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DBMS > GridDB vs. Hawkular Metrics vs. Kdb vs. Splice Machine

System Properties Comparison GridDB vs. Hawkular Metrics vs. Kdb vs. Splice Machine

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Editorial information provided by DB-Engines
NameGridDB  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonKdb  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionScalable in-memory time series database optimized for IoT and Big DataHawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.High performance Time Series DBMSOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelTime Series DBMSTime Series DBMSTime Series DBMS
Vector DBMS
Relational DBMS
Secondary database modelsKey-value store
Relational DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score0.00
Rank#379  Overall
#40  Time Series DBMS
Score7.55
Rank#53  Overall
#3  Time Series DBMS
#1  Vector DBMS
Score0.54
Rank#250  Overall
#114  Relational DBMS
Websitegriddb.netwww.hawkular.orgkx.comsplicemachine.com
Technical documentationdocs.griddb.netwww.hawkular.org/­hawkular-metrics/­docs/­user-guidecode.kx.comsplicemachine.com/­how-it-works
DeveloperToshiba CorporationCommunity supported by Red HatKx Systems, a division of First Derivatives plcSplice Machine
Initial release201320142000 infokdb was released 2000, kdb+ in 20032014
Current release5.1, August 20223.6, May 20183.1, March 2021
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 2.0commercial infofree 32-bit versionOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++JavaqJava
Server operating systemsLinuxLinux
OS X
Windows
Linux
OS X
Solaris
Windows
Linux
OS X
Solaris
Windows
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyes infonumerical, string, blob, geometry, boolean, timestampyesyesyes
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.nonoyes
Secondary indexesyesnoyes infotable attribute 'grouped'yes
SQL infoSupport of SQLSQL92, SQL-like TQL (Toshiba Query Language)noSQL-like query language (q)yes
APIs and other access methodsJDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
HTTP RESTHTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket
JDBC
Native Spark Datasource
ODBC
Supported programming languagesC
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
Go
Java
Python
Ruby
C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresnonouser defined functionsyes infoJava
Triggersyesyes infovia Hawkular Alertingyes infowith viewsyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infobased on Cassandrahorizontal partitioningShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationselectable replication factor infobased on CassandraSource-replica replicationMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsnono infosimilar paradigm used for internal processingYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency within container, eventual consistency across containersEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID at container levelnonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)
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.yesnoyesyes
User concepts infoAccess controlAccess rights for users can be defined per databasenorights management via user accountsAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
GridDBHawkular MetricsKdbSplice Machine
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Integrated columnar database & programming system for streaming, real time and historical...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
provides seamless scalability; runs on industry standard server platforms; is top-ranked...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
tick database streaming sensor data massive intelligence applications oil and gas...
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more
Market metricsGitHub trending repository
» more
kdb+ performance and reliability proven by our customers in critical infrastructure...
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more
upon request
» more

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GridDBHawkular MetricsKdbSplice Machine
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