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DBMS > Amazon Aurora vs. Apache Spark (SQL) vs. TDSQL for MySQL

System Properties Comparison Amazon Aurora vs. Apache Spark (SQL) vs. TDSQL for MySQL

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Editorial information provided by DB-Engines
NameAmazon Aurora  Xexclude from comparisonApache Spark (SQL)  Xexclude from comparisonTDSQL for MySQL  Xexclude from comparison
DescriptionMySQL and PostgreSQL compatible cloud service by AmazonApache Spark SQL is a component on top of 'Spark Core' for structured data processingA high-performance distributed database management system with features such as automatic sharding, intelligent operation and maintenance, elastic scalability without downtime, and enterprise-grade security. It is highly compatible with MySQL.
Primary database modelRelational DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument storeDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score7.48
Rank#45  Overall
#28  Relational DBMS
Score17.39
Rank#32  Overall
#20  Relational DBMS
Score0.94
Rank#193  Overall
#89  Relational DBMS
Websiteaws.amazon.com/­rds/­auroraspark.apache.org/­sqlwww.tencentcloud.com/­products/­dcdb
Technical documentationdocs.aws.amazon.com/­AmazonRDS/­latest/­AuroraUserGuide/­CHAP_Aurora.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.htmlwww.tencentcloud.com/­document/­product/­1042
DeveloperAmazonApache Software FoundationTencent
Initial release201520142013
Current release3.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercial
Cloud-based only infoOnly available as a cloud serviceyesnoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageScala
Server operating systemshostedLinux
OS X
Windows
hosted
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.yesnono
Secondary indexesyesnoyes
SQL infoSupport of SQLyesSQL-like DML and DDL statementsyes
APIs and other access methodsADO.NET
JDBC
ODBC
JDBC
ODBC
JDBC
ODBC
Supported programming languagesAda
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
C
C#
C++
Java
PHP
Python
Ruby
Server-side scripts infoStored proceduresyesnoyes
Triggersyesnoyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningyes, utilizing Spark CoreAutomatic sharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationnoneMulti-source replication
Source-replica replication
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 integrityyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACID
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.yesnono
User concepts infoAccess controlfine grained access rights according to SQL-standardnoUsers with fine-grained authorization concept

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More resources
Amazon AuroraApache Spark (SQL)TDSQL for MySQL
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