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DBMS > Amazon Neptune vs. Apache Druid vs. Apache IoTDB vs. Dragonfly

System Properties Comparison Amazon Neptune vs. Apache Druid vs. Apache IoTDB vs. Dragonfly

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Editorial information provided by DB-Engines
NameAmazon Neptune  Xexclude from comparisonApache Druid  Xexclude from comparisonApache IoTDB  Xexclude from comparisonDragonfly  Xexclude from comparison
DescriptionFast, reliable graph database built for the cloudOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkA drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance
Primary database modelGraph DBMS
RDF store
Relational DBMS
Time Series DBMS
Time Series DBMSKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.29
Rank#113  Overall
#9  Graph DBMS
#5  RDF stores
Score3.25
Rank#90  Overall
#47  Relational DBMS
#7  Time Series DBMS
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score0.49
Rank#261  Overall
#38  Key-value stores
Websiteaws.amazon.com/­neptunedruid.apache.orgiotdb.apache.orggithub.com/­dragonflydb/­dragonfly
www.dragonflydb.io
Technical documentationaws.amazon.com/­neptune/­developer-resourcesdruid.apache.org/­docs/­latest/­designiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlwww.dragonflydb.io/­docs
DeveloperAmazonApache Software Foundation and contributorsApache Software FoundationDragonflyDB team and community contributors
Initial release2017201220182023
Current release29.0.1, April 20241.1.0, April 20231.0, March 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache license v2Open Source infoApache Version 2.0Open Source infoBSL 1.1
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaC++
Server operating systemshostedLinux
OS X
Unix
All OS with a Java VM (>= 1.8)Linux
Data schemeschema-freeyes infoschema-less columns are supportedyesscheme-free
Typing infopredefined data types such as float or dateyesyesyesstrings, hashes, lists, sets, sorted sets, bit arrays
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
Secondary indexesnoyesyesno
SQL infoSupport of SQLnoSQL for queryingSQL-like query languageno
APIs and other access methodsOpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin
JDBC
RESTful HTTP/JSON API
JDBC
Native API
Proprietary protocol infoRESP - REdis Serialization Protocol
Supported programming languagesC#
Go
Java
JavaScript
PHP
Python
Ruby
Scala
Clojure
JavaScript
PHP
Python
R
Ruby
Scala
C
C#
C++
Go
Java
Python
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
Server-side scripts infoStored proceduresnonoyesLua
Triggersnonoyespublish/subscribe channels provide some trigger functionality
Partitioning methods infoMethods for storing different data on different nodesnoneSharding infomanual/auto, time-basedhorizontal partitioning (by time range) + vertical partitioning (by deviceId)
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.yes, via HDFS, S3 or other storage enginesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoIntegration with Hadoop and Sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency
Strong Consistency with Raft
Eventual Consistency
Foreign keys infoReferential integrityyes infoRelationships in graphsnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnonoAtomic execution of command blocks and scripts
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, strict serializability by the server
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.noyesyes
User concepts infoAccess controlAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)RBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemyesPassword-based authentication

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More resources
Amazon NeptuneApache DruidApache IoTDBDragonfly
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