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DBMS > Datomic vs. Kinetica vs. Vertica vs. VictoriaMetrics

System Properties Comparison Datomic vs. Kinetica vs. Vertica vs. VictoriaMetrics

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Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonKinetica  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparisonVictoriaMetrics  Xexclude from comparison
DescriptionDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityFully vectorized database across both GPUs and CPUsCloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.A fast, cost-effective and scalable Time Series DBMS and monitoring solution
Primary database modelRelational DBMSRelational DBMSRelational DBMS infoColumn orientedTime Series DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.66
Rank#144  Overall
#66  Relational DBMS
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score10.06
Rank#42  Overall
#26  Relational DBMS
Score1.23
Rank#172  Overall
#15  Time Series DBMS
Websitewww.datomic.comwww.kinetica.comwww.vertica.comvictoriametrics.com
Technical documentationdocs.datomic.comdocs.kinetica.comvertica.com/­documentationdocs.victoriametrics.com
github.com/­VictoriaMetrics/­VictoriaMetrics/­wiki
DeveloperCognitectKineticaOpenText infopreviously Micro Focus and Hewlett PackardVictoriaMetrics
Initial release2012201220052018
Current release1.0.6735, June 20237.1, August 202112.0.3, January 2023v1.91, May 2023
License infoCommercial or Open Sourcecommercial infolimited edition freecommercialcommercial infoLimited community edition freeOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenonono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containersno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, ClojureC, C++C++Go
Server operating systemsAll OS with a Java VMLinuxLinuxFreeBSD
Linux
macOS
OpenBSD
Data schemeyesyesYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.
Typing infopredefined data types such as float or dateyesyesyes
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.nononono
Secondary indexesyesyesNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLnoSQL-like DML and DDL statementsFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.no
APIs and other access methodsRESTful HTTP APIJDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write
Supported programming languagesClojure
Java
C++
Java
JavaScript (Node.js)
Python
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresyes infoTransaction Functionsuser defined functionsyes, PostgreSQL PL/pgSQL, with minor differencesno
TriggersBy using transaction functionsyes infotriggers when inserted values for one or more columns fall within a specified rangeyes, called Custom Alertsno
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersShardinghorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesnone infoBut extensive use of caching in the application peersSource-replica replicationMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.Synchronous replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono infoBi-directional Spark integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynoyesyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes inforecommended only for testing and developmentyes infoGPU vRAM or System RAMnono
User concepts infoAccess controlnoAccess rights for users and roles on table levelfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
DatomicKineticaVertica infoOpenText™ Vertica™VictoriaMetrics
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
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Competitive advantagesFast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
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Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
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DatomicKineticaVertica infoOpenText™ Vertica™VictoriaMetrics
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