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DBMS > Bangdb vs. Kinetica vs. Spark SQL vs. Splice Machine

System Properties Comparison Bangdb vs. Kinetica vs. Spark SQL vs. Splice Machine

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NameBangdb  Xexclude from comparisonKinetica  Xexclude from comparisonSpark SQL  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionConverged and high performance database for device data, events, time series, document and graphFully vectorized database across both GPUs and CPUsSpark 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 Spark
Primary database modelDocument store
Graph DBMS
Time Series DBMS
Relational DBMSRelational DBMSRelational DBMS
Secondary database modelsSpatial DBMSSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.16
Rank#338  Overall
#47  Document stores
#32  Graph DBMS
#31  Time Series DBMS
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websitebangdb.comwww.kinetica.comspark.apache.org/­sqlsplicemachine.com
Technical documentationdocs.bangdb.comdocs.kinetica.comspark.apache.org/­docs/­latest/­sql-programming-guide.htmlsplicemachine.com/­how-it-works
DeveloperSachin Sinha, BangDBKineticaApache Software FoundationSplice Machine
Initial release2012201220142014
Current releaseBangDB 2.0, October 20217.1, August 20213.5.0 ( 2.13), September 20233.1, March 2021
License infoCommercial or Open SourceOpen Source infoBSD 3commercialOpen Source infoApache 2.0Open Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, C++C, C++ScalaJava
Server operating systemsLinuxLinuxLinux
OS X
Windows
Linux
OS X
Solaris
Windows
Data schemeschema-freeyesyesyes
Typing infopredefined data types such as float or dateyes: string, long, double, int, geospatial, stream, eventsyesyesyes
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 indexesyes infosecondary, composite, nested, reverse, geospatialyesnoyes
SQL infoSupport of SQLSQL like support with command line toolSQL-like DML and DDL statementsSQL-like DML and DDL statementsyes
APIs and other access methodsProprietary protocol
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
JDBC
ODBC
JDBC
Native Spark Datasource
ODBC
Supported programming languagesC
C#
C++
Java
Python
C++
Java
JavaScript (Node.js)
Python
Java
Python
R
Scala
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresnouser defined functionsnoyes infoJava
Triggersyes, Notifications (with Streaming only)yes infotriggers when inserted values for one or more columns fall within a specified rangenoyes
Partitioning methods infoMethods for storing different data on different nodesSharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithmShardingyes, utilizing Spark CoreShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor, Knob for CAP (enterprise version only)Source-replica replicationnoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemTunable consistency, set CAP knob accordinglyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integritynoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnonoACID
Concurrency infoSupport for concurrent manipulation of datayes, optimistic concurrency controlyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyes, implements WAL (Write ahead log) as wellyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes, run db with in-memory only modeyes infoGPU vRAM or System RAMnoyes
User concepts infoAccess controlyes (enterprise version only)Access rights for users and roles on table levelnoAccess rights for users, groups and roles according to SQL-standard

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More resources
BangdbKineticaSpark SQLSplice Machine
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