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DBMS > FatDB vs. Hawkular Metrics vs. Ingres vs. Riak KV vs. Spark SQL

System Properties Comparison FatDB vs. Hawkular Metrics vs. Ingres vs. Riak KV vs. Spark SQL

Editorial information provided by DB-Engines
NameFatDB  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonIngres  Xexclude from comparisonRiak KV  Xexclude from comparisonSpark SQL  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.
DescriptionA .NET NoSQL DBMS that can integrate with and extend SQL Server.Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.Well established RDBMSDistributed, fault tolerant key-value storeSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelDocument store
Key-value store
Time Series DBMSRelational DBMSKey-value store infowith links between data sets and object tags for the creation of secondary indexesRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.00
Rank#379  Overall
#40  Time Series DBMS
Score4.11
Rank#81  Overall
#44  Relational DBMS
Score4.10
Rank#82  Overall
#9  Key-value stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitewww.hawkular.orgwww.actian.com/­databases/­ingresspark.apache.org/­sql
Technical documentationwww.hawkular.org/­hawkular-metrics/­docs/­user-guidedocs.actian.com/­ingreswww.tiot.jp/­riak-docs/­riak/­kv/­latestspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperFatCloudCommunity supported by Red HatActian CorporationOpenSource, formerly Basho TechnologiesApache Software Foundation
Initial release201220141974 infooriginally developed at University Berkely in early 1970s20092014
Current release11.2, May 20223.2.0, December 20223.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercialOpen Source infoApache version 2, commercial enterprise editionOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC#JavaCErlangScala
Server operating systemsWindowsLinux
OS X
Windows
AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows
Linux
OS X
Linux
OS X
Windows
Data schemeschema-freeschema-freeyesschema-freeyes
Typing infopredefined data types such as float or dateyesyesyesnoyes
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.nono infobut tools for importing/exporting data from/to XML-files availablenono
Secondary indexesyesnoyesrestrictedno
SQL infoSupport of SQLno infoVia inetgration in SQL ServernoyesnoSQL-like DML and DDL statements
APIs and other access methods.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
HTTP REST.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)
HTTP API
Native Erlang Interface
JDBC
ODBC
Supported programming languagesC#Go
Java
Python
Ruby
C infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
Java
Python
R
Scala
Server-side scripts infoStored proceduresyes infovia applicationsnoyesErlangno
Triggersyes infovia applicationsyes infovia Hawkular Alertingyesyes infopre-commit hooks and post-commit hooksno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infobased on Cassandrahorizontal partitioning infoIngres Star to access multiple databases simultaneouslySharding infono "single point of failure"yes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorselectable replication factor infobased on CassandraIngres Replicatorselectable replication factornone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnonoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Eventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynonoyesno infolinks between data sets can be storedno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyes infoMVCCyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonono
User concepts infoAccess controlno infoCan implement custom security layer via applicationsnofine grained access rights according to SQL-standardyes, using Riak Securityno

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More resources
FatDBHawkular MetricsIngresRiak KVSpark SQL
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