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DBMS > Hawkular Metrics vs. Interbase vs. Spark SQL vs. Yaacomo

System Properties Comparison Hawkular Metrics vs. Interbase vs. Spark SQL vs. Yaacomo

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Editorial information provided by DB-Engines
NameHawkular Metrics  Xexclude from comparisonInterbase  Xexclude from comparisonSpark SQL  Xexclude from comparisonYaacomo  Xexclude from comparison
Yaacomo seems to be discontinued and is removed from the DB-Engines ranking
DescriptionHawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.Light-weight proven RDBMS infooriginally from BorlandSpark SQL is a component on top of 'Spark Core' for structured data processingOpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing
Primary database modelTime Series DBMSRelational DBMSRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.08
Rank#366  Overall
#39  Time Series DBMS
Score4.08
Rank#75  Overall
#41  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitewww.hawkular.orgwww.embarcadero.com/­products/­interbasespark.apache.org/­sqlyaacomo.com
Technical documentationwww.hawkular.org/­hawkular-metrics/­docs/­user-guidedocs.embarcadero.com/­products/­interbasespark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperCommunity supported by Red HatEmbarcaderoApache Software FoundationQ2WEB GmbH
Initial release2014198420142009
Current releaseInterBase 2020, December 20193.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialOpen Source infoApache 2.0commercial
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 languageJavaCScala
Server operating systemsLinux
OS X
Windows
Android
iOS
Linux
OS X
Windows
Linux
OS X
Windows
Android
Linux
Windows
Data schemeschema-freeyesyesyes
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.nono infoexport as XML data possiblenono
Secondary indexesnoyesnoyes
SQL infoSupport of SQLnoyesSQL-like DML and DDL statementsyes
APIs and other access methodsHTTP RESTADO.NET
JDBC
ODBC
JDBC
ODBC
JDBC
ODBC
Supported programming languagesGo
Java
Python
Ruby
.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby
Java
Python
R
Scala
Server-side scripts infoStored proceduresnoyes infoInterbase procedure and trigger languageno
Triggersyes infovia Hawkular Alertingyesnoyes
Partitioning methods infoMethods for storing different data on different nodesSharding infobased on Cassandranoneyes, utilizing Spark Corehorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor infobased on CassandraInterbase Change ViewsnoneSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyes infoMultiversion concurreny controlyesyes
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.noyesnoyes
User concepts infoAccess controlnofine grained access rights according to SQL-standardnofine grained access rights according to SQL-standard

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Hawkular MetricsInterbaseSpark SQLYaacomo
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