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DBMS > ClickHouse vs. Google Search Appliance vs. Spark SQL

System Properties Comparison ClickHouse vs. Google Search Appliance vs. Spark SQL

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Editorial information provided by DB-Engines
NameClickHouse  Xexclude from comparisonGoogle Search Appliance  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionColumn-oriented Relational DBMS powering YandexThe Google Search Appliance consists of hardware and software for indexing documents in local networks.Spark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSSearch engineRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.33
Rank#82  Overall
#41  Relational DBMS
Score2.77
Rank#94  Overall
#10  Search engines
Score16.14
Rank#35  Overall
#21  Relational DBMS
Websiteclickhouse.yandexenterprise.google.com/­search/­products/­gsa.htmlspark.apache.org/­sql
Technical documentationclickhouse.yandex/­docssupport.google.com/­gsaspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperYandex LLCGoogleApache Software Foundation
Initial release201620022014
Current releasev19.13.3.26, August 2019v2.4.4, September 2019
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScala
Server operating systemsLinux
OS X
Windows
Data schemeyes
Typing infopredefined data types such as float or dateyes
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
Secondary indexesno
SQL infoSupport of SQLnoSQL-like DML and DDL statements
APIs and other access methodsAdministrative API
Browser interface
HTTP API
JDBC
ODBC
Supported programming languagesJava
Python
R
Scala
Server-side scripts infoStored proceduresno
Triggersno
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesMaster-slave replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsno
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nono
User concepts infoAccess controlUser authentication via various security protocolsno

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More resources
ClickHouseGoogle Search ApplianceSpark SQL
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