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DBMS > GeoSpock vs. Hive vs. NuoDB vs. PostgreSQL vs. Spark SQL

System Properties Comparison GeoSpock vs. Hive vs. NuoDB vs. PostgreSQL vs. Spark SQL

Editorial information provided by DB-Engines
NameGeoSpock  Xexclude from comparisonHive  Xexclude from comparisonNuoDB  Xexclude from comparisonPostgreSQL  Xexclude from comparisonSpark SQL  Xexclude from comparison
GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.
DescriptionSpatial and temporal data processing engine for extreme data scaledata warehouse software for querying and managing large distributed datasets, built on HadoopNuoDB is a webscale distributed database that supports SQL and ACID transactionsWidely 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 DBMSRelational DBMSRelational DBMSRelational 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 modelsTime Series DBMSDocument 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
Score59.76
Rank#18  Overall
#12  Relational DBMS
Score0.94
Rank#197  Overall
#92  Relational DBMS
Score636.25
Rank#4  Overall
#4  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitegeospock.comhive.apache.orgwww.3ds.com/­nuodb-distributed-sql-databasewww.postgresql.orgspark.apache.org/­sql
Technical documentationcwiki.apache.org/­confluence/­display/­Hive/­Homedoc.nuodb.comwww.postgresql.org/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperGeoSpockApache Software Foundation infoinitially developed by FacebookDassault Systèmes infooriginally NuoDB, Inc.PostgreSQL Global Development Group infowww.postgresql.org/­developerApache Software Foundation
Initial release201220131989 info1989: Postgres, 1996: PostgreSQL2014
Current release2.0, September 20193.1.3, April 202216.3, May 20243.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2commercial infolimited edition freeOpen Source infoBSDOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnononono
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Implementation languageJava, JavascriptJavaC++CScala
Server operating systemshostedAll OS with a Java VMhosted infoAmazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows
FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows
Linux
OS X
Windows
Data schemeyesyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyesyes
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.nonoyes infospecific XML-type available, but no XML query functionality.no
Secondary indexestemporal, categoricalyesyesyesno
SQL infoSupport of SQLANSI SQL for query only (using Presto)SQL-like DML and DDL statementsyesyes infostandard with numerous extensionsSQL-like DML and DDL statements
APIs and other access methodsJDBCJDBC
ODBC
Thrift
ADO.NET
JDBC
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
Supported programming languagesC++
Java
PHP
Python
.Net
C
C++
Go
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
.Net
C
C++
Delphi
Java infoJDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Java
Python
R
Scala
Server-side scripts infoStored proceduresnoyes infouser defined functions and integration of map-reduceJava, SQLuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.no
Triggersnonoyesyesno
Partitioning methods infoMethods for storing different data on different nodesAutomatic shardingShardingdata is dynamically stored/cached on the nodes where it is read/writtenpartitioning by range, list and (since PostgreSQL 11) by hashyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryes infoManaged transparently by NuoDBSource-replica replication infoother methods possible by using 3rd party extensionsnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infoquery execution via MapReducenono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID infotunable commit protocolACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyes infoMVCCyesyes
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.noyes infoTemporary tablenono
User concepts infoAccess controlAccess rights for users can be defined per tableAccess rights for users, groups and rolesStandard SQL roles/ privileges, Administrative Usersfine grained access rights according to SQL-standardno

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More resources
GeoSpockHiveNuoDBPostgreSQLSpark SQL
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