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DBMS > Apache Phoenix vs. InterSystems Caché vs. Spark SQL

System Properties Comparison Apache Phoenix vs. InterSystems Caché vs. Spark SQL

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonInterSystems Caché  Xexclude from comparisonSpark SQL  Xexclude from comparison
Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseA multi-model DBMS and application serverSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSKey-value store
Object oriented DBMS
Relational DBMS
Relational DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitephoenix.apache.orgwww.intersystems.com/­products/­cachespark.apache.org/­sql
Technical documentationphoenix.apache.orgdocs.intersystems.comspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software FoundationInterSystemsApache Software Foundation
Initial release201419972014
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20192018.1.4, May 20203.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaScala
Server operating systemsLinux
Unix
Windows
AIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows
Linux
OS X
Windows
Data schemeyes infolate-bound, schema-on-read capabilitiesdepending on used data modelyes
Typing infopredefined data types such as float or dateyesyesyes
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.noyesno
Secondary indexesyesyesno
SQL infoSupport of SQLyesyesSQL-like DML and DDL statements
APIs and other access methodsJDBC.NET Client API
JDBC
ODBC
RESTful HTTP API
JDBC
ODBC
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C#
C++
Java
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsyesno
Triggersnoyesno
Partitioning methods infoMethods for storing different data on different nodesShardingnoneyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Source-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesno
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyAccess rights for users, groups and rolesno

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More resources
Apache PhoenixInterSystems CachéSpark SQL
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