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DBMS > SiriDB vs. Spark SQL vs. Splice Machine vs. Tkrzw

System Properties Comparison SiriDB vs. Spark SQL vs. Splice Machine vs. Tkrzw

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Editorial information provided by DB-Engines
NameSiriDB  Xexclude from comparisonSpark SQL  Xexclude from comparisonSplice Machine  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
DescriptionOpen Source Time Series DBMSSpark SQL is a component on top of 'Spark Core' for structured data processingOpen-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 Cabinet
Primary database modelTime Series DBMSRelational DBMSRelational DBMSKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.07
Rank#378  Overall
#42  Time Series DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.54
Rank#252  Overall
#115  Relational DBMS
Score0.07
Rank#372  Overall
#57  Key-value stores
Websitesiridb.comspark.apache.org/­sqlsplicemachine.comdbmx.net/­tkrzw
Technical documentationdocs.siridb.comspark.apache.org/­docs/­latest/­sql-programming-guide.htmlsplicemachine.com/­how-it-works
DeveloperCesbitApache Software FoundationSplice MachineMikio Hirabayashi
Initial release2017201420142020
Current release3.5.0 ( 2.13), September 20233.1, March 20210.9.3, August 2020
License infoCommercial or Open SourceOpen Source infoMIT LicenseOpen Source infoApache 2.0Open Source infoAGPL 3.0, commercial license availableOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCScalaJavaC++
Server operating systemsLinuxLinux
OS X
Windows
Linux
OS X
Solaris
Windows
Linux
macOS
Data schemeyesyesyesschema-free
Typing infopredefined data types such as float or dateyes infoNumeric datayesyesno
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.nonono
Secondary indexesyesnoyes
SQL infoSupport of SQLnoSQL-like DML and DDL statementsyesno
APIs and other access methodsHTTP APIJDBC
ODBC
JDBC
Native Spark Datasource
ODBC
Supported programming languagesC
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R
Java
Python
R
Scala
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresnonoyes infoJavano
Triggersnonoyesno
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark CoreShared Nothhing Auto-Sharding, Columnar Partitioningnone
Replication methods infoMethods for redundantly storing data on multiple nodesyesnoneMulti-source replication
Source-replica replication
none
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoYes, via Full Spark Integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)yes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyesyes infousing specific database classes
User concepts infoAccess controlsimple rights management via user accountsnoAccess rights for users, groups and roles according to SQL-standardno

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More resources
SiriDBSpark SQLSplice MachineTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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