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DBMS > Apache Phoenix vs. Milvus vs. Spark SQL vs. Transbase

System Properties Comparison Apache Phoenix vs. Milvus vs. Spark SQL vs. Transbase

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonMilvus  Xexclude from comparisonSpark SQL  Xexclude from comparisonTransbase  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseA DBMS designed for efficient storage of vector data and vector similarity searchesSpark SQL is a component on top of 'Spark Core' for structured data processingA resource-optimized, high-performance, universally applicable RDBMS
Primary database modelRelational DBMSMultivalue DBMSRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.11
Rank#95  Overall
#50  Relational DBMS
Score0.28
Rank#271  Overall
#11  Multivalue DBMS
Score22.94
Rank#34  Overall
#20  Relational DBMS
Score0.22
Rank#285  Overall
#132  Relational DBMS
Websitephoenix.apache.orgmilvus.iospark.apache.org/­sqlwww.transaction.de/­en/­solutions/­transbase-resource-optimized-high-performance-database
Technical documentationphoenix.apache.orgmilvus.io/­docs/­overview.mdspark.apache.org/­docs/­latest/­sql-programming-guide.htmlwww.transaction.de/­en/­transbase-inside
DeveloperApache Software FoundationApache Software FoundationTransaction Software GmbH
Initial release2014201920141987
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20191.0, March 20213.1.1, March 2021Transbase 8.4, September 2020
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2.0Open Source infoApache 2.0commercial infofree development license
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++ScalaC and C++
Server operating systemsLinux
Unix
Windows
LinuxLinux
OS X
Windows
FreeBSD
Linux
macOS
Solaris
Windows
Data schemeyes infolate-bound, schema-on-read capabilitiesyesyes
Typing infopredefined data types such as float or dateyesNumeric data onlyyesyes
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.nononono
Secondary indexesyesnonoyes
SQL infoSupport of SQLyesnoSQL-like DML and DDL statementsyes
APIs and other access methodsJDBCRESTful HTTP APIJDBC
ODBC
ADO.NET
JDBC
ODBC
Proprietary native API
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C++
Go
Java
Python
Java
Python
R
Scala
C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python
Server-side scripts infoStored proceduresuser defined functionsnonoyes
Triggersnononoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
noneSource-replica replication
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 integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnonoyes
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-tenancynonofine grained access rights according to SQL-standard

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More resources
Apache PhoenixMilvusSpark SQLTransbase
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