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DBMS > Kinetica vs. LevelDB vs. Spark SQL vs. Warp 10

System Properties Comparison Kinetica vs. LevelDB vs. Spark SQL vs. Warp 10

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonLevelDB  Xexclude from comparisonSpark SQL  Xexclude from comparisonWarp 10  Xexclude from comparison
DescriptionFully vectorized database across both GPUs and CPUsEmbeddable fast key-value storage library that provides an ordered mapping from string keys to string valuesSpark SQL is a component on top of 'Spark Core' for structured data processingTimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase
Primary database modelRelational DBMSKey-value storeRelational DBMSTime Series 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
Score2.25
Rank#115  Overall
#19  Key-value stores
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.14
Rank#344  Overall
#32  Time Series DBMS
Websitewww.kinetica.comgithub.com/­google/­leveldbspark.apache.org/­sqlwww.warp10.io
Technical documentationdocs.kinetica.comgithub.com/­google/­leveldb/­blob/­main/­doc/­index.mdspark.apache.org/­docs/­latest/­sql-programming-guide.htmlwww.warp10.io/­content/­02_Getting_started
DeveloperKineticaGoogleApache Software FoundationSenX
Initial release2012201120142015
Current release7.1, August 20211.23, February 20213.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoBSDOpen Source infoApache 2.0Open Source infoApache License 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++ScalaJava
Server operating systemsLinuxIllumos
Linux
OS X
Windows
Linux
OS X
Windows
Linux
OS X
Windows
Data schemeyesschema-freeyesschema-free
Typing infopredefined data types such as float or dateyesnoyesyes
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.nononono
Secondary indexesyesnonono
SQL infoSupport of SQLSQL-like DML and DDL statementsnoSQL-like DML and DDL statementsno
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
HTTP API
Jupyter
WebSocket
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
C++
Go
Java info3rd party binding
JavaScript (Node.js) info3rd party binding
Python info3rd party binding
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsnonoyes infoWarpScript
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangenonono
Partitioning methods infoMethods for storing different data on different nodesShardingnoneyes, utilizing Spark CoreSharding infobased on HBase
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationnonenoneselectable replication factor infobased on HBase
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency infobased on HBase
Foreign keys infoReferential integrityyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes infowith automatic compression on writesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes infoGPU vRAM or System RAMnoyes
User concepts infoAccess controlAccess rights for users and roles on table levelnonoMandatory use of cryptographic tokens, containing fine-grained authorizations

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KineticaLevelDBSpark SQLWarp 10
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