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DBMS > FatDB vs. GridDB vs. Hive vs. Linter vs. NSDb

System Properties Comparison FatDB vs. GridDB vs. Hive vs. Linter vs. NSDb

Editorial information provided by DB-Engines
NameFatDB  Xexclude from comparisonGridDB  Xexclude from comparisonHive  Xexclude from comparisonLinter  Xexclude from comparisonNSDb  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.
DescriptionA .NET NoSQL DBMS that can integrate with and extend SQL Server.Scalable in-memory time series database optimized for IoT and Big Datadata warehouse software for querying and managing large distributed datasets, built on HadoopRDBMS for high security requirementsScalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes
Primary database modelDocument store
Key-value store
Time Series DBMSRelational DBMSRelational DBMSTime Series DBMS
Secondary database modelsKey-value store
Relational DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.09
Rank#120  Overall
#10  Time Series DBMS
Score59.76
Rank#18  Overall
#12  Relational DBMS
Score0.12
Rank#350  Overall
#152  Relational DBMS
Score0.08
Rank#369  Overall
#40  Time Series DBMS
Websitegriddb.nethive.apache.orglinter.runsdb.io
Technical documentationdocs.griddb.netcwiki.apache.org/­confluence/­display/­Hive/­Homensdb.io/­Architecture
DeveloperFatCloudToshiba CorporationApache Software Foundation infoinitially developed by Facebookrelex.ru
Initial release20122013201219902017
Current release5.1, August 20223.1.3, April 2022
License infoCommercial or Open SourcecommercialOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoApache Version 2commercialOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC#C++JavaC and C++Java, Scala
Server operating systemsWindowsLinuxAll OS with a Java VMAIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows
Linux
macOS
Data schemeschema-freeyesyesyes
Typing infopredefined data types such as float or dateyesyes infonumerical, string, blob, geometry, boolean, timestampyesyesyes: int, bigint, decimal, string
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 indexesyesyesyesyesall fields are automatically indexed
SQL infoSupport of SQLno infoVia inetgration in SQL ServerSQL92, SQL-like TQL (Toshiba Query Language)SQL-like DML and DDL statementsyesSQL-like query language
APIs and other access methods.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
JDBC
ODBC
Thrift
ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)
gRPC
HTTP REST
WebSocket
Supported programming languagesC#C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C++
Java
PHP
Python
C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl
Java
Scala
Server-side scripts infoStored proceduresyes infovia applicationsnoyes infouser defined functions and integration of map-reduceyes infoproprietary syntax with the possibility to convert from PL/SQLno
Triggersyes infovia applicationsyesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingnoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replicationselectable replication factorSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsyes infoquery execution via MapReducenono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate consistency within container, eventual consistency across containersEventual ConsistencyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynononoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID at container levelnoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesUsing Apache Lucene
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes
User concepts infoAccess controlno infoCan implement custom security layer via applicationsAccess rights for users can be defined per databaseAccess rights for users, groups and rolesfine grained access rights according to SQL-standard
More information provided by the system vendor
FatDBGridDBHiveLinterNSDb
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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More resources
FatDBGridDBHiveLinterNSDb
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