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DBMS > Apache IoTDB vs. GBase vs. Hazelcast vs. Kdb vs. Linter

System Properties Comparison Apache IoTDB vs. GBase vs. Hazelcast vs. Kdb vs. Linter

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonGBase  Xexclude from comparisonHazelcast  Xexclude from comparisonKdb  Xexclude from comparisonLinter  Xexclude from comparison
DescriptionAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkAn analytical database for business intelligence with large customers in China.A widely adopted in-memory data gridHigh performance Time Series DBMSRDBMS for high security requirements
Primary database modelTime Series DBMSRelational DBMSKey-value storeTime Series DBMS
Vector DBMS
Relational DBMS
Secondary database modelsDocument store infoJSON support with IMDG 3.12Relational DBMSSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.19
Rank#176  Overall
#15  Time Series DBMS
Score1.04
Rank#190  Overall
#88  Relational DBMS
Score6.87
Rank#55  Overall
#6  Key-value stores
Score7.70
Rank#53  Overall
#3  Time Series DBMS
#1  Vector DBMS
Score0.10
Rank#350  Overall
#153  Relational DBMS
Websiteiotdb.apache.orgwww.gbase.cnhazelcast.comkx.comlinter.ru
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlhazelcast.org/­imdg/­docscode.kx.com
DeveloperApache Software FoundationHazelcastKx Systems, a division of First Derivatives plcrelex.ru
Initial release201820082000 infokdb was released 2000, kdb+ in 20031990
Current release1.1.0, April 2023GBase 8a5.3.6, November 20233.6, May 2018
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache Version 2; commercial licenses availablecommercial infofree 32-bit versioncommercial
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaqC and C++
Server operating systemsAll OS with a Java VM (>= 1.8)All OS with a Java VMLinux
OS X
Solaris
Windows
AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesyesyes
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 strategyyesno
Secondary indexesyesyesyes infotable attribute 'grouped'yes
SQL infoSupport of SQLSQL-like query languageSQL-like query languageSQL-like query language (q)yes
APIs and other access methodsJDBC
Native API
JCache
JPA
Memcached protocol
RESTful HTTP API
HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket
ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala
C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl
Server-side scripts infoStored proceduresyesyes infoEvent Listeners, Executor Servicesuser defined functionsyes infoproprietary syntax with the possibility to convert from PL/SQL
Triggersyesyes infoEventsyes infowith viewsyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)Shardinghorizontal partitioningnone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasyes infoReplicated MapSource-replica replicationSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparkyesno infosimilar paradigm used for internal processingno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoone or two-phase-commit; repeatable reads; read commitednoACID
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.yesyesyes
User concepts infoAccess controlyesRole-based access controlrights management via user accountsfine grained access rights according to SQL-standard
More information provided by the system vendor
Apache IoTDBGBaseHazelcastKdbLinter
Specific characteristicsIntegrated columnar database & programming system for streaming, real time and historical...
» more
Competitive advantagesprovides seamless scalability; runs on industry standard server platforms; is top-ranked...
» more
Typical application scenariostick database streaming sensor data massive intelligence applications oil and gas...
» more
Key customersGoldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more
Market metricskdb+ performance and reliability proven by our customers in critical infrastructure...
» more
Licensing and pricing modelsupon request
» more

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More resources
Apache IoTDBGBaseHazelcastKdbLinter
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