DBMS > LMDB vs. Oracle Berkeley DB vs. Quasardb

System Properties Comparison LMDB vs. Oracle Berkeley DB vs. Quasardb

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Editorial information provided by DB-Engines

Name

Description

A high performant, light-weight, embedded key-value database library

Widely used in-process key-value store

Distributed, high-performance timeseries database

Primary database model

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Time Series DBMS

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

Score	1.99
Rank	#125	Overall
	#21	Key-value stores

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

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

www.symas.com/symas-embedded-database-lmdb

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

quasar.ai

Technical documentation

www.lmdb.tech/doc

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

doc.quasar.ai/master

Developer

Symas

Oracle

originally developed by Sleepycat, which was acquired by Oracle

quasardb

Initial release

2011

1994

2009

Current release

0.9.32, January 2024

18.1.40, May 2020

3.14.1, January 2024

License

Commercial or Open Source

Open Source

commercial license available

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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++ (depending on the Berkeley DB edition)

C++

Server operating systems

Linux
Unix
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

BSD
Linux
OS X
Windows

Data scheme

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

SQL-like query language

APIs and other access methods

HTTP API

Supported programming languages

.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl

.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

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

Server-side scripts

Stored procedures

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

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

by using LevelDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

User concepts

Access control

Cryptographically strong user authentication and audit trail

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