DBMS > Atos Standard Common Repository vs. EJDB vs. IBM Db2 Event Store vs. Linter vs. SWC-DB

System Properties Comparison Atos Standard Common Repository vs. EJDB vs. IBM Db2 Event Store vs. Linter vs. SWC-DB

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

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Distributed Event Store optimized for Internet of Things use cases

RDBMS for high security requirements

A high performance, scalable Wide Column DBMS

Primary database model

Document store
Key-value store

Document store

Event Store
Time Series DBMS

Relational DBMS

Wide column store

Secondary database models

Spatial DBMS

Time Series DBMS

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

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

Score	0.09
Rank	#346	Overall
	#152	Relational DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

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

github.com/Softmotions/ejdb

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

linter.ru

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

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

Developer

Atos Convergence Creators

Softmotions

IBM

relex.ru

Alex Kashirin

Initial release

2016

2012

2017

1990

2020

Current release

1703

2.0

0.5, April 2021

License

Commercial or Open Source

commercial

Open Source

GPLv2

commercial

free developer edition available

commercial

Open Source

GPL V3

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

C and C++

C++

Server operating systems

Linux

server-less

Linux

Linux, macOS, Windows for the developer addition

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Linux

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

schema-free

Typing

predefined data types such as float or date

optional

yes

string, integer, double, bool, date, object_id

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

yes

through the embedded Spark runtime

yes

SQL-like query language

APIs and other access methods

LDAP

in-process shared library

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

Proprietary protocol
Thrift

Supported programming languages

All languages with LDAP bindings

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
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
Perl
PHP
Python
Qt
Ruby
Tcl

C++

Server-side scripts

Stored procedures

yes

proprietary syntax with the possibility to convert from PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

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

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

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

Read/Write Locking

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

fine grained access rights according to SQL-standard

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