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DBMS > GridGain vs. HEAVY.AI vs. Hive

System Properties Comparison GridGain vs. HEAVY.AI vs. Hive

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Editorial information provided by DB-Engines
NameGridGain  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonHive  Xexclude from comparison
DescriptionGridGain is an in-memory computing platform, built on Apache IgniteA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwaredata warehouse software for querying and managing large distributed datasets, built on Hadoop
Primary database modelKey-value store
Relational DBMS
Relational DBMSRelational DBMS
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.59
Rank#158  Overall
#26  Key-value stores
#73  Relational DBMS
Score2.30
Rank#127  Overall
#60  Relational DBMS
Score64.82
Rank#18  Overall
#12  Relational DBMS
Websitewww.gridgain.comgithub.com/­heavyai/­heavydb
www.heavy.ai
hive.apache.org
Technical documentationwww.gridgain.com/­docs/­index.htmldocs.heavy.aicwiki.apache.org/­confluence/­display/­Hive/­Home
DeveloperGridGain Systems, Inc.HEAVY.AI, Inc.Apache Software Foundation infoinitially developed by Facebook
Initial release200720162012
Current releaseGridGain 8.5.15.10, January 20223.1.3, April 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2; enterprise edition availableOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, C++, .NetC++ and CUDAJava
Server operating systemsLinux
OS X
Solaris
Windows
LinuxAll OS with a Java VM
Data schemeyesyesyes
Typing infopredefined data types such as float or dateyesyesyes
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.yesno
Secondary indexesyesnoyes
SQL infoSupport of SQLANSI-99 for query and DML statements, subset of DDLyesSQL-like DML and DDL statements
APIs and other access methodsHDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
JDBC
ODBC
Thrift
Vega
JDBC
ODBC
Thrift
Supported programming languagesC#
C++
Java
PHP
Python
Ruby
Scala
All languages supporting JDBC/ODBC/Thrift
Python
C++
Java
PHP
Python
Server-side scripts infoStored proceduresyes (compute grid and cache interceptors can be used instead)noyes infouser defined functions and integration of map-reduce
Triggersyes (cache interceptors and events)nono
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoRound robinSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes (replicated cache)Multi-source replicationselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes (compute grid and hadoop accelerator)noyes infoquery execution via MapReduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes
User concepts infoAccess controlSecurity Hooks for custom implementationsfine grained access rights according to SQL-standardAccess rights for users, groups and roles

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More resources
GridGainHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Hive
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