DBMS > GBase vs. IBM Db2 Event Store vs. Prometheus vs. Tigris

System Properties Comparison GBase vs. IBM Db2 Event Store vs. Prometheus vs. Tigris

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Editorial information provided by DB-Engines

Name

Description

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

Open-source Time Series DBMS and monitoring system

A horizontally scalable, ACID transactional, document database available both as a fully managed cloud service and for deployment on self-managed infrastructure

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Time Series DBMS

Document store
Key-value store
Search engine
Time Series DBMS

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

Score	1.05
Rank	#186	Overall
	#86	Relational DBMS

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

Score	7.69
Rank	#50	Overall
	#3	Time Series DBMS

Score	0.09
Rank	#363	Overall
	#49	Document stores
	#54	Key-value stores
	#22	Search engines
	#38	Time Series DBMS

Website

www.gbase.cn

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

prometheus.io

www.tigrisdata.com

Technical documentation

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

prometheus.io/docs

www.tigrisdata.com/docs

Developer

General Data Technology Co., Ltd.

IBM

Tigris Data, Inc.

Initial release

2004

2017

2015

2022

Current release

GBase 8a, GBase 8s, GBase 8c

2.0

License

Commercial or Open Source

commercial

free developer edition available

Open Source

Apache 2.0

Open Source

Apache Version 2.0

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, Java, Python

C and C++

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Linux
Windows

Linux
macOS
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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

Import of XML data possible

Secondary indexes

yes

SQL

Support of SQL

Standard with numerous extensions

yes

through the embedded Spark runtime

APIs and other access methods

ADO.NET
C API
JDBC
ODBC

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

CLI Client
gRPC
RESTful HTTP 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++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Go
Java
JavaScript (Node.js)

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Active-active shard replication

yes

by Federation

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

none

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

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

yes, using FoundationDB

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

Access rights for users and roles

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