DBMS > Atos Standard Common Repository vs. GigaSpaces vs. IBM Db2 Event Store vs. Prometheus

System Properties Comparison Atos Standard Common Repository vs. GigaSpaces vs. IBM Db2 Event Store vs. Prometheus

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Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Distributed Event Store optimized for Internet of Things use cases

Open-source Time Series DBMS and monitoring system

Primary database model

Document store
Key-value store

Document store
Object oriented DBMS

Values are user defined objects

Event Store
Time Series DBMS

Time Series DBMS

Secondary database models

Graph DBMS
Search engine

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

Score	1.02
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	0.23
Rank	#316	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	7.92
Rank	#51	Overall
	#2	Time Series DBMS

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

www.gigaspaces.com

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

prometheus.io

Technical documentation

docs.gigaspaces.com/latest/landing.html

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

prometheus.io/docs

Developer

Atos Convergence Creators

Gigaspaces Technologies

IBM

Initial release

2016

2000

2017

2015

Current release

1703

15.5, September 2020

2.0

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; Commercial licenses available

commercial

free developer edition available

Open Source

Apache 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

Java

Java, C++, .Net

C and C++

Server operating systems

Linux

Linux
macOS
Solaris
Windows

Linux

Linux, macOS, Windows for the developer addition

Linux
Windows

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

Typing

predefined data types such as float or date

optional

yes

Numeric data only

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

XML can be used for describing objects metadata

Import of XML data possible

Secondary indexes

yes

SQL

Support of SQL

SQL-99 for query and DML statements

yes

through the embedded Spark runtime

APIs and other access methods

LDAP

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

Supported programming languages

All languages with LDAP bindings

.Net
C++
Java
Python
Scala

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

Server-side scripts

Stored procedures

yes

Triggers

yes

yes, event driven architecture

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Active-active shard replication

yes

by Federation

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Eventual Consistency

none

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of specific operations

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

User concepts

Access control

LDAP bind authentication

Role-based access control

fine grained access rights according to SQL-standard

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