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DBMS > Drizzle vs. GridDB vs. Hawkular Metrics vs. HBase

System Properties Comparison Drizzle vs. GridDB vs. Hawkular Metrics vs. HBase

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Editorial information provided by DB-Engines
NameDrizzle  Xexclude from comparisonGridDB  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonHBase  Xexclude from comparison
Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.
DescriptionMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Scalable 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.Wide-column store based on Apache Hadoop and on concepts of BigTable
Primary database modelRelational DBMSTime Series DBMSTime Series DBMSWide column store
Secondary database modelsKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.02
Rank#132  Overall
#11  Time Series DBMS
Score0.04
Rank#374  Overall
#38  Time Series DBMS
Score31.25
Rank#26  Overall
#2  Wide column stores
Websitegriddb.netwww.hawkular.orghbase.apache.org
Technical documentationdocs.griddb.netwww.hawkular.org/­hawkular-metrics/­docs/­user-guidehbase.apache.org/­book.html
DeveloperDrizzle project, originally started by Brian AkerToshiba CorporationCommunity supported by Red HatApache Software Foundation infoApache top-level project, originally developed by Powerset
Initial release2008201320142008
Current release7.2.4, September 20125.1, August 20222.3.4, January 2021
License infoCommercial or Open SourceOpen Source infoGNU GPLOpen 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 2
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C++JavaJava
Server operating systemsFreeBSD
Linux
OS X
LinuxLinux
OS X
Windows
Linux
Unix
Windows infousing Cygwin
Data schemeyesyesschema-freeschema-free, schema definition possible
Typing infopredefined data types such as float or dateyesyes infonumerical, string, blob, geometry, boolean, timestampyesoptions to bring your own types, AVRO
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.nonono
Secondary indexesyesyesnono
SQL infoSupport of SQLyes infowith proprietary extensionsSQL92, SQL-like TQL (Toshiba Query Language)nono
APIs and other access methodsJDBCJDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
HTTP RESTJava API
RESTful HTTP API
Thrift
Supported programming languagesC
C++
Java
PHP
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
Go
Java
Python
Ruby
C
C#
C++
Groovy
Java
PHP
Python
Scala
Server-side scripts infoStored proceduresnononoyes infoCoprocessors in Java
Triggersno infohooks for callbacks inside the server can be used.yesyes infovia Hawkular Alertingyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infobased on CassandraSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Source-replica replicationselectable replication factor infobased on CassandraMulti-source replication
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 jobsnoyes
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 Consistency or Eventual Consistency
Foreign keys infoReferential integrityyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID at container levelnoSingle row ACID (across millions of columns)
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
User concepts infoAccess controlPluggable authentication mechanisms infoe.g. LDAP, HTTPAccess rights for users can be defined per databasenoAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC
More information provided by the system vendor
DrizzleGridDBHawkular MetricsHBase
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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DrizzleGridDBHawkular MetricsHBase
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