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DBMS > Kinetica vs. MarkLogic vs. Riak KV vs. Spark SQL

System Properties Comparison Kinetica vs. MarkLogic vs. Riak KV vs. Spark SQL

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonMarkLogic  Xexclude from comparisonRiak KV  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionFully vectorized database across both GPUs and CPUsOperational and transactional Enterprise NoSQL databaseDistributed, fault tolerant key-value storeSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSDocument store
Native XML DBMS
RDF store infoas of version 7
Search engine
Key-value store infowith links between data sets and object tags for the creation of secondary indexesRelational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score5.18
Rank#63  Overall
#11  Document stores
#1  Native XML DBMS
#1  RDF stores
#7  Search engines
Score4.01
Rank#79  Overall
#9  Key-value stores
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitewww.kinetica.comwww.marklogic.comspark.apache.org/­sql
Technical documentationdocs.kinetica.comdocs.marklogic.comwww.tiot.jp/­riak-docs/­riak/­kv/­latestspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperKineticaMarkLogic Corp.OpenSource, formerly Basho TechnologiesApache Software Foundation
Initial release2012200120092014
Current release7.1, August 202111.0, December 20223.2.0, December 20223.5.0 ( 2.13), September 2023
License infoCommercial or Open Sourcecommercialcommercial inforestricted free version is availableOpen Source infoApache version 2, commercial enterprise editionOpen Source infoApache 2.0
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++ErlangScala
Server operating systemsLinuxLinux
OS X
Windows
Linux
OS X
Linux
OS X
Windows
Data schemeyesschema-free infoSchema can be enforcedschema-freeyes
Typing infopredefined data types such as float or dateyesyesnoyes
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.noyesnono
Secondary indexesyesyesrestrictedno
SQL infoSupport of SQLSQL-like DML and DDL statementsyes infoSQL92noSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
Java API
Node.js Client API
ODBC
proprietary Optic API infoProprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT
HTTP API
Native Erlang Interface
JDBC
ODBC
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsyes infovia XQuery or JavaScriptErlangno
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangeyesyes infopre-commit hooks and post-commit hooksno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infono "single point of failure"yes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyesselectable replication factornone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integrityyesnono infolinks between data sets can be storedno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infocan act as a resource manager in an XA/JTA transactionnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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.yes infoGPU vRAM or System RAMyes, with Range Indexesno
User concepts infoAccess controlAccess rights for users and roles on table levelRole-based access control at the document and subdocument levelsyes, using Riak Securityno

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More resources
KineticaMarkLogicRiak KVSpark SQL
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