DBMS > Atos Standard Common Repository vs. IRONdb vs. Oracle Berkeley DB vs. Postgres-XL

System Properties Comparison Atos Standard Common Repository vs. IRONdb vs. Oracle Berkeley DB vs. Postgres-XL

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Editorial information provided by DB-Engines
Name	Atos Standard Common Repository  Xexclude from comparison	IRONdb  Xexclude from comparison	Oracle Berkeley DB  Xexclude from comparison	Postgres-XL  Xexclude from comparison
	This system has been discontinued and will be removed from the DB-Engines ranking.	IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
Description	Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks	A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity	Widely used in-process key-value store	Based on PostgreSQL enhanced with MPP and write-scale-out cluster features
Primary database model	Document store Key-value store	Time Series DBMS	Key-value store supports sorted and unsorted key sets Native XML DBMS in the Oracle Berkeley DB XML version	Relational DBMS
Secondary database models				Document store Spatial DBMS
DB-Engines Ranking measures the popularity of database management systems Trend Chart			Score 2.01 Rank #126   Overall #21   Key-value stores #3   Native XML DBMS	Score 0.53 Rank #254   Overall #117   Relational DBMS
Website	atos.net/en/convergence-creators/portfolio/standard-common-repository	www.circonus.com/solutions/time-series-database/	www.oracle.com/­database/­technologies/­related/­berkeleydb.html	www.postgres-xl.org
Technical documentation		docs.circonus.com/irondb/category/getting-started	docs.oracle.com/­cd/­E17076_05/­html/­index.html	www.postgres-xl.org/­documentation
Developer	Atos Convergence Creators	Circonus LLC.	Oracle originally developed by Sleepycat, which was acquired by Oracle
Initial release	2016	2017	1994	2014 since 2012, originally named StormDB
Current release	1703	V0.10.20, January 2018	18.1.40, May 2020	10 R1, October 2018
License Commercial or Open Source	commercial	commercial	Open Source commercial license available	Open Source Mozilla public license
Cloud-based only Only available as a cloud service	no	no	no	no
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, Java, C++ (depending on the Berkeley DB edition)	C
Server operating systems	Linux	Linux	AIX Android FreeBSD iOS Linux OS X Solaris VxWorks Windows	Linux macOS
Data scheme	Schema and schema-less with LDAP views	schema-free	schema-free	yes
Typing predefined data types such as float or date	optional	yes text, numeric, histograms	no	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	no	yes only with the Berkeley DB XML edition	yes XML type, but no XML query functionality
Secondary indexes	yes	no	yes	yes
SQL Support of SQL	no	SQL-like query language (Circonus Analytics Query Language: CAQL)	yes SQL interfaced based on SQLite is available	yes distributed, parallel query execution
APIs and other access methods	LDAP	HTTP API		ADO.NET JDBC native C library ODBC streaming API for large objects
Supported programming languages	All languages with LDAP bindings	.Net C C++ Clojure Erlang Go Haskell Java JavaScript JavaScript (Node.js) Lisp Lua Perl PHP Python R Ruby Rust Scala	.Net Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET others Third-party libraries to manipulate Berkeley DB files are available for many languages C C# C++ Java JavaScript (Node.js) 3rd party binding Perl Python Tcl	.Net C C++ Delphi Erlang Java JavaScript (Node.js) Perl PHP Python Tcl
Server-side scripts Stored procedures	no	yes, in Lua	no	user defined functions
Triggers	yes	no	yes only for the SQL API	yes
Partitioning methods Methods for storing different data on different nodes	Sharding cell division	Automatic, metric affinity per node	none	horizontal partitioning
Replication methods Methods for redundantly storing data on multiple nodes	yes	configurable replication factor, datacenter aware	Source-replica replication
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 per node, eventual consistency across nodes		Immediate Consistency
Foreign keys Referential integrity	no	no	no	yes
Transaction concepts Support to ensure data integrity after non-atomic manipulations of data	Atomic execution of specific operations	no	ACID	ACID MVCC
Concurrency Support for concurrent manipulation of data	yes	yes		yes
Durability Support for making data persistent	yes	yes	yes	yes
In-memory capabilities Is there an option to define some or all structures to be held in-memory only.	yes	no	yes	no
User concepts Access control	LDAP bind authentication	no	no	fine grained access rights according to SQL-standard
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More resources
	Atos Standard Common Repository	IRONdb	Oracle Berkeley DB	Postgres-XL
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