DB-EnginesInfluxDB: Focus on building software with an easy-to-use serverless, scalable time series platformEnglish
Deutsch
Knowledge Base of Relational and NoSQL Database Management Systemsprovided by solid IT

DBMS > Drizzle vs. eXtremeDB vs. Hawkular Metrics vs. Microsoft Azure Table Storage

System Properties Comparison Drizzle vs. eXtremeDB vs. Hawkular Metrics vs. Microsoft Azure Table Storage

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines
NameDrizzle  Xexclude from comparisoneXtremeDB  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonMicrosoft Azure Table Storage  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.Natively in-memory DBMS with options for persistency, high-availability and clusteringHawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.A Wide Column Store for rapid development using massive semi-structured datasets
Primary database modelRelational DBMSRelational DBMS
Time Series DBMS
Time Series DBMSWide column store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.80
Rank#214  Overall
#99  Relational DBMS
#18  Time Series DBMS
Score0.08
Rank#366  Overall
#39  Time Series DBMS
Score4.04
Rank#77  Overall
#6  Wide column stores
Websitewww.mcobject.comwww.hawkular.orgazure.microsoft.com/­en-us/­services/­storage/­tables
Technical documentationwww.mcobject.com/­docs/­extremedb.htmwww.hawkular.org/­hawkular-metrics/­docs/­user-guide
DeveloperDrizzle project, originally started by Brian AkerMcObjectCommunity supported by Red HatMicrosoft
Initial release2008200120142012
Current release7.2.4, September 20128.2, 2021
License infoCommercial or Open SourceOpen Source infoGNU GPLcommercialOpen Source infoApache 2.0commercial
Cloud-based only infoOnly available as a cloud servicenononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++C and C++Java
Server operating systemsFreeBSD
Linux
OS X
AIX
HP-UX
Linux
macOS
Solaris
Windows
Linux
OS X
Windows
hosted
Data schemeyesyesschema-freeschema-free
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.no infosupport of XML interfaces availablenono
Secondary indexesyesyesnono
SQL infoSupport of SQLyes infowith proprietary extensionsyes infowith the option: eXtremeSQLnono
APIs and other access methodsJDBC.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API
HTTP RESTRESTful HTTP API
Supported programming languagesC
C++
Java
PHP
.Net
C
C#
C++
Java
Lua
Python
Scala
Go
Java
Python
Ruby
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresnoyesnono
Triggersno infohooks for callbacks inside the server can be used.yes infoby defining eventsyes infovia Hawkular Alertingno
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioning / shardingSharding infobased on CassandraSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Active Replication Fabric™ for IoT
Multi-source replication infoby means of eXtremeDB Cluster option
Source-replica replication infoby means of eXtremeDB High Availability option
selectable replication factor infobased on Cassandrayes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate Consistency
Foreign keys infoReferential integrityyesyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnooptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyes infoOptimistic (MVCC) and pessimistic (locking) strategies availableyesyes
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.yesnono
User concepts infoAccess controlPluggable authentication mechanisms infoe.g. LDAP, HTTPnoAccess rights based on private key authentication or shared access signatures
More information provided by the system vendor
DrizzleeXtremeDBHawkular MetricsMicrosoft Azure Table Storage
Specific characteristicseXtremeDB is an in-memory and/or persistent database system that offers an ultra-small...
» more
Competitive advantageseXtremeDB databases can be modeled relationally or as objects and can utilize SQL...
» more
Typical application scenariosIoT application across all markets: Industrial Control, Netcom, Telecom, Defense,...
» more
Key customersSchneider Electronics, F5 Networks, TNS, Boeing, Northrop Grumman, GoPro, ViaSat,...
» more
Market metricsWith hundreds of customers and over 30 million devices/applications using the product...
» more
Licensing and pricing modelsFor server use cases, there is a simple per-server license irrespective of the number...
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources
DrizzleeXtremeDBHawkular MetricsMicrosoft Azure Table Storage
DB-Engines blog posts

MySQL won the April ranking; did its forks follow?
1 April 2015, Paul Andlinger

Has MySQL finally lost its mojo?
1 July 2013, Matthias Gelbmann

show all

Recent citations in the news

Latest embedded DBMS supports asymmetric multiprocessing systems
24 May 2023, Embedded

McObject Delivers eXtremeDB 8.4 Improving Performance, Security, and Developer Productivity
13 May 2024, Embedded Computing Design

McObject LLC Joins STMicroelectronics Partner Program to Expand, Enhance and Accelerate Customer
6 June 2024, EIN News

The Data in Hard Real-time SCADA Systems Lets Companies Do More with Less
11 August 2023, Automation.com

McObject Announces the Release of eXtremeDB/rt 1.2
23 May 2023, Embedded Computing Design

provided by Google News

Waiting for Red Hat OpenShift 4.0? Too late, 4.1 has already arrived… • DEVCLASS
5 June 2019, DevClass

provided by Google News

Working with Azure to Use and Manage Data Lakes
7 March 2024, Simplilearn

How to use Azure Table storage in .Net
14 January 2019, InfoWorld

How to Use C# Azure.Data.Tables SDK with Azure Cosmos DB
9 July 2021, hackernoon.com

Inside Azure File Storage
7 October 2015, azure.microsoft.com

How to write data to Azure Table Store with an Azure Function
14 April 2017, Experts Exchange

provided by Google News



Share this page

Featured Products

Milvus logo

Vector database designed for GenAI, fully equipped for enterprise implementation.
Try Managed Milvus for Free

Datastax Astra logo

Bring all your data to Generative AI applications with vector search enabled by the most scalable
vector database available.
Try for Free

Neo4j logo

See for yourself how a graph database can make your life easier.
Use Neo4j online for free.

Present your product here