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DBMS > Hazelcast vs. Spark SQL vs. SWC-DB vs. Yanza

System Properties Comparison Hazelcast vs. Spark SQL vs. SWC-DB vs. Yanza

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Editorial information provided by DB-Engines
NameHazelcast  Xexclude from comparisonSpark SQL  Xexclude from comparisonSWC-DB infoSuper Wide Column Database  Xexclude from comparisonYanza  Xexclude from comparison
Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionA widely adopted in-memory data gridSpark SQL is a component on top of 'Spark Core' for structured data processingA high performance, scalable Wide Column DBMSTime Series DBMS for IoT Applications
Primary database modelKey-value storeRelational DBMSWide column storeTime Series DBMS
Secondary database modelsDocument store infoJSON support with IMDG 3.12Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score5.46
Rank#61  Overall
#7  Key-value stores
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.08
Rank#364  Overall
#13  Wide column stores
Websitehazelcast.comspark.apache.org/­sqlgithub.com/­kashirin-alex/­swc-db
www.swcdb.org
yanza.com
Technical documentationhazelcast.org/­imdg/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperHazelcastApache Software FoundationAlex KashirinYanza
Initial release2008201420202015
Current release5.3.6, November 20233.5.0 ( 2.13), September 20230.5, April 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2; commercial licenses availableOpen Source infoApache 2.0Open Source infoGPL V3commercial infofree version available
Cloud-based only infoOnly available as a cloud servicenononono infobut mainly used as a service provided by Yanza
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaScalaC++
Server operating systemsAll OS with a Java VMLinux
OS X
Windows
LinuxWindows
Data schemeschema-freeyesschema-freeschema-free
Typing infopredefined data types such as float or dateyesyesno
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 indexesyesnono
SQL infoSupport of SQLSQL-like query languageSQL-like DML and DDL statementsSQL-like query languageno
APIs and other access methodsJCache
JPA
Memcached protocol
RESTful HTTP API
JDBC
ODBC
Proprietary protocol
Thrift
HTTP API
Supported programming languages.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
Java
Python
R
Scala
C++any language that supports HTTP calls
Server-side scripts infoStored proceduresyes infoEvent Listeners, Executor Servicesnonono
Triggersyes infoEventsnonoyes infoTimer and event based
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark CoreShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoReplicated Mapnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataone or two-phase-commit; repeatable reads; read commitednono
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.yesnono
User concepts infoAccess controlRole-based access controlnono

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HazelcastSpark SQLSWC-DB infoSuper Wide Column DatabaseYanza
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