DBMS > KairosDB vs. MySQL vs. Oracle Berkeley DB vs. Postgres-XL

System Properties Comparison KairosDB vs. MySQL vs. Oracle Berkeley DB vs. Postgres-XL

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Editorial information provided by DB-Engines

Name

Description

Distributed Time Series DBMS based on Cassandra or H2

Widely used open source RDBMS

Widely used in-process key-value store

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Primary database model

Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.60
Rank	#238	Overall
	#20	Time Series DBMS

Score	1029.49
Rank	#2	Overall
	#2	Relational DBMS

Score	1.88
Rank	#130	Overall
	#23	Key-value stores
	#3	Native XML DBMS

Score	0.43
Rank	#260	Overall
	#119	Relational DBMS

Website

github.com/kairosdb/kairosdb

www.mysql.com

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

www.postgres-xl.org

Technical documentation

kairosdb.github.io

dev.mysql.com/doc

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

www.postgres-xl.org/documentation

Developer

Oracle

since 2010, originally MySQL AB, then Sun

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2013

1995

1994

2014

since 2012, originally named StormDB

Current release

1.2.2, November 2018

9.0.0, July 2024

18.1.40, May 2020

10 R1, October 2018

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

commercial license available

Open Source

Mozilla public 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

C and C++

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

Server operating systems

Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
macOS

Data scheme

schema-free

yes

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

yes

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

SQL interfaced based on SQLite is available

yes

distributed, parallel query execution

APIs and other access methods

Graphite protocol
HTTP REST
Telnet API

ADO.NET
JDBC
ODBC
Proprietary native API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

Java
JavaScript

Node.js
PHP
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
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++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

yes

proprietary syntax

user defined functions

Triggers

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

not for MyISAM storage engine

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

User concepts

Access control

simple password-based access control

Users with fine-grained authorization concept

no user groups or roles

fine grained access rights according to SQL-standard

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