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DBMS > GridDB vs. HEAVY.AI vs. Kinetica vs. Typesense

System Properties Comparison GridDB vs. HEAVY.AI vs. Kinetica vs. Typesense

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Editorial information provided by DB-Engines
NameGridDB  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonKinetica  Xexclude from comparisonTypesense  Xexclude from comparison
DescriptionScalable in-memory time series database optimized for IoT and Big DataA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareFully vectorized database across both GPUs and CPUsA typo-tolerant, in-memory search engine optimized for instant search-as-you-type experiences and developer productivity
Primary database modelTime Series DBMSRelational DBMSRelational DBMSSearch engine
Secondary database modelsKey-value store
Relational DBMS
Spatial DBMSSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score1.77
Rank#141  Overall
#65  Relational DBMS
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score0.79
Rank#210  Overall
#14  Search engines
Websitegriddb.netgithub.com/­heavyai/­heavydb
www.heavy.ai
www.kinetica.comtypesense.org
Technical documentationdocs.griddb.netdocs.heavy.aidocs.kinetica.comtypesense.org/­docs
DeveloperToshiba CorporationHEAVY.AI, Inc.Kinetica
Initial release2013201620122015
Current release5.1, August 20225.10, January 20227.1, August 2021
License infoCommercial or Open SourceOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoApache Version 2; enterprise edition availablecommercialOpen Source infoGPL V3
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++ and CUDAC, C++C++
Server operating systemsLinuxLinuxLinuxLinux
Data schemeyesyesyesschema-free infopre-defined schema optional
Typing infopredefined data types such as float or dateyes infonumerical, string, blob, geometry, boolean, timestampyesyesyes
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 indexesyesnoyesyes
SQL infoSupport of SQLSQL92, SQL-like TQL (Toshiba Query Language)yesSQL-like DML and DDL statementsno
APIs and other access methodsJDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
JDBC
ODBC
Thrift
Vega
JDBC
ODBC
RESTful HTTP API
RESTful HTTP API
Supported programming languagesC
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
All languages supporting JDBC/ODBC/Thrift
Python
C++
Java
JavaScript (Node.js)
Python
.Net infocommunity maintained
Clojure infocommunity maintained
Dart infocommunity maintained
Go infocommunity maintained
Java infocommunity maintained
JavaScript
Perl infocommunity maintained
PHP
Python
Ruby
Rust infocommunity maintained
Swift infocommunity maintained
Server-side scripts infoStored proceduresnonouser defined functionsno
Triggersyesnoyes infotriggers when inserted values for one or more columns fall within a specified range
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoRound robinSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationMulti-source replicationSource-replica replicationMulti-source replication using RAFT
MapReduce infoOffers an API for user-defined Map/Reduce methodsConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency within container, eventual consistency across containersImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID at container levelnonono
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.yesyesyes infoGPU vRAM or System RAMyes
User concepts infoAccess controlAccess rights for users can be defined per databasefine grained access rights according to SQL-standardAccess rights for users and roles on table level
More information provided by the system vendor
GridDBHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022KineticaTypesense
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
GridDBHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022KineticaTypesense
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