DBMS > Dolt vs. GridGain vs. InterSystems IRIS vs. Microsoft Azure Cosmos DB

System Properties Comparison Dolt vs. GridGain vs. InterSystems IRIS vs. Microsoft Azure Cosmos DB

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Editorial information provided by DB-Engines

Name

Description

A MySQL compatible DBMS with Git-like versioning of data and schema

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

A containerised multi-model DBMS, interoperability and analytics data platform with wide capabilities for vertical and horizontal scalability

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Document store
Key-value store
Object oriented DBMS
Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Document store

Spatial DBMS

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

Score	0.96
Rank	#193	Overall
	#90	Relational DBMS

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

Score	4.05
Rank	#83	Overall
	#14	Document stores
	#10	Key-value stores
	#1	Object oriented DBMS
	#45	Relational DBMS

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

Website

github.com/dolthub/dolt
www.dolthub.com

www.gridgain.com

www.intersystems.com/products/intersystems-iris

azure.microsoft.com/services/cosmos-db

Technical documentation

docs.dolthub.com

www.gridgain.com/docs/index.html

docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls

learn.microsoft.com/azure/cosmos-db

Developer

DoltHub Inc

GridGain Systems, Inc.

InterSystems

Microsoft

Initial release

2018

2007

2018

2014

Current release

GridGain 8.5.1

2023.3, June 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

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

Server operating systems

Linux
macOS
Windows

Linux
OS X
Solaris
Windows

AIX
Linux
macOS
Ubuntu
Windows

hosted

Data scheme

yes

depending on used data model

schema-free

Typing

predefined data types such as float or date

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

yes

SQL-like query language

APIs and other access methods

CLI Client
HTTP REST

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

JDBC
ODBC
RESTful HTTP API

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

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
C++
Java
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

yes

currently in alpha release

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

yes

JavaScript

Triggers

yes

yes (cache interceptors and events)

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

yes (replicated cache)

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

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

Only one user is configurable, and must be specified in the config file at startup

Security Hooks for custom implementations

yes

Access rights can be defined down to the item level

More information provided by the system vendor

Specific characteristics

InterSystems IRIS is a complete cloud-first data platform which includes a multi-model...
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