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DBMS > ClickHouse vs. MongoDB vs. Spark SQL

System Properties Comparison ClickHouse vs. MongoDB vs. Spark SQL

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Editorial information provided by DB-Engines
NameClickHouse  Xexclude from comparisonMongoDB  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionColumn-oriented Relational DBMS powering YandexOne of the most popular document stores available both as a fully managed cloud service and for deployment on self-managed infrastructureSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSDocument storeRelational DBMS
Secondary database modelsTime Series DBMSSpatial DBMS
Search engine infointegrated Lucene index, currently in MongoDB Atlas only.
Time Series DBMS infoTime Series Collections introduced in Release 5.0
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.18
Rank#46  Overall
#30  Relational DBMS
Score477.66
Rank#5  Overall
#1  Document stores
Score23.72
Rank#35  Overall
#21  Relational DBMS
Websiteclickhouse.techwww.mongodb.comspark.apache.org/­sql
Technical documentationclickhouse.tech/­docsdocs.mongodb.com/­manualspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperClickhouse Inc.MongoDB, IncApache Software Foundation
Initial release201620092014
Current releasev22.6.3.35-stable, July 20226.0.0, July 20223.3.0, June 2022
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoMongoDB Inc.'s Server Side Public License v1. Prior versions were published under GNU AGPL v3.0. Commercial licenses are also available.Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenono infoMongoDB available as DBaaS (MongoDB Atlas)no
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
  • MongoDB Atlas: Global multi-cloud database with unmatched data distribution and mobility across AWS, Azure, and Google Cloud, built-in automation for resource and workload optimization, and so much more.
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Implementation languageC++C++Scala
Server operating systemsFreeBSD
Linux
macOS
Linux
OS X
Solaris
Windows
Linux
OS X
Windows
Data schemeyesschema-free infoAlthough schema-free, documents of the same collection often follow the same structure. Optionally impose all or part of a schema by defining a JSON schema.yes
Typing infopredefined data types such as float or dateyesyes infostring, integer, double, decimal, boolean, date, object_id, geospatialyes
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 indexesnoyesno
SQL infoSupport of SQLClose to ANSI SQLRead-only SQL queries via the MongoDB Connector for BISQL-like DML and DDL statements
APIs and other access methodsHTTP REST
JDBC
ODBC
Proprietary protocol
proprietary protocol using JSONJDBC
ODBC
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
Actionscript infounofficial driver
C
C#
C++
Clojure infounofficial driver
ColdFusion infounofficial driver
D infounofficial driver
Dart infounofficial driver
Delphi infounofficial driver
Erlang
Go
Groovy infounofficial driver
Haskell
Java
JavaScript
Lisp infounofficial driver
Lua infounofficial driver
MatLab infounofficial driver
Perl
PHP
PowerShell infounofficial driver
Prolog infounofficial driver
Python
R infounofficial driver
Ruby
Rust
Scala
Smalltalk infounofficial driver
Swift
Java
Python
R
Scala
Server-side scripts infoStored proceduresnoJavaScriptno
Triggersnoyes infoin MongoDB Atlas onlyno
Partitioning methods infoMethods for storing different data on different nodesSharding infoPartitioned by hashed, ranged, or zoned sharding keys. Live resharding allows users to change their shard keys as an online operation with zero downtime.yes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-Source deployments with MongoDB Atlas Global Clusters
Source-replica replication
none
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency infocan be individually decided for each write operation
Foreign keys infoReferential integritynono infotypically not used, however similar functionality with DBRef possibleno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoMulti-document ACID Transactions with snapshot isolationno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyes infooptional, enabled by defaultyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes infoIn-memory storage engine introduced with MongoDB version 3.2no
User concepts infoAccess controlAccess rights for users and rolesAccess rights for users and rolesno
More information provided by the system vendor
ClickHouseMongoDBSpark SQL
Specific characteristicsMongoDB’s application data platform provides developers a unified interface to power...
» more
Competitive advantagesThe MongoDB platform can be used by developers building transactional, operational,...
» more
Typical application scenariosInternet of Things and Time Series (Bosch, Jaguar Land Rover) Mobile (The Weather...
» more
Key customersADP, Adobe, Amadeus, AstraZeneca, Auto Trader, Barclays, BBVA, Bosch, Cisco, CERN,...
» more
Market metrics200+ million downloads, over 1.5M Atlas database clusters under management handling...
» more
Licensing and pricing modelsMongoDB database server: Server-Side Public License (SSPL) . Commercial licenses...
» more

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More resources
ClickHouseMongoDBSpark SQL
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