DBMS > Cachelot.io vs. GBase vs. IBM Db2 Event Store vs. Transbase

System Properties Comparison Cachelot.io vs. GBase vs. IBM Db2 Event Store vs. Transbase

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Editorial information provided by DB-Engines

Name

Description

In-memory caching system

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Distributed Event Store optimized for Internet of Things use cases

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Key-value store

Relational DBMS

Event Store
Time Series DBMS

Relational DBMS

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

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

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

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

cachelot.io

www.gbase.cn

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

www.transaction.de/en/products/transbase.html

Technical documentation

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

www.transaction.de/en/products/transbase/features.html

Developer

General Data Technology Co., Ltd.

IBM

Transaction Software GmbH

Initial release

2015

2004

2017

1987

Current release

GBase 8a, GBase 8s, GBase 8c

2.0

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Simplified BSD License

commercial

free developer edition available

commercial

free development license

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

C, Java, Python

C and C++

Server operating systems

FreeBSD
Linux
OS X

Linux

Linux, macOS, Windows for the developer addition

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

schema-free

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

Standard with numerous extensions

yes

through the embedded Spark runtime

yes

APIs and other access methods

Memcached protocol

ADO.NET
C API
JDBC
ODBC

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

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

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Active-active shard replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

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

User concepts

Access control

yes

fine grained access rights according to SQL-standard

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