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DBMS > Hive vs. KeyDB vs. MySQL vs. Spark SQL

System Properties Comparison Hive vs. KeyDB vs. MySQL vs. Spark SQL

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Editorial information provided by DB-Engines
NameHive  Xexclude from comparisonKeyDB  Xexclude from comparisonMySQL  Xexclude from comparisonSpark SQL  Xexclude from comparison
Descriptiondata warehouse software for querying and managing large distributed datasets, built on HadoopAn ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocolsWidely used open source RDBMSSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSKey-value storeRelational DBMS infoKey/Value like access via memcached APIRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score64.82
Rank#18  Overall
#12  Relational DBMS
Score0.94
Rank#205  Overall
#30  Key-value stores
Score1101.50
Rank#2  Overall
#2  Relational DBMS
Score19.56
Rank#34  Overall
#21  Relational DBMS
Websitehive.apache.orggithub.com/­Snapchat/­KeyDB
keydb.dev
www.mysql.comspark.apache.org/­sql
Technical documentationcwiki.apache.org/­confluence/­display/­Hive/­Homedocs.keydb.devdev.mysql.com/­docspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software Foundation infoinitially developed by FacebookEQ Alpha Technology Ltd.Oracle infosince 2010, originally MySQL AB, then SunApache Software Foundation
Initial release2012201919952014
Current release3.1.3, April 20228.3.0, January 20243.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoBSD-3Open Source infoGPL version 2. Commercial licenses with extended functionallity are availableOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Aiven for MySQL: Fully managed MySQL, deployable in the cloud of your choice, with seamless integrations and lightning-fast setup.
Implementation languageJavaC++C and C++Scala
Server operating systemsAll OS with a Java VMLinuxFreeBSD
Linux
OS X
Solaris
Windows
Linux
OS X
Windows
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyespartial infoSupported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexesyesyes
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.noyesno
Secondary indexesyesyes infoby using the Redis Search moduleyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsnoyes infowith proprietary extensionsSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
Thrift
Proprietary protocol infoRESP - REdis Serialization ProtocoADO.NET
JDBC
ODBC
Proprietary native API
JDBC
ODBC
Supported programming languagesC++
Java
PHP
Python
C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic
Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
Java
Python
R
Scala
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceLuayes infoproprietary syntaxno
Triggersnonoyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardinghorizontal partitioning, sharding with MySQL Cluster or MySQL Fabricyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorMulti-source replication
Source-replica replication
Multi-source replication
Source-replica replication
none
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyEventual Consistency
Strong eventual consistency with CRDTs
Immediate Consistency
Foreign keys infoReferential integritynonoyes infonot for MyISAM storage engineno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoOptimistic locking, atomic execution of commands blocks and scriptsACID infonot for MyISAM storage engineno
Concurrency infoSupport for concurrent manipulation of datayesyesyes infotable locks or row locks depending on storage engineyes
Durability infoSupport for making data persistentyesyes infoConfigurable mechanisms for persistency via snapshots and/or operations logsyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesno
User concepts infoAccess controlAccess rights for users, groups and rolessimple password-based access control and ACLUsers with fine-grained authorization concept infono user groups or rolesno

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