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DBMS > Apache Phoenix vs. Geode vs. Microsoft Azure Table Storage vs. Solr

System Properties Comparison Apache Phoenix vs. Geode vs. Microsoft Azure Table Storage vs. Solr

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonGeode  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseGeode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processesA Wide Column Store for rapid development using massive semi-structured datasetsA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelRelational DBMSKey-value storeWide column storeSearch engine
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score1.92
Rank#131  Overall
#23  Key-value stores
Score4.48
Rank#75  Overall
#6  Wide column stores
Score42.91
Rank#24  Overall
#3  Search engines
Websitephoenix.apache.orggeode.apache.orgazure.microsoft.com/­en-us/­services/­storage/­tablessolr.apache.org
Technical documentationphoenix.apache.orggeode.apache.org/­docssolr.apache.org/­resources.html
DeveloperApache Software FoundationOriginally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.MicrosoftApache Software Foundation
Initial release2014200220122006
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20191.1, February 20179.6.0, April 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2; commercial licenses available as GemfirecommercialOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaJava
Server operating systemsLinux
Unix
Windows
All OS with a Java VM infothe JDK (8 or later) is also requiredhostedAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyes infolate-bound, schema-on-read capabilitiesschema-freeschema-freeyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyesyesyes infosupports customizable data types and automatic typing
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
Secondary indexesyesnonoyes infoAll search fields are automatically indexed
SQL infoSupport of SQLyesSQL-like query language (OQL)noSolr Parallel SQL Interface
APIs and other access methodsJDBCJava Client API
Memcached protocol
RESTful HTTP API
RESTful HTTP APIJava API
RESTful HTTP/JSON API
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
.Net
All JVM based languages
C++
Groovy
Java
Scala
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresuser defined functionsuser defined functionsnoJava plugins
Triggersnoyes infoCache Event Listenersnoyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoImplicit feature of the cloud serviceSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Multi-source replicationyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationnonospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyEventual ConsistencyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDyes, on a single nodeoptimistic lockingoptimistic locking
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.yesyesnoyes
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyAccess rights per client and object definableAccess rights based on private key authentication or shared access signaturesyes

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Apache PhoenixGeodeMicrosoft Azure Table StorageSolr
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