DBMS > OpenTSDB vs. Oracle Berkeley DB vs. OrigoDB vs. Riak KV

System Properties Comparison OpenTSDB vs. Oracle Berkeley DB vs. OrigoDB vs. Riak KV

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Editorial information provided by DB-Engines

Name

Description

Scalable Time Series DBMS based on HBase

Widely used in-process key-value store

A fully ACID in-memory object graph database

Distributed, fault tolerant key-value store

Primary database model

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Document store
Object oriented DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

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

Score	1.68
Rank	#142	Overall
	#12	Time Series DBMS

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

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#18	Object oriented DBMS

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Website

opentsdb.net

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

origodb.com

Technical documentation

opentsdb.net/docs/build/html/index.html

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

origodb.com/docs

www.tiot.jp/riak-docs/riak/kv/latest

Developer

currently maintained by Yahoo and other contributors

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Robert Friberg et al

OpenSource, formerly Basho Technologies

Initial release

2011

1994

2009

under the name LiveDB

2009

Current release

18.1.40, May 2020

3.2.0, December 2022

License

Commercial or Open Source

Open Source

LGPL

Open Source

commercial license available

Open Source

Apache version 2, commercial enterprise edition

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

Erlang

Server operating systems

Linux
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
Windows

Linux
OS X

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

numeric data for metrics, strings for tags

User defined using .NET types and collections

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

can be achieved using .NET

Secondary indexes

yes

restricted

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

APIs and other access methods

HTTP API
Telnet API

.NET Client API
HTTP API
LINQ

HTTP API
Native Erlang Interface

Supported programming languages

Erlang
Go
Java
Python
R
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

.Net

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

Erlang

Triggers

yes

only for the SQL API

yes

Domain Events

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on HBase

none

horizontal partitioning

client side managed; servers are not synchronized

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on HBase

Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

based on HBase

Eventual Consistency

Foreign keys

Referential integrity

depending on model

links between data sets can be stored

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

Write ahead log

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

Role based authorization

yes, using Riak Security

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More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

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