DBMS > LeanXcale vs. OpenQM vs. Percona Server for MongoDB vs. Solr vs. Transbase

System Properties Comparison LeanXcale vs. OpenQM vs. Percona Server for MongoDB vs. Solr vs. Transbase

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

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

A widely used distributed, scalable search engine based on Apache Lucene

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Key-value store
Relational DBMS

Secondary database models

Spatial 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	0.52
Rank	#254	Overall
	#39	Document stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

www.leanxcale.com

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

www.percona.com/mongodb/software/percona-server-for-mongodb

solr.apache.org

www.transaction.de/en/products/transbase.html

Technical documentation

docs.percona.com/percona-distribution-for-mongodb

solr.apache.org/resources.html

www.transaction.de/en/products/transbase/features.html

Developer

LeanXcale

Rocket Software, originally Martin Phillips

Percona

Apache Software Foundation

Transaction Software GmbH

Initial release

2015

1993

2015

2006

1987

Current release

3.4-12

3.4.10-2.10, November 2017

9.6.0, April 2024

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

Open Source

GPLv2, extended commercial license available

Open Source

GPL Version 2

Open Source

Apache Version 2

commercial

free development license

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++

Java

C and C++

Server operating systems

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

with some exceptions

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

yes

SQL

Support of SQL

yes

through Apache Derby

Solr Parallel SQL Interface

yes

APIs and other access methods

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

proprietary protocol using JSON

Java API
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C
Java
Scala

.Net
Basic
C
Java
Objective C
PHP
Python

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

yes

JavaScript

Java plugins

yes

Triggers

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
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

optimistic locking

yes

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

via In-Memory Engine

yes

User concepts

Access control

Access rights can be defined down to the item level

Access rights for users and roles

yes

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

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12 January 2016, Paul Andlinger

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2 July 2014, Paul Andlinger

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4 February 2013, Paul Andlinger

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