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DBMS > Amazon DocumentDB vs. Apache Phoenix vs. RDFox vs. Solr

System Properties Comparison Amazon DocumentDB vs. Apache Phoenix vs. RDFox vs. Solr

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Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonApache Phoenix  Xexclude from comparisonRDFox  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceA scale-out RDBMS with evolutionary schema built on Apache HBaseHigh performance knowledge graph and semantic reasoning engineA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelDocument storeRelational DBMSGraph DBMS
RDF store
Search engine
Secondary database modelsRelational DBMSSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.91
Rank#131  Overall
#24  Document stores
Score2.06
Rank#123  Overall
#58  Relational DBMS
Score0.29
Rank#300  Overall
#24  Graph DBMS
#13  RDF stores
Score41.02
Rank#24  Overall
#3  Search engines
Websiteaws.amazon.com/­documentdbphoenix.apache.orgwww.oxfordsemantic.techsolr.apache.org
Technical documentationaws.amazon.com/­documentdb/­resourcesphoenix.apache.orgdocs.oxfordsemantic.techsolr.apache.org/­resources.html
DeveloperApache Software FoundationOxford Semantic TechnologiesApache Software Foundation
Initial release2019201420172006
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20196.0, Septermber 20229.6.1, May 2024
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0commercialOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++Java
Server operating systemshostedLinux
Unix
Windows
Linux
macOS
Windows
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeschema-freeyes infolate-bound, schema-on-read capabilitiesyes infoRDF schemasyes 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.nonoyes
Secondary indexesyesyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLnoyesnoSolr Parallel SQL Interface
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)JDBCRESTful HTTP API
SPARQL 1.1
Java API
RESTful HTTP/JSON API
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
C
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C
Java
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresnouser defined functionsJava plugins
Triggersnonoyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesnoneShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones for high availability, asynchronous replication for up to 15 read replicasMulti-source replication
Source-replica replication
replication via a shared file systemyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)Hadoop integrationspark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual ConsistencyImmediate Consistency in stand-alone mode, Eventual Consistency in replicated setupsEventual Consistency
Foreign keys infoReferential integrityno infotypically not used, however similar functionality with DBRef possiblenono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsACIDACIDoptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyes
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.yesyesyes
User concepts infoAccess controlAccess rights for users and rolesAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyRoles, resources, and access typesyes

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Amazon DocumentDBApache PhoenixRDFoxSolr
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