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DBMS > ClickHouse vs. Kinetica vs. Vertica

System Properties Comparison ClickHouse vs. Kinetica vs. Vertica

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Editorial information provided by DB-Engines
NameClickHouse  Xexclude from comparisonKinetica  Xexclude from comparisonVertica infoVertica Analytics Platform  Xexclude from comparison
DescriptionColumn-oriented Relational DBMS powering YandexGPU-accelerated database for real-time analysis of large and streaming datasetsColumnar relational DBMS designed to handle modern analytic workloads, enabling fast query performance
Primary database modelRelational DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score7.61
Rank#54  Overall
#31  Relational DBMS
Score0.76
Rank#193  Overall
#97  Relational DBMS
Score21.21
Rank#32  Overall
#19  Relational DBMS
Websiteclickhouse.techwww.kinetica.comwww.vertica.com
Technical documentationclickhouse.tech/­docswww.kinetica.com/­docswww.vertica.com/­docs/­10.0.x/­HTML/­Content/­Home.htm
DeveloperYandex LLCKineticaVertica / Micro Focus infoprior to that Hewlett Packard
Initial release201620122005
Current releasev20.11.3.3-stable, November 20206.0Vertica 10.0 Software, May 2020
Vertica for SQL on Hadoop 10.0 Software, May 2020
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialcommercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenonono infoAvailable on all major clouds - Amazon AWS, Microsoft Azure and Google Cloud Platform
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++C, C++
Server operating systemsFreeBSD
Linux
macOS
LinuxLinux
Data schemeyesyesyes infoUnstructured data can be stored in specific Flex-Tables
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.nonoyes
Secondary indexesnoyesno infoThe concept of 'projections' can be used instead of indexes.
SQL infoSupport of SQLClose to ANSI SQLSQL-like DML and DDL statementsyes infonearly full SQL99 without support for indexes and triggers
APIs and other access methodsHTTP REST
JDBC
ODBC
Proprietary protocol
JDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
Kafka
ODBC
Proprietary protocol
RESTful HTTP API
Supported programming languagesC# info3rd party library
C++
Elixir info3rd party library
Go info3rd party library
Java info3rd party library
JavaScript (Node.js) info3rd party library
Kotlin info3rd party library
Nim info3rd party library
Perl info3rd party library
PHP info3rd party library
Python info3rd party library
R info3rd party library
Ruby info3rd party library
Scala info3rd party library
C++
Java
JavaScript (Node.js)
Python
C++
Java
Perl
Python
R
Server-side scripts infoStored proceduresnouser defined functionsyes
Triggersnoyes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoSegmentation, Projection Segmentation, Hash Segmentation
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationMulti-source replication infoCluster level and Object level replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes infowith Vertica Connector for Hadoop
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integritynoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID
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.yesyes infoGPU vRAM or System RAM
User concepts infoAccess controlAccess rights for users and rolesAccess 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
ClickHouseKineticaVertica infoVertica Analytics Platform
Specific characteristicsColumnar storage, MPP, Hybrid storage model, Aggressive data compression, High availability,...
» more
Competitive advantagesPerformance at exabyte scale, Deploy anywhere, Open source innovation, Analysis in...
» more
Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more
Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more
Licensing and pricing modelsCost-based models and subscription-based models are available
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
ClickHouseKineticaVertica infoVertica Analytics Platform
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