graph database

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Document store
Graph DBMS
Time Series DBMS

Time Series DBMS

Relational DBMS

Document store
Object oriented DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	1.27
Rank	#169	Overall
	#76	Relational DBMS

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#18	Object oriented DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

bangdb.com

hyprcubd.com (offline)

hughestech.com.au/products/msql

origodb.com

splicemachine.com

Technical documentation

docs.bangdb.com

origodb.com/docs

splicemachine.com/how-it-works

Developer

Sachin Sinha, BangDB

Hyprcubd, Inc.

Hughes Technologies

Robert Friberg et al

Splice Machine

Initial release

2012

1994

2009

under the name LiveDB

2014

Current release

BangDB 2.0, October 2021

4.4, October 2021

3.1, March 2021

License

Commercial or Open Source

Open Source

BSD 3

commercial

free licenses can be provided

Open Source

AGPL 3.0, commercial license available

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

C, C++

Java

Server operating systems

Linux

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux
Windows

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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

yes

time, int, uint, float, string

yes

User defined using .NET types and collections

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.

can be achieved using .NET

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

SQL-like query language

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

gRPC (https)

JDBC
ODBC

.NET Client API
HTTP API
LINQ

JDBC
Native Spark Datasource
ODBC

Supported programming languages

C
C#
C++
Java
Python

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

yes

Java

Triggers

yes, Notifications (with Streaming only)

yes

Domain Events

yes

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

none

horizontal partitioning

client side managed; servers are not synchronized

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

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

none

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Eventual Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

depending on model

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

Write ahead log

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)

token access

Role based authorization

Access rights for users, groups and roles according to SQL-standard

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