DBMS > LeanXcale vs. LevelDB vs. Netezza vs. Oracle Berkeley DB

System Properties Comparison LeanXcale vs. LevelDB vs. Netezza vs. Oracle Berkeley DB

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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

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Data warehouse and analytics appliance part of IBM PureSystems

Widely used in-process key-value store

Primary database model

Key-value store
Relational DBMS

Key-value store

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

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

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	8.59
Rank	#45	Overall
	#29	Relational DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Website

www.leanxcale.com

github.com/google/leveldb

www.ibm.com/products/netezza

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

Technical documentation

github.com/google/leveldb/blob/main/doc/index.md

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

Developer

LeanXcale

Google

IBM

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2015

2011

2000

1994

Current release

1.23, February 2021

18.1.40, May 2020

License

Commercial or Open Source

commercial

Open Source

BSD

commercial

Open Source

commercial license available

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++

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

Server operating systems

Illumos
Linux
OS X
Windows

Linux

included in appliance

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

yes

schema-free

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

JDBC
ODBC
OLE DB

Supported programming languages

C
Java
Scala

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

C
C++
Fortran
Java
Lua
Perl
Python
R

.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

Server-side scripts

Stored procedures

yes

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

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

with automatic compression on writes

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

Users with fine-grained authorization concept

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