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DBMS > Apache Phoenix vs. Google Cloud Bigtable vs. InfinityDB vs. Vitess

System Properties Comparison Apache Phoenix vs. Google Cloud Bigtable vs. InfinityDB vs. Vitess

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonInfinityDB  Xexclude from comparisonVitess  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.A Java embedded Key-Value Store which extends the Java Map interfaceScalable, distributed, cloud-native DBMS, extending MySQL
Primary database modelRelational DBMSKey-value store
Wide column store
Key-value storeRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score0.00
Rank#378  Overall
#57  Key-value stores
Score0.82
Rank#209  Overall
#97  Relational DBMS
Websitephoenix.apache.orgcloud.google.com/­bigtableboilerbay.comvitess.io
Technical documentationphoenix.apache.orgcloud.google.com/­bigtable/­docsboilerbay.com/­infinitydb/­manualvitess.io/­docs
DeveloperApache Software FoundationGoogleBoiler Bay Inc.The Linux Foundation, PlanetScale
Initial release2014201520022013
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 2.0commercialcommercialOpen Source infoApache Version 2.0, commercial licenses available
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaGo
Server operating systemsLinux
Unix
Windows
hostedAll OS with a Java VMDocker
Linux
macOS
Data schemeyes infolate-bound, schema-on-read capabilitiesschema-freeyes infonested virtual Java Maps, multi-value, logical ‘tuple space’ runtime Schema upgradeyes
Typing infopredefined data types such as float or dateyesnoyes 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.nonono
Secondary indexesyesnono infomanual creation possible, using inversions based on multi-value capabilityyes
SQL infoSupport of SQLyesnonoyes infowith proprietary extensions
APIs and other access methodsJDBCgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
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
C#
C++
Go
Java
JavaScript (Node.js)
Python
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 functionsnonoyes infoproprietary syntax
Triggersnononoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingnoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Internal replication in Colossus, and regional replication between two clusters in different zonesnoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate Consistency infoREAD-COMMITTED or SERIALIZEDEventual Consistency across shards
Immediate Consistency within a shard
Foreign keys infoReferential integritynonono 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 dataACIDAtomic single-row operationsACID infoOptimistic locking for transactions; no isolation for bulk loadsACID at shard level
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes 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.yesnonoyes
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 based on Google Cloud Identity and Access Management (IAM)noUsers with fine-grained authorization concept infono user groups or roles

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More resources
Apache PhoenixGoogle Cloud BigtableInfinityDBVitess
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