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

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

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonGridDB  Xexclude from comparisonGridGain  Xexclude from comparisonKinetica  Xexclude from comparison
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 DataGridGain is an in-memory computing platform, built on Apache IgniteFully vectorized database across both GPUs and CPUs
Primary database modelRelational DBMS
Time Series DBMS
Time Series DBMSKey-value store
Relational DBMS
Relational 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
Score1.53
Rank#155  Overall
#26  Key-value stores
#73  Relational DBMS
Score0.69
Rank#234  Overall
#107  Relational DBMS
Websitedruid.apache.orggriddb.netwww.gridgain.comwww.kinetica.com
Technical documentationdruid.apache.org/­docs/­latest/­designdocs.griddb.netwww.gridgain.com/­docs/­index.htmldocs.kinetica.com
DeveloperApache Software Foundation and contributorsToshiba CorporationGridGain Systems, Inc.Kinetica
Initial release2012201320072012
Current release29.0.1, April 20245.1, August 2022GridGain 8.5.17.1, 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 availablecommercialcommercial
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++Java, C++, .NetC, C++
Server operating systemsLinux
OS X
Unix
LinuxLinux
OS X
Solaris
Windows
Linux
Data schemeyes infoschema-less columns are supportedyesyesyes
Typing infopredefined data types such as float or dateyesyes infonumerical, string, blob, geometry, boolean, timestampyesyes
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.nonoyesno
Secondary indexesyesyesyesyes
SQL infoSupport of SQLSQL for queryingSQL92, SQL-like TQL (Toshiba Query Language)ANSI-99 for query and DML statements, subset of DDLSQL-like DML and DDL statements
APIs and other access methodsJDBC
RESTful HTTP/JSON API
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
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#
C++
Java
PHP
Python
Ruby
Scala
C++
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresnonoyes (compute grid and cache interceptors can be used instead)user defined functions
Triggersnoyesyes (cache interceptors and events)yes 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 replicationyes (replicated cache)Source-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 jobsyes (compute grid and hadoop accelerator)no
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 levelACIDno
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.noyesyesyes 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 databaseSecurity Hooks for custom implementationsAccess rights for users and roles on table level
More information provided by the system vendor
Apache DruidGridDBGridGainKinetica
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 DruidGridDBGridGainKinetica
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