DBMS > EXASOL vs. GridGain vs. IRONdb vs. Microsoft Azure Cosmos DB vs. SwayDB

System Properties Comparison EXASOL vs. GridGain vs. IRONdb vs. Microsoft Azure Cosmos DB vs. SwayDB

Editorial information provided by DB-Engines

Name

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

Description

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

GridGain is an in-memory computing platform, built on Apache Ignite

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

Globally distributed, horizontally scalable, multi-model database service

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

Secondary database models

Spatial DBMS

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

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

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

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Website

www.exasol.com

www.gridgain.com

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

azure.microsoft.com/services/cosmos-db

swaydb.simer.au

Technical documentation

www.exasol.com/resources

www.gridgain.com/docs/index.html

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

learn.microsoft.com/azure/cosmos-db

Developer

Exasol

GridGain Systems, Inc.

Circonus LLC.

Microsoft

Simer Plaha

Initial release

2000

2007

2017

2014

2018

Current release

GridGain 8.5.1

V0.10.20, January 2018

License

Commercial or Open Source

commercial

Open Source

GNU Affero GPL V3.0

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++, .Net

C and C++

Scala

Server operating systems

Linux
OS X
Solaris
Windows

Linux

hosted

Data scheme

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

yes

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

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

SQL-like query language

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

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

HTTP API

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

Supported programming languages

Java
Lua
Python
R

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

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

Java
Kotlin
Scala

Server-side scripts

Stored procedures

user defined functions

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

yes, in Lua

JavaScript

Triggers

yes

yes (cache interceptors and events)

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

configurable replication factor, datacenter aware

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

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

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate 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

Atomic execution of operations

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

Access rights for users, groups and roles according to SQL-standard

Security Hooks for custom implementations

Access rights can be defined down to the item level

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