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DBMS > Amazon DocumentDB vs. Heroic vs. RDFox vs. Tkrzw

System Properties Comparison Amazon DocumentDB vs. Heroic vs. RDFox vs. Tkrzw

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Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonHeroic  Xexclude from comparisonRDFox  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceTime Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchHigh performance knowledge graph and semantic reasoning engineA concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet
Primary database modelDocument storeTime Series DBMSGraph DBMS
RDF store
Key-value store
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.91
Rank#131  Overall
#24  Document stores
Score0.46
Rank#265  Overall
#22  Time Series DBMS
Score0.29
Rank#300  Overall
#24  Graph DBMS
#13  RDF stores
Score0.07
Rank#372  Overall
#57  Key-value stores
Websiteaws.amazon.com/­documentdbgithub.com/­spotify/­heroicwww.oxfordsemantic.techdbmx.net/­tkrzw
Technical documentationaws.amazon.com/­documentdb/­resourcesspotify.github.io/­heroicdocs.oxfordsemantic.tech
DeveloperSpotifyOxford Semantic TechnologiesMikio Hirabayashi
Initial release2019201420172020
Current release6.0, Septermber 20220.9.3, August 2020
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercialOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++C++
Server operating systemshostedLinux
macOS
Windows
Linux
macOS
Data schemeschema-freeschema-freeyes infoRDF schemasschema-free
Typing infopredefined data types such as float or dateyesyesyesno
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.nonono
Secondary indexesyesyes infovia Elasticsearch
SQL infoSupport of SQLnononono
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)HQL (Heroic Query Language, a JSON-based language)
HTTP API
RESTful HTTP API
SPARQL 1.1
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
C
Java
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresnonono
Triggersnonono
Partitioning methods infoMethods for storing different data on different nodesnoneShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones for high availability, asynchronous replication for up to 15 read replicasyesreplication via a shared file systemnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)nono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setupsImmediate 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 operationsnoACID
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.noyesyes infousing specific database classes
User concepts infoAccess controlAccess rights for users and rolesRoles, resources, and access typesno

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More resources
Amazon DocumentDBHeroicRDFoxTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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