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DBMS > Hazelcast vs. Kinetica vs. LevelDB vs. Microsoft Azure Table Storage vs. RDF4J

System Properties Comparison Hazelcast vs. Kinetica vs. LevelDB vs. Microsoft Azure Table Storage vs. RDF4J

Editorial information provided by DB-Engines
NameHazelcast  Xexclude from comparisonKinetica  Xexclude from comparisonLevelDB  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparison
DescriptionA widely adopted in-memory data gridFully vectorized database across both GPUs and CPUsEmbeddable fast key-value storage library that provides an ordered mapping from string keys to string valuesA Wide Column Store for rapid development using massive semi-structured datasetsRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.
Primary database modelKey-value storeRelational DBMSKey-value storeWide column storeRDF store
Secondary database modelsDocument store infoJSON support with IMDG 3.12Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score5.46
Rank#61  Overall
#7  Key-value stores
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score2.25
Rank#115  Overall
#19  Key-value stores
Score4.04
Rank#77  Overall
#6  Wide column stores
Score0.74
Rank#222  Overall
#9  RDF stores
Websitehazelcast.comwww.kinetica.comgithub.com/­google/­leveldbazure.microsoft.com/­en-us/­services/­storage/­tablesrdf4j.org
Technical documentationhazelcast.org/­imdg/­docsdocs.kinetica.comgithub.com/­google/­leveldb/­blob/­main/­doc/­index.mdrdf4j.org/­documentation
DeveloperHazelcastKineticaGoogleMicrosoftSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.
Initial release20082012201120122004
Current release5.3.6, November 20237.1, August 20211.23, February 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2; commercial licenses availablecommercialOpen Source infoBSDcommercialOpen Source infoEclipse Distribution License (EDL), v1.0.
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC, C++C++Java
Server operating systemsAll OS with a Java VMLinuxIllumos
Linux
OS X
Windows
hostedLinux
OS X
Unix
Windows
Data schemeschema-freeyesschema-freeschema-freeyes infoRDF Schemas
Typing infopredefined data types such as float or dateyesyesnoyesyes
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.yes infothe object must implement a serialization strategynonono
Secondary indexesyesyesnonoyes
SQL infoSupport of SQLSQL-like query languageSQL-like DML and DDL statementsnonono
APIs and other access methodsJCache
JPA
Memcached protocol
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
RESTful HTTP APIJava API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
Supported programming languages.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C++
Java
JavaScript (Node.js)
Python
C++
Go
Java info3rd party binding
JavaScript (Node.js) info3rd party binding
Python info3rd party binding
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
PHP
Python
Server-side scripts infoStored proceduresyes infoEvent Listeners, Executor Servicesuser defined functionsnonoyes
Triggersyes infoEventsyes infotriggers when inserted values for one or more columns fall within a specified rangenonoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingnoneSharding infoImplicit feature of the cloud servicenone
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoReplicated MapSource-replica replicationnoneyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.none
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnononono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataone or two-phase-commit; repeatable reads; read commitednonooptimistic lockingACID infoIsolation support depends on the API used
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyes infowith automatic compression on writesyesyes infoin-memory storage is supported as well
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes infoGPU vRAM or System RAMno
User concepts infoAccess controlRole-based access controlAccess rights for users and roles on table levelnoAccess rights based on private key authentication or shared access signaturesno

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More resources
HazelcastKineticaLevelDBMicrosoft Azure Table StorageRDF4J infoformerly known as Sesame
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