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DBMS > Amazon Neptune vs. Kinetica vs. Solr vs. Stardog

System Properties Comparison Amazon Neptune vs. Kinetica vs. Solr vs. Stardog

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Editorial information provided by DB-Engines
NameAmazon Neptune  Xexclude from comparisonKinetica  Xexclude from comparisonSolr  Xexclude from comparisonStardog  Xexclude from comparison
DescriptionFast, reliable graph database built for the cloudFully vectorized database across both GPUs and CPUsA widely used distributed, scalable search engine based on Apache LuceneEnterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization
Primary database modelGraph DBMS
RDF store
Relational DBMSSearch engineGraph DBMS
RDF store
Secondary database modelsSpatial DBMS
Time Series DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.29
Rank#113  Overall
#9  Graph DBMS
#5  RDF stores
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score41.02
Rank#24  Overall
#3  Search engines
Score2.07
Rank#122  Overall
#11  Graph DBMS
#6  RDF stores
Websiteaws.amazon.com/­neptunewww.kinetica.comsolr.apache.orgwww.stardog.com
Technical documentationaws.amazon.com/­neptune/­developer-resourcesdocs.kinetica.comsolr.apache.org/­resources.htmldocs.stardog.com
DeveloperAmazonKineticaApache Software FoundationStardog-Union
Initial release2017201220062010
Current release7.1, August 20219.6.1, May 20247.3.0, May 2020
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache Version 2commercial info60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, C++JavaJava
Server operating systemshostedLinuxAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)Linux
macOS
Windows
Data schemeschema-freeyesyes infoDynamic Fields enables on-the-fly addition of new fieldsschema-free and OWL/RDFS-schema support
Typing infopredefined data types such as float or dateyesyesyes infosupports customizable data types and automatic typingyes
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.nonoyesno infoImport/export of XML data possible
Secondary indexesnoyesyes infoAll search fields are automatically indexedyes infosupports real-time indexing in full-text and geospatial
SQL infoSupport of SQLnoSQL-like DML and DDL statementsSolr Parallel SQL InterfaceYes, compatible with all major SQL variants through dedicated BI/SQL Server
APIs and other access methodsOpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin
JDBC
ODBC
RESTful HTTP API
Java API
RESTful HTTP/JSON API
GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3
Supported programming languagesC#
Go
Java
JavaScript
PHP
Python
Ruby
Scala
C++
Java
JavaScript (Node.js)
Python
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby
Server-side scripts infoStored proceduresnouser defined functionsJava pluginsuser defined functions and aggregates, HTTP Server extensions in Java
Triggersnoyes infotriggers when inserted values for one or more columns fall within a specified rangeyes infoUser configurable commands triggered on index changesyes infovia event handlers
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.Source-replica replicationyesMulti-source replication in HA-Cluster
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationEventual ConsistencyImmediate Consistency in HA-Cluster
Foreign keys infoReferential integrityyes infoRelationships in graphsyesnoyes inforelationships in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnooptimistic lockingACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyes infowith encyption-at-restyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes infoGPU vRAM or System RAMyesyes
User concepts infoAccess controlAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)Access rights for users and roles on table levelyesAccess rights for users and roles

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More resources
Amazon NeptuneKineticaSolrStardog
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