DBMS > Derby vs. EsgynDB vs. IBM Db2 Event Store vs. Microsoft Azure Cosmos DB vs. Tibero

System Properties Comparison Derby vs. EsgynDB vs. IBM Db2 Event Store vs. Microsoft Azure Cosmos DB vs. Tibero

Editorial information provided by DB-Engines

Name

Description

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Distributed Event Store optimized for Internet of Things use cases

Globally distributed, horizontally scalable, multi-model database service

A secure RDBMS, designed for easy portability from Oracle

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	4.60
Rank	#70	Overall
	#38	Relational DBMS

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

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	1.59
Rank	#148	Overall
	#68	Relational DBMS

Website

db.apache.org/derby

www.esgyn.cn

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

azure.microsoft.com/services/cosmos-db

us.tmaxsoft.com/products/tibero

Technical documentation

db.apache.org/derby/manuals/index.html

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

learn.microsoft.com/azure/cosmos-db

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

Apache Software Foundation

Esgyn

IBM

Microsoft

TmaxSoft

Initial release

1997

2015

2017

2014

2003

Current release

10.17.1.0, November 2023

2.0

6, April 2015

License

Commercial or Open Source

Open Source

Apache version 2

commercial

free developer edition available

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

Java

C++, Java

C and C++

C and Assembler

Server operating systems

All OS with a Java VM

Linux

Linux, macOS, Windows for the developer addition

hosted

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

schema-free

yes

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

APIs and other access methods

JDBC

ADO.NET
JDBC
ODBC

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

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

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

Java

All languages supporting JDBC/ODBC/ADO.Net

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

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

Java Stored Procedures

yes

JavaScript

Persistent Stored Procedure (PSM)

Triggers

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication between multi datacenters

Active-active shard replication

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

Durability

Support for making data persistent

yes

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

planned for next version

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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