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DBMS > AllegroGraph vs. Amazon Neptune vs. Datomic vs. Solr

System Properties Comparison AllegroGraph vs. Amazon Neptune vs. Datomic vs. Solr

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Editorial information provided by DB-Engines
NameAllegroGraph  Xexclude from comparisonAmazon Neptune  Xexclude from comparisonDatomic  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionHigh performance, persistent RDF store with additional support for Graph DBMSFast, reliable graph database built for the cloudDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelDocument store infowith version 6.5
Graph DBMS
RDF store
Vector DBMS
Graph DBMS
RDF store
Relational DBMSSearch engine
Secondary database modelsSpatial DBMSSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.06
Rank#187  Overall
#30  Document stores
#17  Graph DBMS
#7  RDF stores
#7  Vector DBMS
Score2.20
Rank#119  Overall
#9  Graph DBMS
#5  RDF stores
Score1.59
Rank#150  Overall
#69  Relational DBMS
Score42.91
Rank#24  Overall
#3  Search engines
Websiteallegrograph.comaws.amazon.com/­neptunewww.datomic.comsolr.apache.org
Technical documentationfranz.com/­agraph/­support/­documentation/­current/­agraph-introduction.htmlaws.amazon.com/­neptune/­developer-resourcesdocs.datomic.comsolr.apache.org/­resources.html
DeveloperFranz Inc.AmazonCognitectApache Software Foundation
Initial release2004201720122006
Current release8.0, December 20231.0.6735, June 20239.6.0, April 2024
License infoCommercial or Open Sourcecommercial infoLimited community edition freecommercialcommercial infolimited edition freeOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, ClojureJava
Server operating systemsLinux
OS X
Windows
hostedAll OS with a Java VMAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyes infoRDF schemasschema-freeyesyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyesyesyes infosupports customizable data types and automatic typing
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.no infobulk load of XML files possiblenonoyes
Secondary indexesyesnoyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLSPARQL is used as query languagenonoSolr Parallel SQL Interface
APIs and other access methodsRESTful HTTP API
SPARQL
OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin
RESTful HTTP APIJava API
RESTful HTTP/JSON API
Supported programming languagesC#
Clojure
Java
Lisp
Perl
Python
Ruby
Scala
C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala
Clojure
Java
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresyes infoJavaScript or Common Lispnoyes infoTransaction FunctionsJava plugins
TriggersyesnoBy using transaction functionsyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodeswith Federationnonenone infoBut extensive use of caching in the application peersSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Multi-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.none infoBut extensive use of caching in the application peersyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynoyes infoRelationships in graphsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACIDoptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes infowith encyption-at-restyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes inforecommended only for testing and developmentyes
User concepts infoAccess controlUsers with fine-grained authorization concept, user roles and pluggable authenticationAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)noyes
More information provided by the system vendor
AllegroGraphAmazon NeptuneDatomicSolr
Specific characteristicsKnowledge Graph Platform Leader FedShard - Designed for Entity-Event Knowledge Graph...
» more
Competitive advantagesAllegroGraph is uniquely suited to support adhoc queries through SPARQL, Prolog and...
» more
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