DBMS > OpenQM vs. Sequoiadb vs. Splice Machine vs. Vertica

System Properties Comparison OpenQM vs. Sequoiadb vs. Splice Machine vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

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

NewSQL database with distributed OLTP and SQL

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

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Multivalue DBMS

Document store
Relational DBMS

Relational DBMS

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.27
Rank	#298	Overall
	#10	Multivalue DBMS

Score	0.45
Rank	#261	Overall
	#41	Document stores
	#122	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Website

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

www.sequoiadb.com

splicemachine.com

www.vertica.com

Technical documentation

www.sequoiadb.com/en/index.php?m=Files&a=index

splicemachine.com/how-it-works

vertica.com/documentation

Developer

Rocket Software, originally Martin Phillips

Sequoiadb Ltd.

Splice Machine

OpenText

previously Micro Focus and Hewlett Packard

Initial release

1993

2013

2014

2005

Current release

3.4-12

3.1, March 2021

12.0.3, January 2023

License

Commercial or Open Source

Open Source

GPLv2, extended commercial license available

Open Source

Server: AGPL; Client: Apache V2

Open Source

AGPL 3.0, commercial license available

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Java

C++

Server operating systems

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux

Linux
OS X
Solaris
Windows

Linux

Data scheme

yes

with some exceptions

schema-free

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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.

yes

Secondary indexes

yes

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

SQL-like query language

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

proprietary protocol using JSON

JDBC
Native Spark Datasource
ODBC

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

.Net
Basic
C
Java
Objective C
PHP
Python

.Net
C++
Java
PHP
Python

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

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

Server-side scripts

Stored procedures

yes

JavaScript

yes

Java

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Multi-source replication
Source-replica replication

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Document is locked during a transaction

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

simple password-based access control

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

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more

Competitive advantages

Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
» more

Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more

Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more

Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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