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DBMS > Postgres-XL vs. Spark SQL vs. Sphinx

System Properties Comparison Postgres-XL vs. Spark SQL vs. Sphinx

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Editorial information provided by DB-Engines
NamePostgres-XL  Xexclude from comparisonSpark SQL  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionBased on PostgreSQL enhanced with MPP and write-scale-out cluster featuresSpark SQL is a component on top of 'Spark Core' for structured data processingOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelRelational DBMSRelational DBMSSearch engine
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.47
Rank#213  Overall
#102  Relational DBMS
Score16.95
Rank#35  Overall
#21  Relational DBMS
Score7.26
Rank#48  Overall
#5  Search engines
Websitewww.postgres-xl.orgspark.apache.org/­sqlsphinxsearch.com
Technical documentationwww.postgres-xl.org/­documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmlsphinxsearch.com/­docs
DeveloperApache Software FoundationSphinx Technologies Inc.
Initial release2014 infosince 2012, originally named StormDB20142001
Current release10 R1, October 2018v2.4.4, September 20193.1.1, October 2018
License infoCommercial or Open SourceOpen Source infoMozilla public licenseOpen Source infoApache 2.0Open Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCScalaC++
Server operating systemsLinux
macOS
Linux
OS X
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeyesyesyes
Typing infopredefined data types such as float or dateyesyesno
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.yes infoXML type, but no XML query functionalityno
Secondary indexesyesnoyes infofull-text index on all search fields
SQL infoSupport of SQLyes infodistributed, parallel query executionSQL-like DML and DDL statementsSQL-like query language (SphinxQL)
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
Proprietary protocol
Supported programming languages.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Java
Python
R
Scala
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresuser defined functionsnono
Triggersyesnono
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningyes, utilizing Spark CoreSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency
Foreign keys infoReferential integrityyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoMVCCnono
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes infoThe original contents of fields are not stored in the Sphinx index.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nono
User concepts infoAccess controlfine grained access rights according to SQL-standardnono

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Postgres-XLSpark SQLSphinx
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