DBMS > OpenQM vs. SAP SQL Anywhere vs. Splice Machine vs. Transwarp Hippo

System Properties Comparison OpenQM vs. SAP SQL Anywhere vs. Splice Machine vs. Transwarp Hippo

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Editorial information provided by DB-Engines

Name

Description

QpenQM is a high-performance, self-tuning, multi-value DBMS

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

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

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Multivalue DBMS

Relational DBMS

Vector DBMS

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

Score	0.27
Rank	#298	Overall
	#10	Multivalue DBMS

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	0.00
Rank	#383	Overall
	#13	Vector DBMS

Website

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

www.sap.com/products/technology-platform/sql-anywhere.html

splicemachine.com

www.transwarp.cn/en/subproduct/hippo

Technical documentation

help.sap.com/docs/SAP_SQL_Anywhere

splicemachine.com/how-it-works

Developer

Rocket Software, originally Martin Phillips

SAP

formerly Sybase

Splice Machine

Initial release

1993

1992

2014

2023

Current release

3.4-12

17, July 2015

3.1, March 2021

1.0, May 2023

License

Commercial or Open Source

Open Source

GPLv2, extended commercial license available

commercial

Open Source

AGPL 3.0, commercial license available

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

Java

C++

Server operating systems

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

Linux
macOS

Data scheme

yes

with some exceptions

yes

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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

SQL

Support of SQL

yes

APIs and other access methods

ADO.NET
HTTP API
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

RESTful HTTP API

Supported programming languages

.Net
Basic
C
Java
Objective C
PHP
Python

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

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

C++
Java
Python

Server-side scripts

Stored procedures

yes

yes, in C/C++, Java, .Net or Perl

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Database mirroring

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

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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

Role based access control and fine grained access rights

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