DBMS > Ignite vs. IRONdb vs. Linter vs. Microsoft Azure Cosmos DB

System Properties Comparison Ignite vs. IRONdb vs. Linter vs. Microsoft Azure Cosmos DB

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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

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

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

RDBMS for high security requirements

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	0.09
Rank	#346	Overall
	#152	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

ignite.apache.org

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

linter.ru

azure.microsoft.com/services/cosmos-db

Technical documentation

apacheignite.readme.io/docs

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

learn.microsoft.com/azure/cosmos-db

Developer

Apache Software Foundation

Circonus LLC.

relex.ru

Microsoft

Initial release

2015

2017

1990

2014

Current release

Apache Ignite 2.6

V0.10.20, January 2018

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++, Java, .Net

C and C++

Server operating systems

Linux
OS X
Solaris
Windows

Linux

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

hosted

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

JSON types

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

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

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

yes

SQL-like query language

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

HTTP API

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

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

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

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

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

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

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

yes, in Lua

yes

proprietary syntax with the possibility to convert from PL/SQL

JavaScript

Triggers

yes (cache interceptors and events)

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

configurable replication factor, datacenter aware

Source-replica replication

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

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.

yes

User concepts

Access control

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

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