DBMS > IBM Db2 Event Store vs. Microsoft Azure Cosmos DB vs. MySQL vs. OrientDB

System Properties Comparison IBM Db2 Event Store vs. Microsoft Azure Cosmos DB vs. MySQL vs. OrientDB

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Editorial information provided by DB-Engines

Name

Description

Distributed Event Store optimized for Internet of Things use cases

Globally distributed, horizontally scalable, multi-model database service

Widely used open source RDBMS

Multi-model DBMS (Document, Graph, Key/Value)

Primary database model

Event Store
Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Key/Value like access via memcached API

Document store
Graph DBMS
Key-value store

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	3.25
Rank	#89	Overall
	#16	Document stores
	#6	Graph DBMS
	#13	Key-value stores

Website

www.ibm.com/products/db2-event-store

azure.microsoft.com/services/cosmos-db

www.mysql.com

orientdb.org

Technical documentation

www.ibm.com/docs/en/db2-event-store

learn.microsoft.com/azure/cosmos-db

dev.mysql.com/doc

www.orientdb.com/docs/last/index.html

Developer

IBM

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

OrientDB LTD; CallidusCloud; SAP

Initial release

2017

2014

1995

2010

Current release

2.0

8.4.0, April 2024

3.2.29, March 2024

License

Commercial or Open Source

commercial

free developer edition available

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache version 2

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C and C++

Java

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

hosted

FreeBSD
Linux
OS X
Solaris
Windows

All OS with a Java JDK (>= JDK 6)

Data scheme

yes

schema-free

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

SQL-like query language

yes

with proprietary extensions

SQL-like query language, no joins

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

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

ADO.NET
JDBC
ODBC
Proprietary native API

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

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

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

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

JavaScript

yes

proprietary syntax

Java, Javascript

Triggers

JavaScript

yes

Hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

relationship in graphs

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

No - written data is immutable

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

Users with fine-grained authorization concept

no user groups or roles

Access rights for users and roles; record level security configurable

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