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DBMS > AgensGraph vs. EventStoreDB vs. RDF4J vs. Spark SQL

System Properties Comparison AgensGraph vs. EventStoreDB vs. RDF4J vs. Spark SQL

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Editorial information provided by DB-Engines
NameAgensGraph  Xexclude from comparisonEventStoreDB  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionMulti-model database supporting relational and graph data models and built upon PostgreSQLIndustrial-strength, open-source database solution built from the ground up for event sourcing.RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.Spark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelGraph DBMS
Relational DBMS
Event StoreRDF storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.23
Rank#315  Overall
#26  Graph DBMS
#140  Relational DBMS
Score1.19
Rank#173  Overall
#1  Event Stores
Score0.74
Rank#222  Overall
#9  RDF stores
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitebitnine.net/­agensgraphwww.eventstore.comrdf4j.orgspark.apache.org/­sql
Technical documentationbitnine.net/­documentationdevelopers.eventstore.comrdf4j.org/­documentationspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperBitnine Global Inc.Event Store LimitedSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.Apache Software Foundation
Initial release2016201220042014
Current release2.1, December 201821.2, February 20213.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache License 2.0Open SourceOpen Source infoEclipse Distribution License (EDL), v1.0.Open 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 languageCJavaScala
Server operating systemsLinux
OS X
Windows
Linux
Windows
Linux
OS X
Unix
Windows
Linux
OS X
Windows
Data schemedepending on used data modelyes infoRDF Schemasyes
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.nono
Secondary indexesyesyesno
SQL infoSupport of SQLyesnoSQL-like DML and DDL statements
APIs and other access methodsCypher Query Language
JDBC
Java API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
JDBC
ODBC
Supported programming languagesC
Java
JavaScript
Python
Java
PHP
Python
Java
Python
R
Scala
Server-side scripts infoStored proceduresyesyesno
Triggersnoyesno
Partitioning methods infoMethods for storing different data on different nodesno, but can be realized using table inheritancenoneyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency
Foreign keys infoReferential integrityyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoIsolation support depends on the API usedno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyes infoin-memory storage is supported as wellyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nono
User concepts infoAccess controlfine grained access rights according to SQL-standardnono

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More resources
AgensGraphEventStoreDBRDF4J infoformerly known as SesameSpark SQL
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