DBMS > GBase vs. IBM Db2 Event Store vs. Ignite vs. SAP HANA vs. XTDB

System Properties Comparison GBase vs. IBM Db2 Event Store vs. Ignite vs. SAP HANA vs. XTDB

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

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

In-memory, column based data store. Available as appliance or cloud service

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Key-value store
Relational DBMS

Relational DBMS

Document store

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

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

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

www.gbase.cn

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

ignite.apache.org

www.sap.com/products/hana.html

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

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

apacheignite.readme.io/docs

help.sap.com/hana

www.xtdb.com/docs

Developer

General Data Technology Co., Ltd.

IBM

Apache Software Foundation

SAP

Juxt Ltd.

Initial release

2004

2017

2015

2010

2019

Current release

GBase 8a, GBase 8s, GBase 8c

2.0

Apache Ignite 2.6

2.0 SPS07 (April 4, 2023), April 2023

1.19, September 2021

License

Commercial or Open Source

commercial

free developer edition available

Open Source

Apache 2.0

commercial

Open Source

MIT License

Cloud-based only

Only available as a cloud service

also available as a cloud based 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++

C++, Java, .Net

Clojure

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Linux
OS X
Solaris
Windows

Appliance or cloud-service

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

ANSI-99 for query and DML statements, subset of DDL

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

ADO.NET
C API
JDBC
ODBC

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC

HTTP REST
JDBC

Supported programming languages

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

C#
C++
Java
PHP
Python
Ruby
Scala

Clojure
Java

Server-side scripts

Stored procedures

user defined functions

yes

yes (compute grid and cache interceptors can be used instead)

SQLScript, R

Triggers

yes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Active-active shard replication

yes (replicated cache)

yes

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

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

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, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

Security Hooks for custom implementations

yes

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