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DBMS > Amazon Aurora vs. Google Cloud Firestore vs. LeanXcale vs. PostgreSQL vs. Spark SQL

System Properties Comparison Amazon Aurora vs. Google Cloud Firestore vs. LeanXcale vs. PostgreSQL vs. Spark SQL

Editorial information provided by DB-Engines
NameAmazon Aurora  Xexclude from comparisonGoogle Cloud Firestore  Xexclude from comparisonLeanXcale  Xexclude from comparisonPostgreSQL  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionMySQL and PostgreSQL compatible cloud service by AmazonCloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilitiesWidely used open source RDBMS infoDeveloped as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQLSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSDocument storeKey-value store
Relational DBMS
Relational DBMS infowith object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.Relational DBMS
Secondary database modelsDocument storeDocument store
Graph DBMS infowith Apache Age
Spatial DBMS
Vector DBMS infowith pgvector extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score7.91
Rank#50  Overall
#32  Relational DBMS
Score7.85
Rank#51  Overall
#8  Document stores
Score0.29
Rank#291  Overall
#41  Key-value stores
#132  Relational DBMS
Score645.54
Rank#4  Overall
#4  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websiteaws.amazon.com/­rds/­aurorafirebase.google.com/­products/­firestorewww.leanxcale.comwww.postgresql.orgspark.apache.org/­sql
Technical documentationdocs.aws.amazon.com/­AmazonRDS/­latest/­AuroraUserGuide/­CHAP_Aurora.htmlfirebase.google.com/­docs/­firestorewww.postgresql.org/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperAmazonGoogleLeanXcalePostgreSQL Global Development Group infowww.postgresql.org/­developerApache Software Foundation
Initial release2015201720151989 info1989: Postgres, 1996: PostgreSQL2014
Current release16.3, May 20243.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source infoBSDOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCScala
Server operating systemshostedhostedFreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows
Linux
OS X
Windows
Data schemeyesschema-freeyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyes
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.yesnoyes infospecific XML-type available, but no XML query functionality.no
Secondary indexesyesyesyesno
SQL infoSupport of SQLyesnoyes infothrough Apache Derbyyes infostandard with numerous extensionsSQL-like DML and DDL statements
APIs and other access methodsADO.NET
JDBC
ODBC
Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API
JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
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
Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python
C
Java
Scala
.Net
C
C++
Delphi
Java infoJDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Java
Python
R
Scala
Server-side scripts infoStored proceduresyesyes, Firebase Rules & Cloud Functionsuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.no
Triggersyesyes, with Cloud Functionsyesno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningShardingpartitioning by range, list and (since PostgreSQL 11) by hashyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationMulti-source replicationSource-replica replication infoother methods possible by using 3rd party extensionsnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoUsing Cloud Dataflownono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnoyesyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDyesACIDACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesnono
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.fine grained access rights according to SQL-standardno

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More resources
Amazon AuroraGoogle Cloud FirestoreLeanXcalePostgreSQLSpark SQL
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