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DBMS > Amazon DocumentDB vs. Amazon Neptune vs. Dragonfly vs. Stardog

System Properties Comparison Amazon DocumentDB vs. Amazon Neptune vs. Dragonfly vs. Stardog

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Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonAmazon Neptune  Xexclude from comparisonDragonfly  Xexclude from comparisonStardog  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceFast, reliable graph database built for the cloudA drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instanceEnterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization
Primary database modelDocument storeGraph DBMS
RDF store
Key-value storeGraph DBMS
RDF store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.91
Rank#131  Overall
#24  Document stores
Score2.29
Rank#113  Overall
#9  Graph DBMS
#5  RDF stores
Score0.49
Rank#261  Overall
#38  Key-value stores
Score2.07
Rank#122  Overall
#11  Graph DBMS
#6  RDF stores
Websiteaws.amazon.com/­documentdbaws.amazon.com/­neptunegithub.com/­dragonflydb/­dragonfly
www.dragonflydb.io
www.stardog.com
Technical documentationaws.amazon.com/­documentdb/­resourcesaws.amazon.com/­neptune/­developer-resourceswww.dragonflydb.io/­docsdocs.stardog.com
DeveloperAmazonDragonflyDB team and community contributorsStardog-Union
Initial release2019201720232010
Current release1.0, March 20237.3.0, May 2020
License infoCommercial or Open SourcecommercialcommercialOpen Source infoBSL 1.1commercial 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 serviceyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++Java
Server operating systemshostedhostedLinuxLinux
macOS
Windows
Data schemeschema-freeschema-freescheme-freeschema-free and OWL/RDFS-schema support
Typing infopredefined data types such as float or dateyesyesstrings, hashes, lists, sets, sorted sets, bit arraysyes
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.nononono infoImport/export of XML data possible
Secondary indexesyesnonoyes infosupports real-time indexing in full-text and geospatial
SQL infoSupport of SQLnononoYes, compatible with all major SQL variants through dedicated BI/SQL Server
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin
Proprietary protocol infoRESP - REdis Serialization ProtocolGraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala
C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl
.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby
Server-side scripts infoStored proceduresnonoLuauser defined functions and aggregates, HTTP Server extensions in Java
Triggersnonopublish/subscribe channels provide some trigger functionalityyes infovia event handlers
Partitioning methods infoMethods for storing different data on different nodesnonenonenone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones for high availability, asynchronous replication for up to 15 read replicasMulti-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 replicationMulti-source replication in HA-Cluster
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)nonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual ConsistencyImmediate Consistency in HA-Cluster
Foreign keys infoReferential integrityno infotypically not used, however similar functionality with DBRef possibleyes infoRelationships in graphsnoyes inforelationships in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsACIDAtomic execution of command blocks and scriptsACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, strict serializability by the serveryes
Durability infoSupport for making data persistentyesyes infowith encyption-at-restyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes
User concepts infoAccess controlAccess rights for users and rolesAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)Password-based authenticationAccess rights for users and roles

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More resources
Amazon DocumentDBAmazon NeptuneDragonflyStardog
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