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DBMS > GridDB vs. Hazelcast vs. Kinetica vs. RRDtool

System Properties Comparison GridDB vs. Hazelcast vs. Kinetica vs. RRDtool

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Editorial information provided by DB-Engines
NameGridDB  Xexclude from comparisonHazelcast  Xexclude from comparisonKinetica  Xexclude from comparisonRRDtool  Xexclude from comparison
DescriptionScalable in-memory time series database optimized for IoT and Big DataA widely adopted in-memory data gridFully vectorized database across both GPUs and CPUsIndustry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database. infoThe data is stored in a circular buffer, thus the system storage footprint remains constant over time.
Primary database modelTime Series DBMSKey-value storeRelational DBMSTime Series DBMS
Secondary database modelsKey-value store
Relational DBMS
Document store infoJSON support with IMDG 3.12Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score5.97
Rank#57  Overall
#6  Key-value stores
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score1.87
Rank#136  Overall
#11  Time Series DBMS
Websitegriddb.nethazelcast.comwww.kinetica.comoss.oetiker.ch/­rrdtool
Technical documentationdocs.griddb.nethazelcast.org/­imdg/­docsdocs.kinetica.comoss.oetiker.ch/­rrdtool/­doc
DeveloperToshiba CorporationHazelcastKineticaTobias Oetiker
Initial release2013200820121999
Current release5.1, August 20225.3.6, November 20237.1, August 20211.8.0, 2022
License infoCommercial or Open SourceOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoApache Version 2; commercial licenses availablecommercialOpen Source infoGPL V2 and FLOSS
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++JavaC, C++C infoImplementations in Java (e.g. RRD4J) and C# available
Server operating systemsLinuxAll OS with a Java VMLinuxHP-UX
Linux
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyes infonumerical, string, blob, geometry, boolean, timestampyesyesNumeric data only
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.noyes infothe object must implement a serialization strategynono infoExporting into and restoring from XML files possible
Secondary indexesyesyesyesno
SQL infoSupport of SQLSQL92, SQL-like TQL (Toshiba Query Language)SQL-like query languageSQL-like DML and DDL statementsno
APIs and other access methodsJDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
JCache
JPA
Memcached protocol
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
in-process shared library
Pipes
Supported programming languagesC
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C++
Java
JavaScript (Node.js)
Python
C infowith librrd library
C# infowith a different implementation of RRDTool
Java infowith a different implementation of RRDTool
JavaScript (Node.js) infowith a different implementation of RRDTool
Lua
Perl
PHP infowith a wrapper library
Python
Ruby
Server-side scripts infoStored proceduresnoyes infoEvent Listeners, Executor Servicesuser defined functionsno
Triggersyesyes infoEventsyes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyes infoReplicated MapSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency within container, eventual consistency across containersImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate Consistency or Eventual Consistency depending on configurationnone
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID at container levelone or two-phase-commit; repeatable reads; read commitednono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes infoby using the rrdcached daemon
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.yesyesyes infoGPU vRAM or System RAMyes
User concepts infoAccess controlAccess rights for users can be defined per databaseRole-based access controlAccess rights for users and roles on table levelno
More information provided by the system vendor
GridDBHazelcastKineticaRRDtool
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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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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GridDBHazelcastKineticaRRDtool
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