DBMS > Atos Standard Common Repository vs. Riak TS vs. Splice Machine vs. Tkrzw

System Properties Comparison Atos Standard Common Repository vs. Riak TS vs. Splice Machine vs. Tkrzw

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Name	Atos Standard Common Repository  Xexclude from comparison	Riak TS  Xexclude from comparison	Splice Machine  Xexclude from comparison	Tkrzw Successor of Tokyo Cabinet and Kyoto Cabinet  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	Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV	Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark	A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet
Primary database model	Document store Key-value store	Time Series DBMS	Relational DBMS	Key-value store
DB-Engines Ranking measures the popularity of database management systems Trend Chart		Score 0.28 Rank #307   Overall #27   Time Series DBMS	Score 0.54 Rank #252   Overall #115   Relational DBMS	Score 0.07 Rank #372   Overall #57   Key-value stores
Website	atos.net/en/convergence-creators/portfolio/standard-common-repository		splicemachine.com	dbmx.net/­tkrzw
Technical documentation		www.tiot.jp/­riak-docs/­riak/­ts/­latest	splicemachine.com/­how-it-works
Developer	Atos Convergence Creators	Open Source, formerly Basho Technologies	Splice Machine	Mikio Hirabayashi
Initial release	2016	2015	2014	2020
Current release	1703	3.0.0, September 2022	3.1, March 2021	0.9.3, August 2020
License Commercial or Open Source	commercial	Open Source	Open Source AGPL 3.0, commercial license available	Open Source Apache Version 2.0
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	Erlang	Java	C++
Server operating systems	Linux	Linux OS X	Linux OS X Solaris Windows	Linux macOS
Data scheme	Schema and schema-less with LDAP views	schema-free	yes	schema-free
Typing predefined data types such as float or date	optional	no	yes	no
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		no
Secondary indexes	yes	restricted	yes
SQL Support of SQL	no	yes, limited	yes	no
APIs and other access methods	LDAP	HTTP API Native Erlang Interface	JDBC Native Spark Datasource ODBC
Supported programming languages	All languages with LDAP bindings	C unofficial client library C# C++ unofficial client library Clojure unofficial client library Dart unofficial client library Erlang Go unofficial client library Groovy unofficial client library Haskell unofficial client library Java JavaScript unofficial client library Lisp unofficial client library Perl unofficial client library PHP Python Ruby Scala unofficial client library Smalltalk unofficial client library	C# C++ Java JavaScript (Node.js) Python R Scala	C++ Java Python Ruby
Server-side scripts Stored procedures	no	Erlang	yes Java	no
Triggers	yes	yes pre-commit hooks and post-commit hooks	yes	no
Partitioning methods Methods for storing different data on different nodes	Sharding cell division	Sharding	Shared Nothhing Auto-Sharding, Columnar Partitioning	none
Replication methods Methods for redundantly storing data on multiple nodes	yes	selectable replication factor	Multi-source replication Source-replica replication	none
MapReduce Offers an API for user-defined Map/Reduce methods		yes	Yes, via Full Spark Integration	no
Consistency concepts Methods to ensure consistency in a distributed system	Immediate Consistency or Eventual Consistency depending on configuration	Eventual Consistency	Immediate Consistency	Immediate Consistency
Foreign keys Referential integrity	no	no links between datasets can be stored	yes	no
Transaction concepts Support to ensure data integrity after non-atomic manipulations of data	Atomic execution of specific operations	no	ACID
Concurrency Support for concurrent manipulation of data	yes	yes	yes, multi-version concurrency control (MVCC)	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		yes	yes using specific database classes
User concepts Access control	LDAP bind authentication	no	Access rights for users, groups and roles according to SQL-standard	no
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More resources
	Atos Standard Common Repository	Riak TS	Splice Machine	Tkrzw Successor of Tokyo Cabinet and Kyoto Cabinet
Recent citations in the news		New Basho Data Platform Provides Operational Simplicity for Enterprise Big Data Applications 7 June 2015, insideBIGDATA Best open source databases for IoT applications 26 May 2017, Open Source For You provided by Google News	Machine learning data pipeline outfit Splice Machine files for insolvency 26 August 2021, The Register Splice Machine Launches the Splice Machine Feature Store to Simplify Feature Engineering and Democratize Machine ... 19 January 2021, PR Newswire Splice Machine Launches Feature Store to Simplify Feature Engineering 19 January 2021, Datanami Real-time machine learning with Splice Machine's ML Manager 17 April 2019, TechTarget How To Axe Db2 But Keep Your Code 10 March 2020, Towards Data Science provided by Google News

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