DBMS > Elasticsearch vs. IRONdb vs. OrigoDB vs. Riak TS

System Properties Comparison Elasticsearch vs. IRONdb vs. OrigoDB vs. Riak TS

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Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

A fully ACID in-memory object graph database

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Search engine

Time Series DBMS

Document store
Object oriented DBMS

Time Series DBMS

Secondary database models

Document store
Spatial DBMS
Vector DBMS

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

Score	135.35
Rank	#7	Overall
	#1	Search engines

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Website

www.elastic.co/elasticsearch

www.circonus.com/solutions/time-series-database/

origodb.com

Technical documentation

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

docs.circonus.com/irondb/category/getting-started

origodb.com/docs

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

Developer

Elastic

Circonus LLC.

Robert Friberg et al

Open Source, formerly Basho Technologies

Initial release

2010

2017

2009

under the name LiveDB

2015

Current release

8.6, January 2023

V0.10.20, January 2018

3.0.0, September 2022

License

Commercial or Open Source

Open Source

Elastic License

commercial

Open Source

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++

Erlang

Server operating systems

All OS with a Java VM

Linux

Linux
Windows

Linux
OS X

Data scheme

schema-free

Flexible type definitions. Once a type is defined, it is persistent

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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.

can be achieved using .NET

Secondary indexes

yes

All search fields are automatically indexed

yes

restricted

SQL

Support of SQL

SQL-like query language

SQL-like query language (Circonus Analytics Query Language: CAQL)

yes, limited

APIs and other access methods

Java API
RESTful HTTP/JSON API

HTTP API

.NET Client API
HTTP API
LINQ

HTTP API
Native Erlang Interface

Supported programming languages

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

.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

yes, in Lua

yes

Erlang

Triggers

yes

by using the 'percolation' feature

yes

Domain Events

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

horizontal partitioning

client side managed; servers are not synchronized

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

configurable replication factor, datacenter aware

Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Immediate consistency per node, eventual consistency across nodes

Eventual Consistency

Foreign keys

Referential integrity

depending on model

links between datasets 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.

Memcached and Redis integration

yes

User concepts

Access control

Role based authorization

More information provided by the system vendor

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More resources

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2 March 2016, Paul Andlinger

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