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DBMS > Amazon Neptune vs. Apache IoTDB vs. Databend vs. openGauss vs. Tkrzw

System Properties Comparison Amazon Neptune vs. Apache IoTDB vs. Databend vs. openGauss vs. Tkrzw

Editorial information provided by DB-Engines
NameAmazon Neptune  Xexclude from comparisonApache IoTDB  Xexclude from comparisonDatabend  Xexclude from comparisonopenGauss  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
DescriptionFast, reliable graph database built for the cloudAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkAn open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexityAn enterprise-class RDBMS compatible with high-performance, high-availability and high-performance originally developed by HuaweiA 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 modelGraph DBMS
RDF store
Time Series DBMSRelational DBMSRelational DBMSKey-value store
Secondary database modelsDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.29
Rank#113  Overall
#9  Graph DBMS
#5  RDF stores
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score0.34
Rank#283  Overall
#130  Relational DBMS
Score1.06
Rank#184  Overall
#84  Relational DBMS
Score0.07
Rank#372  Overall
#57  Key-value stores
Websiteaws.amazon.com/­neptuneiotdb.apache.orggithub.com/­datafuselabs/­databend
www.databend.com
gitee.com/­opengauss
opengauss.org
dbmx.net/­tkrzw
Technical documentationaws.amazon.com/­neptune/­developer-resourcesiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.databend.comdocs.opengauss.org/­en
gitee.com/­opengauss/­docs
DeveloperAmazonApache Software FoundationDatabend LabsHuawei and openGauss communityMikio Hirabayashi
Initial release20172018202120192020
Current release1.1.0, April 20231.0.59, April 20233.0, March 20220.9.3, August 2020
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0Open Source infoApache Version 2.0Open SourceOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud serviceyesnononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaRustC, C++, JavaC++
Server operating systemshostedAll OS with a Java VM (>= 1.8)hosted
Linux
macOS
LinuxLinux
macOS
Data schemeschema-freeyesyesyesschema-free
Typing infopredefined data types such as float or dateyesyesyesyesno
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.nonononono
Secondary indexesnoyesnoyes
SQL infoSupport of SQLnoSQL-like query languageyesANSI SQL 2011no
APIs and other access methodsOpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin
JDBC
Native API
CLI Client
JDBC
RESTful HTTP API
JDBC
ODBC
Supported programming languagesC#
Go
Java
JavaScript
PHP
Python
Ruby
Scala
C
C#
C++
Go
Java
Python
Scala
Go
Java
JavaScript (Node.js)
Python
Rust
C
C++
Java
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresnoyesnoyesno
Triggersnoyesnoyesno
Partitioning methods infoMethods for storing different data on different nodesnonehorizontal partitioning (by time range) + vertical partitioning (by deviceId)nonehorizontal partitioning (by range, list and hash)none
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.selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasnoneSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoIntegration with Hadoop and Sparknonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyes infoRelationships in graphsnonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoyesACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyes infowith encyption-at-restyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes infousing specific database classes
User concepts infoAccess controlAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)yesUsers with fine-grained authorization concept, user rolesAccess rights for users, groups and roles according to SQL-standardno

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More resources
Amazon NeptuneApache IoTDBDatabendopenGaussTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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