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DBMS > FatDB vs. GridDB vs. Spark SQL vs. TimescaleDB

System Properties Comparison FatDB vs. GridDB vs. Spark SQL vs. TimescaleDB

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Editorial information provided by DB-Engines
NameFatDB  Xexclude from comparisonGridDB  Xexclude from comparisonSpark SQL  Xexclude from comparisonTimescaleDB  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 DataSpark SQL is a component on top of 'Spark Core' for structured data processingA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelDocument store
Key-value store
Time Series DBMSRelational DBMSTime Series DBMS
Secondary database modelsKey-value store
Relational DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.02
Rank#132  Overall
#11  Time Series DBMS
Score19.15
Rank#33  Overall
#20  Relational DBMS
Score4.87
Rank#74  Overall
#4  Time Series DBMS
Websitegriddb.netspark.apache.org/­sqlwww.timescale.com
Technical documentationdocs.griddb.netspark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.timescale.com
DeveloperFatCloudToshiba CorporationApache Software FoundationTimescale
Initial release2012201320142017
Current release5.1, August 20223.5.0 ( 2.13), September 20232.13.0, November 2023
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 2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC#C++ScalaC
Server operating systemsWindowsLinuxLinux
OS X
Windows
Linux
OS X
Windows
Data schemeschema-freeyesyesyes
Typing infopredefined data types such as float or dateyesyes infonumerical, string, blob, geometry, boolean, timestampyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types
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.nonoyes
Secondary indexesyesyesnoyes
SQL infoSupport of SQLno infoVia inetgration in SQL ServerSQL92, SQL-like TQL (Toshiba Query Language)SQL-like DML and DDL statementsyes infofull PostgreSQL SQL syntax
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
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesC#C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
Java
Python
R
Scala
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresyes infovia applicationsnonouser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersyes infovia applicationsyesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes, utilizing Spark Coreyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replicationnoneSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate consistency within container, eventual consistency across containersImmediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID at container levelnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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.yesnono
User concepts infoAccess controlno infoCan implement custom security layer via applicationsAccess rights for users can be defined per databasenofine grained access rights according to SQL-standard
More information provided by the system vendor
FatDBGridDBSpark SQLTimescaleDB
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
FatDBGridDBSpark SQLTimescaleDB
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