DBMS > Atos Standard Common Repository vs. Citus vs. IBM Db2 Event Store vs. Stardog

System Properties Comparison Atos Standard Common Repository vs. Citus vs. IBM Db2 Event Store vs. Stardog

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Editorial information provided by DB-Engines
Name	Atos Standard Common Repository  Xexclude from comparison	Citus  Xexclude from comparison	IBM Db2 Event Store  Xexclude from comparison	Stardog  Xexclude from comparison
	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	Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL	Distributed Event Store optimized for Internet of Things use cases	Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization
Primary database model	Document store Key-value store	Relational DBMS	Event Store Time Series DBMS	Graph DBMS RDF store
Secondary database models		Document store
DB-Engines Ranking measures the popularity of database management systems Trend Chart		Score 2.15 Rank #117   Overall #56   Relational DBMS	Score 0.27 Rank #309   Overall #2   Event Stores #28   Time Series DBMS	Score 2.07 Rank #122   Overall #11   Graph DBMS #6   RDF stores
Website	atos.net/en/convergence-creators/portfolio/standard-common-repository	www.citusdata.com	www.ibm.com/­products/­db2-event-store	www.stardog.com
Technical documentation		docs.citusdata.com	www.ibm.com/­docs/­en/­db2-event-store	docs.stardog.com
Developer	Atos Convergence Creators		IBM	Stardog-Union
Initial release	2016	2010	2017	2010
Current release	1703	8.1, December 2018	2.0	7.3.0, May 2020
License Commercial or Open Source	commercial	Open Source AGPL, commercial license also available	commercial free developer edition available	commercial 60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students
Cloud-based only Only available as a cloud service	no	no	no	no
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Implementation language	Java	C	C and C++	Java
Server operating systems	Linux	Linux	Linux Linux, macOS, Windows for the developer addition	Linux macOS Windows
Data scheme	Schema and schema-less with LDAP views	yes	yes	schema-free and OWL/RDFS-schema support
Typing predefined data types such as float or date	optional	yes	yes	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	yes specific XML type available, but no XML query functionality	no	no Import/export of XML data possible
Secondary indexes	yes	yes	no	yes supports real-time indexing in full-text and geospatial
SQL Support of SQL	no	yes standard, with numerous extensions	yes through the embedded Spark runtime	Yes, compatible with all major SQL variants through dedicated BI/SQL Server
APIs and other access methods	LDAP	ADO.NET JDBC native C library ODBC streaming API for large objects	ADO.NET DB2 Connect JDBC ODBC RESTful HTTP API	GraphQL query language HTTP API Jena RDF API OWL RDF4J API Sesame REST HTTP Protocol SNARL SPARQL Spring Data Stardog Studio TinkerPop 3
Supported programming languages	All languages with LDAP bindings	.Net C C++ Delphi Java JavaScript (Node.js) Perl PHP Python Tcl	C C# C++ Cobol Delphi Fortran Go Java JavaScript (Node.js) Perl PHP Python R Ruby Scala Visual Basic	.Net Clojure Groovy Java JavaScript Python Ruby
Server-side scripts Stored procedures	no	user defined functions realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.	yes	user defined functions and aggregates, HTTP Server extensions in Java
Triggers	yes	yes	no	yes via event handlers
Partitioning methods Methods for storing different data on different nodes	Sharding cell division	Sharding	Sharding	none
Replication methods Methods for redundantly storing data on multiple nodes	yes	Source-replica replication other methods possible by using 3rd party extensions	Active-active shard replication	Multi-source replication in HA-Cluster
MapReduce Offers an API for user-defined Map/Reduce methods		no	no	no
Consistency concepts Methods to ensure consistency in a distributed system	Immediate Consistency or Eventual Consistency depending on configuration	Immediate Consistency	Eventual Consistency	Immediate Consistency in HA-Cluster
Foreign keys Referential integrity	no	yes	no	yes relationships in graphs
Transaction concepts Support to ensure data integrity after non-atomic manipulations of data	Atomic execution of specific operations	ACID	no	ACID
Concurrency Support for concurrent manipulation of data	yes	yes	No - written data is immutable	yes
Durability Support for making data persistent	yes	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	no	yes	yes
User concepts Access control	LDAP bind authentication	fine grained access rights according to SQL-standard	fine grained access rights according to SQL-standard	Access rights for users and roles
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More resources
	Atos Standard Common Repository	Citus	IBM Db2 Event Store	Stardog
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