DBMS > IBM Db2 Event Store vs. Microsoft Azure SQL Database vs. TDSQL for MySQL

System Properties Comparison IBM Db2 Event Store vs. Microsoft Azure SQL Database vs. TDSQL for MySQL

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Editorial information provided by DB-Engines

Name

Description

Distributed Event Store optimized for Internet of Things use cases

Database as a Service offering with high compatibility to Microsoft SQL Server

A high-performance distributed database management system with features such as automatic sharding, intelligent operation and maintenance, elastic scalability without downtime, and enterprise-grade security. It is highly compatible with MySQL.

Primary database model

Event Store
Time Series DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Spatial DBMS

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

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

Score	0.85
Rank	#205	Overall
	#95	Relational DBMS

Website

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

azure.microsoft.com/en-us/products/azure-sql/database

www.tencentcloud.com/products/dcdb

Technical documentation

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

docs.microsoft.com/en-us/azure/azure-sql

www.tencentcloud.com/document/product/1042

Developer

IBM

Microsoft

Tencent

Initial release

2017

2010

2013

Current release

2.0

V12

License

Commercial or Open Source

commercial

free developer edition available

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

C and C++

C++

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

hosted

Data scheme

yes

Typing

predefined data types such as float or date

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

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

JDBC
ODBC

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 (Node.js)
PHP
Python
Ruby

C
C#
C++
Java
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

Transact SQL

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

yes, with always 3 replicas available

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

No - written data is immutable

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

Users with fine-grained authorization concept

More information provided by the system vendor

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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