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DBMS > Apache Spark (SQL) vs. Citus vs. ClickHouse

System Properties Comparison Apache Spark (SQL) vs. Citus vs. ClickHouse

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Editorial information provided by DB-Engines
NameApache Spark (SQL)  Xexclude from comparisonCitus  Xexclude from comparisonClickHouse  Xexclude from comparison
DescriptionApache Spark SQL is a component on top of 'Spark Core' for structured data processingScalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQLA high-performance, column-oriented SQL DBMS for online analytical processing (OLAP) that uses all available system resources to their full potential to process each analytical query as fast as possible. It is available as both an open-source software and a cloud offering.
Primary database modelRelational DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument storeTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score18.45
Rank#31  Overall
#19  Relational DBMS
Score1.98
Rank#114  Overall
#55  Relational DBMS
Score18.71
Rank#30  Overall
#18  Relational DBMS
Websitespark.apache.org/­sqlwww.citusdata.comclickhouse.com
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.citusdata.comclickhouse.com/­docs
DeveloperApache Software FoundationClickhouse Inc.
Initial release201420102016
Current release3.5.0 ( 2.13), September 20238.1, December 2018v24.6.2.17-stable, July 2024
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoAGPL, commercial license also availableOpen 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 languageScalaCC++
Server operating systemsLinux
OS X
Windows
LinuxFreeBSD
Linux
macOS
Data schemeyesyesyes
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.noyes infospecific XML type available, but no XML query functionalityno
Secondary indexesnoyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsyes infostandard, with numerous extensionsClose to ANSI SQL (SQL/JSON + extensions)
APIs and other access methodsJDBC
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
gRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol
Supported programming languagesJava
Python
R
Scala
.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C# 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
Rust
Scala info3rd party library
Server-side scripts infoStored proceduresnouser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.yes
Triggersnoyesno
Partitioning methods infoMethods for storing different data on different nodesyes, utilizing Spark CoreShardingkey based and custom
Replication methods infoMethods for redundantly storing data on multiple nodesnoneSource-replica replication infoother methods possible by using 3rd party extensionsAsynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDno
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.nonoyes
User concepts infoAccess controlnofine grained access rights according to SQL-standardAccess rights for users and roles. Column and row based policies. Quotas and resource limits. Pluggable authentication with LDAP and Kerberos. Password based, X.509 certificate, and SSH key authentication.

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More resources
Apache Spark (SQL)CitusClickHouse
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