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DBMS > Apache Phoenix vs. Google Cloud Bigtable vs. Microsoft Azure AI Search vs. Postgres-XL vs. Spark SQL

System Properties Comparison Apache Phoenix vs. Google Cloud Bigtable vs. Microsoft Azure AI Search vs. Postgres-XL vs. Spark SQL

Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonMicrosoft Azure AI Search  Xexclude from comparisonPostgres-XL  Xexclude from comparisonSpark SQL  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.Search-as-a-service for web and mobile app developmentBased on PostgreSQL enhanced with MPP and write-scale-out cluster featuresSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSKey-value store
Wide column store
Search engineRelational DBMSRelational DBMS
Secondary database modelsVector DBMSDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.06
Rank#123  Overall
#58  Relational DBMS
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Score5.52
Rank#59  Overall
#6  Search engines
Score0.53
Rank#254  Overall
#117  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitephoenix.apache.orgcloud.google.com/­bigtableazure.microsoft.com/­en-us/­services/­searchwww.postgres-xl.orgspark.apache.org/­sql
Technical documentationphoenix.apache.orgcloud.google.com/­bigtable/­docslearn.microsoft.com/­en-us/­azure/­searchwww.postgres-xl.org/­documentationspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software FoundationGoogleMicrosoftApache Software Foundation
Initial release2014201520152014 infosince 2012, originally named StormDB2014
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 2019V110 R1, October 20183.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialOpen Source infoMozilla public licenseOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenoyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaCScala
Server operating systemsLinux
Unix
Windows
hostedhostedLinux
macOS
Linux
OS X
Windows
Data schemeyes infolate-bound, schema-on-read capabilitiesschema-freeyesyesyes
Typing infopredefined data types such as float or dateyesnoyesyesyes
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.nononoyes infoXML type, but no XML query functionalityno
Secondary indexesyesnoyesyesno
SQL infoSupport of SQLyesnonoyes infodistributed, parallel query executionSQL-like DML and DDL statements
APIs and other access methodsJDBCgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
RESTful HTTP APIADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C#
C++
Go
Java
JavaScript (Node.js)
Python
C#
Java
JavaScript
Python
.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsnonouser defined functionsno
Triggersnononoyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoImplicit feature of the cloud servicehorizontal partitioningyes, utilizing Spark Core
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 zonesyes infoImplicit feature of the cloud servicenone
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 ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic single-row operationsnoACID infoMVCCno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnononono
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)yes infousing Azure authenticationfine grained access rights according to SQL-standardno

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More resources
Apache PhoenixGoogle Cloud BigtableMicrosoft Azure AI SearchPostgres-XLSpark SQL
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