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DBMS > Hazelcast vs. Spark SQL vs. Virtuoso

System Properties Comparison Hazelcast vs. Spark SQL vs. Virtuoso

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Editorial information provided by DB-Engines
NameHazelcast  Xexclude from comparisonSpark SQL  Xexclude from comparisonVirtuoso  Xexclude from comparison
DescriptionA widely adopted in-memory data gridSpark SQL is a component on top of 'Spark Core' for structured data processingVirtuoso is a multi-model hybrid-RDBMS that supports management of data represented as relational tables and/or property graphs
Primary database modelKey-value storeRelational DBMSDocument store
Graph DBMS
Native XML DBMS
Relational DBMS
RDF store
Search engine
Secondary database modelsDocument store infoJSON support with IMDG 3.12Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score6.87
Rank#55  Overall
#6  Key-value stores
Score19.15
Rank#33  Overall
#20  Relational DBMS
Score4.20
Rank#83  Overall
#14  Document stores
#4  Graph DBMS
#2  Native XML DBMS
#45  Relational DBMS
#2  RDF stores
#9  Search engines
Websitehazelcast.comspark.apache.org/­sqlvirtuoso.openlinksw.com
Technical documentationhazelcast.org/­imdg/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.openlinksw.com/­virtuoso
DeveloperHazelcastApache Software FoundationOpenLink Software
Initial release200820141998
Current release5.3.6, November 20233.5.0 ( 2.13), September 20237.2.11, September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2; commercial licenses availableOpen Source infoApache 2.0Open Source infoGPLv2, extended commercial license available
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaScalaC
Server operating systemsAll OS with a Java VMLinux
OS X
Windows
AIX
FreeBSD
HP-UX
Linux
OS X
Solaris
Windows
Data schemeschema-freeyesyes infoSQL - Standard relational schema
RDF - Quad (S, P, O, G) or Triple (S, P, O)
XML - DTD, XML Schema
DAV - freeform filesystem objects, plus User Defined Types a/k/a Dynamic Extension Type
Typing infopredefined data types such as float or dateyesyesyes
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 strategynoyes
Secondary indexesyesnoyes
SQL infoSupport of SQLSQL-like query languageSQL-like DML and DDL statementsyes infoSQL-92, SQL-200x, SQL-3, SQLX
APIs and other access methodsJCache
JPA
Memcached protocol
RESTful HTTP API
JDBC
ODBC
ADO.NET
GeoSPARQL
HTTP API
JDBC
Jena RDF API
ODBC
OLE DB
RDF4J API
RESTful HTTP API
Sesame REST HTTP Protocol
SOAP webservices
SPARQL 1.1
WebDAV
XPath
XQuery
XSLT
Supported programming languages.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
Java
Python
R
Scala
.Net
C
C#
C++
Java
JavaScript
Perl
PHP
Python
Ruby
Visual Basic
Server-side scripts infoStored proceduresyes infoEvent Listeners, Executor Servicesnoyes infoVirtuoso PL
Triggersyes infoEventsnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark Coreyes
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoReplicated MapnoneChain, star, and bi-directional replication
Multi-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate Consistency
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataone or two-phase-commit; repeatable reads; read commitednoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes
User concepts infoAccess controlRole-based access controlnoFine-grained Attribute-Based Access Control (ABAC) in addition to typical coarse-grained Role-Based Access Control (RBAC) according to SQL-standard. Pluggable authentication with supported standards (LDAP, Active Directory, Kerberos)
More information provided by the system vendor
HazelcastSpark SQLVirtuoso
Specific characteristicsVirtuoso is a modern multi-model RDBMS for managing data represented as tabular relations...
» more
Competitive advantagesPerformance & Scale โ€” as exemplified by DBpedia and the LOD Cloud it spawned, i.e.,...
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Typical application scenariosUsed for โ€” Analytics/BI Conceptual Data Virtualization Enterprise Knowledge Graphs...
» more
Key customersBroad use across enterprises and governments including โ€” European Union (EU) US Government...
» more
Market metricsLargest installed-base โ€‹of Multi-Model RDBMS for AI-friendly Knowledge Graphs Platform...
» more
Licensing and pricing modelsAvailable in both Commercial Enterprise and Open Source (GPL v2) Editions Feature...
» more

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HazelcastSpark SQLVirtuoso
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