DBMS > InfinityDB vs. IRONdb vs. Microsoft Azure Cosmos DB vs. Transbase

System Properties Comparison InfinityDB vs. IRONdb vs. Microsoft Azure Cosmos DB vs. Transbase

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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 Java embedded Key-Value Store which extends the Java Map interface

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

Globally distributed, horizontally scalable, multi-model database service

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

Primary database model

Key-value store

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.07
Rank	#359	Overall
	#54	Key-value stores

Score	29.85
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.15
Rank	#337	Overall
	#147	Relational DBMS

Website

boilerbay.com

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

azure.microsoft.com/services/cosmos-db

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

Technical documentation

boilerbay.com/infinitydb/manual

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

learn.microsoft.com/azure/cosmos-db

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

Developer

Boiler Bay Inc.

Circonus LLC.

Microsoft

Transaction Software GmbH

Initial release

2002

2017

2014

1987

Current release

4.0

V0.10.20, January 2018

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

free development license

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 and C++

Server operating systems

All OS with a Java VM

Linux

hosted

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

schema-free

yes

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

yes

text, numeric, histograms

yes

JSON types

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.

Secondary indexes

manual creation possible, using inversions based on multi-value capability

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

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

SQL-like query language

yes

APIs and other access methods

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Java

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

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

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

Server-side scripts

Stored procedures

yes, in Lua

JavaScript

yes

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

none

Automatic, metric affinity per node

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

none

configurable replication factor, datacenter aware

yes

Implicit feature of the cloud service

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate consistency per node, eventual consistency across nodes

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

manual creation possible, using inversions based on multi-value capability

yes

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

Multi-item ACID transactions with snapshot isolation within a partition

yes

Concurrency

Support for concurrent manipulation of data

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.

User concepts

Access control

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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