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DBMS > Splice Machine vs. Tkrzw vs. Yaacomo

System Properties Comparison Splice Machine vs. Tkrzw vs. Yaacomo

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Editorial information provided by DB-Engines
NameSplice Machine  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparisonYaacomo  Xexclude from comparison
DescriptionOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and SparkA concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto CabinetOpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing
Primary database modelRelational DBMSKey-value storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.97
Rank#212  Overall
#99  Relational DBMS
Score0.00
Rank#382  Overall
#63  Key-value stores
Score0.00
Rank#382  Overall
#157  Relational DBMS
Websitesplicemachine.comdbmx.net/­tkrzwyaacomo.com
Technical documentationdoc.splicemachine.com
DeveloperSplice MachineMikio HirabayashiQ2WEB GmbH
Initial release201420202009
Current release3.1, March 20210.9.3, August 2020
License infoCommercial or Open SourceOpen Source infoAGPL 3.0, commercial license availableOpen Source infoApache Version 2.0commercial
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++
Server operating systemsLinux
OS X
Solaris
Windows
Linux
macOS
Android
Linux
Windows
Data schemeyesschema-freeyes
Typing infopredefined data types such as float or dateyesnoyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nono
Secondary indexesyesyes
SQL infoSupport of SQLyesnoyes
APIs and other access methodsJDBC
Native Spark Datasource
ODBC
JDBC
ODBC
Supported programming languagesC#
C++
Java
JavaScript (Node.js)
Python
R
Scala
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresyes infoJavano
Triggersyesnoyes
Partitioning methods infoMethods for storing different data on different nodesShared Nothhing Auto-Sharding, Columnar Partitioningnonehorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
noneSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsYes, via Full Spark Integrationnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID
Concurrency infoSupport for concurrent manipulation of datayes, multi-version concurrency control (MVCC)yesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes infousing specific database classesyes
User concepts infoAccess controlAccess rights for users, groups and roles according to SQL-standardnofine grained access rights according to SQL-standard
More information provided by the system vendor
Splice MachineTkrzw infoSuccessor of Tokyo Cabinet and Kyoto CabinetYaacomo
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More resources
Splice MachineTkrzw infoSuccessor of Tokyo Cabinet and Kyoto CabinetYaacomo
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