DBMS > Apache Kylin vs. Lovefield vs. OpenMLDB vs. Oracle Berkeley DB vs. Transbase

System Properties Comparison Apache Kylin vs. Lovefield vs. OpenMLDB vs. Oracle Berkeley DB vs. Transbase

Editorial information provided by DB-Engines

Name

Description

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

Embeddable relational database for web apps written in pure JavaScript

An open-source machine learning database that provides a feature platform for training and inference

Widely used in-process key-value store

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

Primary database model

Relational DBMS

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

Secondary database models

Relational DBMS

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

Score	1.25
Rank	#170	Overall
	#77	Relational DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	0.10
Rank	#359	Overall
	#36	Time Series DBMS

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

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

kylin.apache.org

google.github.io/lovefield

openmldb.ai

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

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

Technical documentation

kylin.apache.org/docs

github.com/google/lovefield/blob/master/docs/spec_index.md

openmldb.ai/docs/zh/main

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

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

Developer

Apache Software Foundation, originally contributed from eBay Inc

Google

4 Paradigm Inc.

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Transaction Software GmbH

Initial release

2015

2014

2020

1994

1987

Current release

3.1.0, July 2020

2.1.12, February 2017

2024-2 February 2024

18.1.40, May 2020

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

commercial license available

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

Java

JavaScript

C++, Java, Scala

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

C and C++

Server operating systems

Linux

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

Fixed schema

schema-free

yes

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

ANSI SQL for queries (using Apache Calcite)

SQL-like query language

via JavaScript builder pattern

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
SQLAlchemy

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

JavaScript

C++
Go
Java
Python
Scala

.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

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

Server-side scripts

Stored procedures

yes

Triggers

Using read-only observers

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

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

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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