DBMS > LeanXcale vs. OpenQM vs. SpatiaLite vs. Vertica

System Properties Comparison LeanXcale vs. OpenQM vs. SpatiaLite vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

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

Spatial extension of SQLite

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

Key-value store
Relational DBMS

Multivalue DBMS

Spatial DBMS

Relational DBMS

Column oriented

Secondary database models

Relational DBMS

Spatial DBMS
Time Series DBMS

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

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Score	0.27
Rank	#298	Overall
	#10	Multivalue DBMS

Score	1.60
Rank	#149	Overall
	#3	Spatial DBMS

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Website

www.leanxcale.com

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

www.gaia-gis.it/fossil/libspatialite/index

www.vertica.com

Technical documentation

www.gaia-gis.it/gaia-sins/spatialite_topics.html

vertica.com/documentation

Developer

LeanXcale

Rocket Software, originally Martin Phillips

Alessandro Furieri

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2015

1993

2008

2005

Current release

3.4-12

5.0.0, August 2020

12.0.3, January 2023

License

Commercial or Open Source

commercial

Open Source

GPLv2, extended commercial license available

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

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++

Server operating systems

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

server-less

Linux

Data scheme

yes

with some exceptions

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

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

yes

through Apache Derby

yes

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

APIs and other access methods

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

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

character-based, interactive, front-end utility

Supported programming languages

C
Java
Scala

.Net
Basic
C
Java
Objective C
PHP
Python

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

Server-side scripts

Stored procedures

yes

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

yes

none

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

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

Bi-directional 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

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; 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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