DBMS > Axibase vs. Brytlyt vs. IBM Cloudant vs. IRONdb vs. RDF4J

System Properties Comparison Axibase vs. Brytlyt vs. IBM Cloudant vs. IRONdb vs. RDF4J

Editorial information provided by DB-Engines

Name

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

Description

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

Database as a Service offering based on Apache CouchDB

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

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

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

Score	0.29
Rank	#292	Overall
	#25	Time Series DBMS

Score	0.29
Rank	#288	Overall
	#131	Relational DBMS

Score	2.68
Rank	#106	Overall
	#20	Document stores

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

axibase.com/docs/atsd/finance

brytlyt.io

www.ibm.com/products/cloudant

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

rdf4j.org

Technical documentation

docs.brytlyt.io

cloud.ibm.com/docs/Cloudant

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

rdf4j.org/documentation

Developer

Axibase Corporation

Brytlyt

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Circonus LLC.

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2013

2016

2010

2017

2004

Current release

15585

5.0, August 2023

V0.10.20, January 2018

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C, C++ and CUDA

Erlang

C and C++

Java

Server operating systems

Linux

Linux
OS X
Windows

hosted

Linux

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

yes

text, numeric, histograms

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

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

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

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

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

RESTful HTTP/JSON API

HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

Go
Java
PHP
Python
R
Ruby

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

C#
Java
JavaScript
Objective-C
PHP
Ruby

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

Java
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions

in PL/pgSQL

View functions (Map-Reduce) in JavaScript

yes, in Lua

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication
Source-replica replication

configurable replication factor, datacenter aware

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

atomic operations within a document possible

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

In-memory capabilities

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

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users can be defined per database

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