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DBMS > GridGain vs. Heroic vs. Linter vs. OpenMLDB

System Properties Comparison GridGain vs. Heroic vs. Linter vs. OpenMLDB

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Editorial information provided by DB-Engines
NameGridGain  Xexclude from comparisonHeroic  Xexclude from comparisonLinter  Xexclude from comparisonOpenMLDB  Xexclude from comparison
DescriptionGridGain is an in-memory computing platform, built on Apache IgniteTime Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchRDBMS for high security requirementsAn open-source machine learning database that provides a feature platform for training and inference
Primary database modelKey-value store
Relational DBMS
Time Series DBMSRelational DBMSTime Series DBMS
Secondary database modelsSpatial DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.55
Rank#150  Overall
#26  Key-value stores
#70  Relational DBMS
Score0.46
Rank#265  Overall
#22  Time Series DBMS
Score0.12
Rank#350  Overall
#152  Relational DBMS
Score0.10
Rank#359  Overall
#36  Time Series DBMS
Websitewww.gridgain.comgithub.com/­spotify/­heroiclinter.ruopenmldb.ai
Technical documentationwww.gridgain.com/­docs/­index.htmlspotify.github.io/­heroicopenmldb.ai/­docs/­zh/­main
DeveloperGridGain Systems, Inc.Spotifyrelex.ru4 Paradigm Inc.
Initial release2007201419902020
Current releaseGridGain 8.5.12024-2 February 2024
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercialOpen Source
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 languageJava, C++, .NetJavaC and C++C++, Java, Scala
Server operating systemsLinux
OS X
Solaris
Windows
AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows
Linux
Data schemeyesschema-freeyesFixed schema
Typing infopredefined data types such as float or dateyesyesyesyes
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.yesnonono
Secondary indexesyesyes infovia Elasticsearchyesyes
SQL infoSupport of SQLANSI-99 for query and DML statements, subset of DDLnoyesyes
APIs and other access methodsHDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
HQL (Heroic Query Language, a JSON-based language)
HTTP API
ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)
JDBC
SQLAlchemy
Supported programming languagesC#
C++
Java
PHP
Python
Ruby
Scala
C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl
C++
Go
Java
Python
Scala
Server-side scripts infoStored proceduresyes (compute grid and cache interceptors can be used instead)noyes infoproprietary syntax with the possibility to convert from PL/SQLno
Triggersyes (cache interceptors and events)noyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingnonehorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyes (replicated cache)yesSource-replica replicationSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes (compute grid and hadoop accelerator)nono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDno
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.yesnoyes
User concepts infoAccess controlSecurity Hooks for custom implementationsfine grained access rights according to SQL-standardfine grained access rights according to SQL-standard

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More resources
GridGainHeroicLinterOpenMLDB
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