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DBMS > atoti vs. GridDB vs. Hive vs. Ignite vs. Tkrzw

System Properties Comparison atoti vs. GridDB vs. Hive vs. Ignite vs. Tkrzw

Editorial information provided by DB-Engines
Nameatoti  Xexclude from comparisonGridDB  Xexclude from comparisonHive  Xexclude from comparisonIgnite  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
DescriptionAn in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.Scalable in-memory time series database optimized for IoT and Big Datadata warehouse software for querying and managing large distributed datasets, built on HadoopApache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet
Primary database modelObject oriented DBMSTime Series DBMSRelational DBMSKey-value store
Relational DBMS
Key-value store
Secondary database modelsKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.56
Rank#245  Overall
#10  Object oriented DBMS
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score61.17
Rank#18  Overall
#12  Relational DBMS
Score3.16
Rank#96  Overall
#15  Key-value stores
#49  Relational DBMS
Score0.00
Rank#383  Overall
#60  Key-value stores
Websiteatoti.iogriddb.nethive.apache.orgignite.apache.orgdbmx.net/­tkrzw
Technical documentationdocs.atoti.iodocs.griddb.netcwiki.apache.org/­confluence/­display/­Hive/­Homeapacheignite.readme.io/­docs
DeveloperActiveViamToshiba CorporationApache Software Foundation infoinitially developed by FacebookApache Software FoundationMikio Hirabayashi
Initial release2013201220152020
Current release5.1, August 20223.1.3, April 2022Apache Ignite 2.60.9.3, August 2020
License infoCommercial or Open Sourcecommercial infofree versions availableOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoApache Version 2Open Source infoApache 2.0Open Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++JavaC++, Java, .NetC++
Server operating systemsLinuxAll OS with a Java VMLinux
OS X
Solaris
Windows
Linux
macOS
Data schemeyesyesyesschema-free
Typing infopredefined data types such as float or dateyes infonumerical, string, blob, geometry, boolean, timestampyesyesno
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.noyesno
Secondary indexesyesyesyes
SQL infoSupport of SQLMultidimensional Expressions (MDX)SQL92, SQL-like TQL (Toshiba Query Language)SQL-like DML and DDL statementsANSI-99 for query and DML statements, subset of DDLno
APIs and other access methodsJDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
JDBC
ODBC
Thrift
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
Supported programming languagesC
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C++
Java
PHP
Python
C#
C++
Java
PHP
Python
Ruby
Scala
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresPythonnoyes infouser defined functions and integration of map-reduceyes (compute grid and cache interceptors can be used instead)no
Triggersyesnoyes (cache interceptors and events)no
Partitioning methods infoMethods for storing different data on different nodesSharding, horizontal partitioningShardingShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationselectable replication factoryes (replicated cache)none
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsyes infoquery execution via MapReduceyes (compute grid and hadoop accelerator)no
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency within container, eventual consistency across containersEventual ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID at container levelnoACID
Concurrency infoSupport for concurrent manipulation of datayes, multi-version concurrency control (MVCC)yesyesyesyes
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.yesyesyesyes infousing specific database classes
User concepts infoAccess controlAccess rights for users can be defined per databaseAccess rights for users, groups and rolesSecurity Hooks for custom implementationsno
More information provided by the system vendor
atotiGridDBHiveIgniteTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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
atotiGridDBHiveIgniteTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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