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DBMS > GridDB vs. Hawkular Metrics vs. Hive vs. MarkLogic

System Properties Comparison GridDB vs. Hawkular Metrics vs. Hive vs. MarkLogic

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Editorial information provided by DB-Engines
NameGridDB  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonHive  Xexclude from comparisonMarkLogic  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.data warehouse software for querying and managing large distributed datasets, built on HadoopOperational and transactional Enterprise NoSQL database
Primary database modelTime Series DBMSTime Series DBMSRelational DBMSDocument store
Native XML DBMS
RDF store infoas of version 7
Search engine
Secondary database modelsKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.23
Rank#159  Overall
#11  Time Series DBMS
Score0.02
Rank#340  Overall
#30  Time Series DBMS
Score81.61
Rank#17  Overall
#11  Relational DBMS
Score9.85
Rank#50  Overall
#9  Document stores
#1  Native XML DBMS
#1  RDF stores
#4  Search engines
Websitegriddb.netwww.hawkular.orghive.apache.orgwww.marklogic.com
Technical documentationdocs.griddb.netwww.hawkular.org/­hawkular-metrics/­docs/­user-guidecwiki.apache.org/­confluence/­display/­Hive/­Homedocs.marklogic.com
DeveloperToshiba CorporationCommunity supported by Red HatApache Software Foundation infoinitially developed by FacebookMarkLogic Corp.
Initial release2013201420122001
Current release4.6, February 20213.1.2, August 20199.0, 2017
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 2.0Open Source infoApache Version 2commercial inforestricted free version is 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++JavaJavaC++
Server operating systemsLinuxLinux
OS X
Windows
All OS with a Java VMLinux
OS X
Windows
Data schemeyesschema-freeyesschema-free infoSchema can be enforced
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 indexesyesnoyesyes
SQL infoSupport of SQLSQL92, SQL-like TQL (Toshiba Query Language)noSQL-like DML and DDL statementsyes infoSQL92
APIs and other access methodsProprietary protocolHTTP RESTJDBC
ODBC
Thrift
Java API
Node.js Client API
ODBC
proprietary Optic API infoProprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT
Supported programming languagesC
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
Go
Java
Python
Ruby
C++
Java
PHP
Python
C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
Server-side scripts infoStored proceduresnonoyes infouser defined functions and integration of map-reduceyes infovia XQuery or JavaScript
Triggersyesyes infovia Hawkular Alertingnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infobased on CassandraShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationselectable replication factor infobased on Cassandraselectable replication factoryes
MapReduce infoOffers an API for user-defined Map/Reduce methodsConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsnoyes infoquery execution via MapReduceyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs
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
Eventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID at container levelnonoACID infocan act as a resource manager in an XA/JTA transaction
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.yesnoyes, with Range Indexes
User concepts infoAccess controlAccess rights for users can be defined per databasenoAccess rights for users, groups and rolesRole-based access control at the document and subdocument levels
More information provided by the system vendor
GridDBHawkular MetricsHiveMarkLogic
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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3rd partiesSQLFlow: Provides a visual representation of the overall flow of data. Automated SQL data lineage analysis across Databases, ETL, Business Intelligence, Cloud and Hadoop environments by parsing SQL Script and stored procedure.
» more

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More resources
GridDBHawkular MetricsHiveMarkLogic
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