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DBMS > Apache Druid vs. GridDB vs. Hypertable vs. Kinetica

System Properties Comparison Apache Druid vs. GridDB vs. Hypertable vs. Kinetica

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonGridDB  Xexclude from comparisonHypertable  Xexclude from comparisonKinetica  Xexclude from comparison
Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataScalable in-memory time series database optimized for IoT and Big DataAn open source BigTable implementation based on distributed file systems such as HadoopFully vectorized database across both GPUs and CPUs
Primary database modelRelational DBMS
Time Series DBMS
Time Series DBMSWide column storeRelational DBMS
Secondary database modelsKey-value store
Relational DBMS
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.29
Rank#95  Overall
#49  Relational DBMS
#7  Time Series DBMS
Score2.02
Rank#132  Overall
#11  Time Series DBMS
Score0.69
Rank#234  Overall
#107  Relational DBMS
Websitedruid.apache.orggriddb.netwww.kinetica.com
Technical documentationdruid.apache.org/­docs/­latest/­designdocs.griddb.netdocs.kinetica.com
DeveloperApache Software Foundation and contributorsToshiba CorporationHypertable Inc.Kinetica
Initial release2012201320092012
Current release29.0.1, April 20245.1, August 20220.9.8.11, March 20167.1, August 2021
License infoCommercial or Open SourceOpen Source infoApache license v2Open Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoGNU version 3. Commercial license availablecommercial
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++C++C, C++
Server operating systemsLinux
OS X
Unix
LinuxLinux
OS X
Windows infoan inofficial Windows port is available
Linux
Data schemeyes infoschema-less columns are supportedyesschema-freeyes
Typing infopredefined data types such as float or dateyesyes infonumerical, string, blob, geometry, boolean, timestampnoyes
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 indexesyesyesrestricted infoonly exact value or prefix value scansyes
SQL infoSupport of SQLSQL for queryingSQL92, SQL-like TQL (Toshiba Query Language)noSQL-like DML and DDL statements
APIs and other access methodsJDBC
RESTful HTTP/JSON API
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
C++ API
Thrift
JDBC
ODBC
RESTful HTTP API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C++
Java
Perl
PHP
Python
Ruby
C++
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresnononouser defined functions
Triggersnoyesnoyes infotriggers when inserted values for one or more columns fall within a specified range
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesSource-replica replicationselectable replication factor on file system levelSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate consistency within container, eventual consistency across containersImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID at container levelnono
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.noyesyes infoGPU vRAM or System RAM
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemAccess rights for users can be defined per databasenoAccess rights for users and roles on table level
More information provided by the system vendor
Apache DruidGridDBHypertableKinetica
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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Apache DruidGridDBHypertableKinetica
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