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DBMS > Apache Phoenix vs. Sadas Engine vs. Spark SQL

System Properties Comparison Apache Phoenix vs. Sadas Engine vs. Spark SQL

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonSadas Engine  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseSADAS Engine is a columnar DBMS specifically designed for high performance in Data warehouse environmentsSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.20
Rank#96  Overall
#50  Relational DBMS
Score0.01
Rank#351  Overall
#146  Relational DBMS
Score22.97
Rank#34  Overall
#20  Relational DBMS
Websitephoenix.apache.orgwww.sadasengine.comspark.apache.org/­sql
Technical documentationphoenix.apache.orgwww.sadasengine.com/­index.php/­downloads/­documentation-columnar-dbspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software FoundationSADAS s.r.l.Apache Software Foundation
Initial release201420062014
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20198.03.2.0, October 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercial infofree trial version availableOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++Scala
Server operating systemsLinux
Unix
Windows
AIX
Linux
Windows
Linux
OS X
Windows
Data schemeyes infolate-bound, schema-on-read capabilitiesyesyes
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.nonono
Secondary indexesyesyesno
SQL infoSupport of SQLyesyesSQL-like DML and DDL statements
APIs and other access methodsJDBCJDBC
ODBC
Proprietary protocol
JDBC
ODBC
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
.Net
C
C#
C++
Groovy
Java
PHP
Python
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsnono
Triggersnonono
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioningyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
nonenone
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 dataACIDno
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.yesyes infomanaged by 'Learn by Usage'no
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 roles according to SQL-standardno

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More resources
Apache PhoenixSadas EngineSpark SQL
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