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DBMS > Apache Druid vs. GridGain vs. Hyprcubd vs. Kinetica

System Properties Comparison Apache Druid vs. GridGain vs. Hyprcubd vs. Kinetica

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonGridGain  Xexclude from comparisonHyprcubd  Xexclude from comparisonKinetica  Xexclude from comparison
Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataGridGain is an in-memory computing platform, built on Apache IgniteServerless Time Series DBMSFully vectorized database across both GPUs and CPUs
Primary database modelRelational DBMS
Time Series DBMS
Key-value store
Relational DBMS
Time Series DBMSRelational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.33
Rank#99  Overall
#51  Relational DBMS
#7  Time Series DBMS
Score1.59
Rank#158  Overall
#26  Key-value stores
#73  Relational DBMS
Score0.74
Rank#229  Overall
#105  Relational DBMS
Websitedruid.apache.orgwww.gridgain.comhyprcubd.com (offline)www.kinetica.com
Technical documentationdruid.apache.org/­docs/­latest/­designwww.gridgain.com/­docs/­index.htmldocs.kinetica.com
DeveloperApache Software Foundation and contributorsGridGain Systems, Inc.Hyprcubd, Inc.Kinetica
Initial release201220072012
Current release29.0.0, February 2024GridGain 8.5.17.1, August 2021
License infoCommercial or Open SourceOpen Source infoApache license v2commercialcommercialcommercial
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJava, C++, .NetGoC, C++
Server operating systemsLinux
OS X
Unix
Linux
OS X
Solaris
Windows
hostedLinux
Data schemeyes infoschema-less columns are supportedyesyesyes
Typing infopredefined data types such as float or dateyesyesyes infotime, int, uint, float, stringyes
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.noyesnono
Secondary indexesyesyesnoyes
SQL infoSupport of SQLSQL for queryingANSI-99 for query and DML statements, subset of DDLSQL-like query languageSQL-like DML and DDL statements
APIs and other access methodsJDBC
RESTful HTTP/JSON API
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
gRPC (https)JDBC
ODBC
RESTful HTTP API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
C#
C++
Java
PHP
Python
Ruby
Scala
C++
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresnoyes (compute grid and cache interceptors can be used instead)nouser defined functions
Triggersnoyes (cache interceptors and events)noyes 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-basedShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesyes (replicated cache)Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes (compute grid and hadoop accelerator)nono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesnoyes
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.noyesnoyes infoGPU vRAM or System RAM
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemSecurity Hooks for custom implementationstoken accessAccess rights for users and roles on table level

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More resources
Apache DruidGridGainHyprcubdKinetica
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