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DBMS > Apache Phoenix vs. Postgres-XL vs. Spark SQL vs. TimescaleDB

System Properties Comparison Apache Phoenix vs. Postgres-XL vs. Spark SQL vs. TimescaleDB

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonPostgres-XL  Xexclude from comparisonSpark SQL  Xexclude from comparisonTimescaleDB  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseBased on PostgreSQL enhanced with MPP and write-scale-out cluster featuresSpark SQL is a component on top of 'Spark Core' for structured data processingA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelRelational DBMSRelational DBMSRelational DBMSTime Series DBMS
Secondary database modelsDocument store
Spatial DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score0.49
Rank#256  Overall
#117  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Websitephoenix.apache.orgwww.postgres-xl.orgspark.apache.org/­sqlwww.timescale.com
Technical documentationphoenix.apache.orgwww.postgres-xl.org/­documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.timescale.com
DeveloperApache Software FoundationApache Software FoundationTimescale
Initial release20142014 infosince 2012, originally named StormDB20142017
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 201910 R1, October 20183.5.0 ( 2.13), September 20232.13.0, November 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoMozilla public licenseOpen Source infoApache 2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaCScalaC
Server operating systemsLinux
Unix
Windows
Linux
macOS
Linux
OS X
Windows
Linux
OS X
Windows
Data schemeyes infolate-bound, schema-on-read capabilitiesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types
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.noyes infoXML type, but no XML query functionalitynoyes
Secondary indexesyesyesnoyes
SQL infoSupport of SQLyesyes infodistributed, parallel query executionSQL-like DML and DDL statementsyes infofull PostgreSQL SQL syntax
APIs and other access methodsJDBCADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Java
Python
R
Scala
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresuser defined functionsuser defined functionsnouser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersnoyesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioningyes, utilizing Spark Coreyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
noneSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoMVCCnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnonono
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyfine grained access rights according to SQL-standardnofine grained access rights according to SQL-standard

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More resources
Apache PhoenixPostgres-XLSpark SQLTimescaleDB
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