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DBMS > Apache Phoenix vs. InfinityDB vs. Vitess vs. YottaDB

System Properties Comparison Apache Phoenix vs. InfinityDB vs. Vitess vs. YottaDB

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonInfinityDB  Xexclude from comparisonVitess  Xexclude from comparisonYottaDB  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseA Java embedded Key-Value Store which extends the Java Map interfaceScalable, distributed, cloud-native DBMS, extending MySQLA fast and solid embedded Key-value store
Primary database modelRelational DBMSKey-value storeRelational DBMSKey-value store
Secondary database modelsDocument store
Spatial DBMS
Relational DBMS infousing the Octo plugin
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score0.00
Rank#378  Overall
#57  Key-value stores
Score0.82
Rank#209  Overall
#97  Relational DBMS
Score0.20
Rank#317  Overall
#47  Key-value stores
Websitephoenix.apache.orgboilerbay.comvitess.ioyottadb.com
Technical documentationphoenix.apache.orgboilerbay.com/­infinitydb/­manualvitess.io/­docsyottadb.com/­resources/­documentation
DeveloperApache Software FoundationBoiler Bay Inc.The Linux Foundation, PlanetScaleYottaDB, LLC
Initial release2014200220132001
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20194.015.0.2, December 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache Version 2.0, commercial licenses availableOpen Source infoAGPL 3.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaGoC
Server operating systemsLinux
Unix
Windows
All OS with a Java VMDocker
Linux
macOS
Docker
Linux
Data schemeyes infolate-bound, schema-on-read capabilitiesyes infonested virtual Java Maps, multi-value, logical ‘tuple space’ runtime Schema upgradeyesschema-free
Typing infopredefined data types such as float or dateyesyes infoall Java primitives, Date, CLOB, BLOB, huge sparse arraysyesno
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 indexesyesno infomanual creation possible, using inversions based on multi-value capabilityyesno
SQL infoSupport of SQLyesnoyes infowith proprietary extensionsby using the Octo plugin
APIs and other access methodsJDBCAccess via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/­docs/­ItemSpaceDataStructures.htm)
ADO.NET
JDBC
MySQL protocol
ODBC
PostgreSQL wire protocol infousing the Octo plugin
Proprietary protocol
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
JavaAda
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust
Server-side scripts infoStored proceduresuser defined functionsnoyes infoproprietary syntax
Triggersnonoyes
Partitioning methods infoMethods for storing different data on different nodesShardingnoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
noneMulti-source replication
Source-replica replication
yes
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate Consistency infoREAD-COMMITTED or SERIALIZEDEventual Consistency across shards
Immediate Consistency within a shard
Foreign keys infoReferential integritynono infomanual creation possible, using inversions based on multi-value capabilityyes infonot for MyISAM storage engine
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoOptimistic locking for transactions; no isolation for bulk loadsACID at shard leveloptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyes infotable locks or row locks depending on storage engine
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.yesnoyesyes
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancynoUsers with fine-grained authorization concept infono user groups or rolesUsers and groups based on OS-security mechanisms

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More resources
Apache PhoenixInfinityDBVitessYottaDB
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Recent citations in the news

Supercharge SQL on Your Data in Apache HBase with Apache Phoenix | Amazon Web Services
2 June 2016, AWS Blog

Bridge the SQL-NoSQL gap with Apache Phoenix
4 February 2016, InfoWorld

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27 March 2017, SDTimes.com

Hortonworks Starts Hadoop Summit with Data Platform Update -- ADTmag
28 June 2016, ADT Magazine

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25 September 2018, azure.microsoft.com

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18 May 2021, Datanami

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13 November 2019, TechTarget

Massively Scaling MySQL Using Vitess
19 February 2019, InfoQ.com

They scaled YouTube -- now they’ll shard everyone with PlanetScale
13 December 2018, TechCrunch

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