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DBMS > Atos Standard Common Repository vs. Spark SQL vs. VictoriaMetrics

System Properties Comparison Atos Standard Common Repository vs. Spark SQL vs. VictoriaMetrics

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Editorial information provided by DB-Engines
NameAtos Standard Common Repository  Xexclude from comparisonSpark SQL  Xexclude from comparisonVictoriaMetrics  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.
DescriptionHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksSpark SQL is a component on top of 'Spark Core' for structured data processingA fast, cost-effective and scalable Time Series DBMS and monitoring solution
Primary database modelDocument store
Key-value store
Relational DBMSTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score1.32
Rank#162  Overall
#14  Time Series DBMS
Websiteatos.net/en/convergence-creators/portfolio/standard-common-repositoryspark.apache.org/­sqlvictoriametrics.com
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.victoriametrics.com
github.com/­VictoriaMetrics/­VictoriaMetrics/­wiki
DeveloperAtos Convergence CreatorsApache Software FoundationVictoriaMetrics
Initial release201620142018
Current release17033.5.0 ( 2.13), September 2023v1.91, May 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaScalaGo
Server operating systemsLinuxLinux
OS X
Windows
FreeBSD
Linux
macOS
OpenBSD
Data schemeSchema and schema-less with LDAP viewsyes
Typing infopredefined data types such as float or dateoptionalyes
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.yesnono
Secondary indexesyesno
SQL infoSupport of SQLnoSQL-like DML and DDL statementsno
APIs and other access methodsLDAPJDBC
ODBC
Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write
Supported programming languagesAll languages with LDAP bindingsJava
Python
R
Scala
Server-side scripts infoStored proceduresnonono
Triggersyesnono
Partitioning methods infoMethods for storing different data on different nodesSharding infocell divisionyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesyesnoneSynchronous replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationEventual Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic execution of specific operationsnono
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnono
User concepts infoAccess controlLDAP bind authenticationno

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More resources
Atos Standard Common RepositorySpark SQLVictoriaMetrics
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