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DBMS > Apache Impala vs. FatDB vs. Ingres vs. PostgreSQL vs. Spark SQL

System Properties Comparison Apache Impala vs. FatDB vs. Ingres vs. PostgreSQL vs. Spark SQL

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonFatDB  Xexclude from comparisonIngres  Xexclude from comparisonPostgreSQL  Xexclude from comparisonSpark SQL  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.
DescriptionAnalytic DBMS for HadoopA .NET NoSQL DBMS that can integrate with and extend SQL Server.Well established RDBMSWidely 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 store
Key-value store
Relational 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 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
Score12.45
Rank#40  Overall
#24  Relational DBMS
Score3.80
Rank#82  Overall
#44  Relational DBMS
Score636.25
Rank#4  Overall
#4  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websiteimpala.apache.orgwww.actian.com/­databases/­ingreswww.postgresql.orgspark.apache.org/­sql
Technical documentationimpala.apache.org/­impala-docs.htmldocs.actian.com/­ingreswww.postgresql.org/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaFatCloudActian CorporationPostgreSQL Global Development Group infowww.postgresql.org/­developerApache Software Foundation
Initial release201320121974 infooriginally developed at University Berkely in early 1970s1989 info1989: Postgres, 1996: PostgreSQL2014
Current release4.1.0, June 202211.2, May 202216.3, May 20243.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercialOpen Source infoBSDOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C#CCScala
Server operating systemsLinuxWindowsAIX
HP Open VMS
HP-UX
Linux
Solaris
Windows
FreeBSD
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 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.nono infobut tools for importing/exporting data from/to XML-files availableyes infospecific XML-type available, but no XML query functionality.no
Secondary indexesyesyesyesyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsno infoVia inetgration in SQL Serveryesyes infostandard with numerous extensionsSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
Supported programming languagesAll languages supporting JDBC/ODBCC#.Net
C
C++
Delphi
Java infoJDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Java
Python
R
Scala
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyes infovia applicationsyesuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.no
Triggersnoyes infovia applicationsyesyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardinghorizontal partitioning infoIngres Star to access multiple databases simultaneouslypartitioning by range, list and (since PostgreSQL 11) by hashyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorselectable replication factorIngres ReplicatorSource-replica replication infoother methods possible by using 3rd party extensionsnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyEventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDACIDno
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.nononono
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosno infoCan implement custom security layer via applicationsfine grained access rights according to SQL-standardfine grained access rights according to SQL-standardno

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More resources
Apache ImpalaFatDBIngresPostgreSQLSpark SQL
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