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DBMS > Apache Phoenix vs. Fujitsu Enterprise Postgres vs. InfinityDB vs. Vitess

System Properties Comparison Apache Phoenix vs. Fujitsu Enterprise Postgres vs. InfinityDB vs. Vitess

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonFujitsu Enterprise Postgres  Xexclude from comparisonInfinityDB  Xexclude from comparisonVitess  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseEnterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.A Java embedded Key-Value Store which extends the Java Map interfaceScalable, distributed, cloud-native DBMS, extending MySQL
Primary database modelRelational DBMSRelational DBMSKey-value storeRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
Document store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score0.31
Rank#285  Overall
#129  Relational DBMS
Score0.00
Rank#378  Overall
#57  Key-value stores
Score0.82
Rank#209  Overall
#97  Relational DBMS
Websitephoenix.apache.orgwww.postgresql.fastware.comboilerbay.comvitess.io
Technical documentationphoenix.apache.orgwww.postgresql.fastware.com/­product-manualsboilerbay.com/­infinitydb/­manualvitess.io/­docs
DeveloperApache Software FoundationPostgreSQL Global Development Group, Fujitsu Australia Software TechnologyBoiler Bay Inc.The Linux Foundation, PlanetScale
Initial release201420022013
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 2019Fujitsu Enterprise Postgres 14, January 20224.015.0.2, December 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialOpen Source infoApache Version 2.0, commercial licenses available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaCJavaGo
Server operating systemsLinux
Unix
Windows
Linux
Windows
All OS with a Java VMDocker
Linux
macOS
Data schemeyes infolate-bound, schema-on-read capabilitiesyesyes infonested virtual Java Maps, multi-value, logical ‘tuple space’ runtime Schema upgradeyes
Typing infopredefined data types such as float or dateyesyesyes infoall Java primitives, Date, CLOB, BLOB, huge sparse arraysyes
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.nono
Secondary indexesyesyesno infomanual creation possible, using inversions based on multi-value capabilityyes
SQL infoSupport of SQLyesyesnoyes infowith proprietary extensions
APIs and other access methodsJDBCADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/­docs/­ItemSpaceDataStructures.htm)
ADO.NET
JDBC
MySQL protocol
ODBC
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
JavaAda
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresuser defined functionsuser defined functionsnoyes infoproprietary syntax
Triggersnoyesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingpartitioning by range, list and by hashnoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Source-replica replicationnoneMulti-source replication
Source-replica replication
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 ConsistencyImmediate Consistency infoREAD-COMMITTED or SERIALIZEDEventual Consistency across shards
Immediate Consistency within a shard
Foreign keys infoReferential integritynoyesno 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 dataACIDACIDACID infoOptimistic locking for transactions; no isolation for bulk loadsACID at shard level
Concurrency infoSupport for concurrent manipulation of datayesyes, multi-version concurrency control (MVCC)yesyes 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.yesnoyes
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-standardnoUsers with fine-grained authorization concept infono user groups or roles
More information provided by the system vendor
Apache PhoenixFujitsu Enterprise PostgresInfinityDBVitess
Specific characteristics100% compatible with community PostgreSQL
» more
Competitive advantagesBuilt-in TDE and Data Masking security. In-Memory Columnar Index, and a high speed...
» more
Typical application scenariosTransactional payments applications, reporting and mixed workloads.
» more
Market metricsOver 30 years experience in database technology. Over 20 years in Postgres development...
» more
Licensing and pricing modelsCore based licensing
» more

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Apache PhoenixFujitsu Enterprise PostgresInfinityDBVitess
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