DBMS > LeanXcale vs. Linter vs. Microsoft Azure Table Storage vs. Oracle Berkeley DB vs. Percona Server for MongoDB

System Properties Comparison LeanXcale vs. Linter vs. Microsoft Azure Table Storage vs. Oracle Berkeley DB vs. Percona Server for MongoDB

Editorial information provided by DB-Engines

Name

Description

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

RDBMS for high security requirements

A Wide Column Store for rapid development using massive semi-structured datasets

Widely used in-process key-value store

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

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Wide column store

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Document store

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	0.09
Rank	#346	Overall
	#152	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	0.52
Rank	#254	Overall
	#39	Document stores

Website

www.leanxcale.com

linter.ru

azure.microsoft.com/en-us/services/storage/tables

www.oracle.com/database/technologies/related/berkeleydb.html

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

Technical documentation

docs.oracle.com/cd/E17076_05/html/index.html

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

Developer

LeanXcale

relex.ru

Microsoft

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Percona

Initial release

2015

1990

2012

1994

2015

Current release

18.1.40, May 2020

3.4.10-2.10, November 2017

License

Commercial or Open Source

commercial

Open Source

commercial license available

Open Source

GPL Version 2

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 and C++

C, Java, C++ (depending on the Berkeley DB edition)

C++

Server operating systems

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

hosted

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux

Data scheme

yes

schema-free

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

yes

through Apache Derby

yes

SQL interfaced based on SQLite is available

APIs and other access methods

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

RESTful HTTP API

proprietary protocol using JSON

Supported programming languages

C
Java
Scala

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

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

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

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

Server-side scripts

Stored procedures

yes

proprietary syntax with the possibility to convert from PL/SQL

JavaScript

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

optimistic locking

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

via In-Memory Engine

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights based on private key authentication or shared access signatures

Access rights for users and roles

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