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DBMS > Apache Phoenix vs. LevelDB vs. Realm vs. Solr vs. Yanza

System Properties Comparison Apache Phoenix vs. LevelDB vs. Realm vs. Solr vs. Yanza

Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonLevelDB  Xexclude from comparisonRealm  Xexclude from comparisonSolr  Xexclude from comparisonYanza  Xexclude from comparison
Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseEmbeddable fast key-value storage library that provides an ordered mapping from string keys to string valuesA DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core DataA widely used distributed, scalable search engine based on Apache LuceneTime Series DBMS for IoT Applications
Primary database modelRelational DBMSKey-value storeDocument storeSearch engineTime Series DBMS
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
Score2.35
Rank#111  Overall
#19  Key-value stores
Score7.60
Rank#52  Overall
#9  Document stores
Score42.91
Rank#24  Overall
#3  Search engines
Websitephoenix.apache.orggithub.com/­google/­leveldbrealm.iosolr.apache.orgyanza.com
Technical documentationphoenix.apache.orggithub.com/­google/­leveldb/­blob/­main/­doc/­index.mdrealm.io/­docssolr.apache.org/­resources.html
DeveloperApache Software FoundationGoogleRealm, acquired by MongoDB in May 2019Apache Software FoundationYanza
Initial release20142011201420062015
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20191.23, February 20219.6.0, April 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoBSDOpen SourceOpen Source infoApache Version 2commercial infofree version available
Cloud-based only infoOnly available as a cloud servicenonononono infobut mainly used as a service provided by Yanza
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++Java
Server operating systemsLinux
Unix
Windows
Illumos
Linux
OS X
Windows
Android
Backend: server-less
iOS
Windows
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)Windows
Data schemeyes infolate-bound, schema-on-read capabilitiesschema-freeyesyes infoDynamic Fields enables on-the-fly addition of new fieldsschema-free
Typing infopredefined data types such as float or dateyesnoyesyes infosupports customizable data types and automatic typingno
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.nononoyesno
Secondary indexesyesnoyesyes infoAll search fields are automatically indexedno
SQL infoSupport of SQLyesnonoSolr Parallel SQL Interfaceno
APIs and other access methodsJDBCJava API
RESTful HTTP/JSON API
HTTP API
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C++
Go
Java info3rd party binding
JavaScript (Node.js) info3rd party binding
Python info3rd party binding
.Net
Java infowith Android only
Objective-C
React Native
Swift
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
any language that supports HTTP calls
Server-side scripts infoStored proceduresuser defined functionsnono inforuns within the applications so server-side scripts are unnecessaryJava pluginsno
Triggersnonoyes infoChange Listenersyes infoUser configurable commands triggered on index changesyes infoTimer and event based
Partitioning methods infoMethods for storing different data on different nodesShardingnonenoneShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
nonenoneyesnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationnonospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate ConsistencyImmediate ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDoptimistic lockingno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes infowith automatic compression on writesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes infoIn-Memory realmyes
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancynoyesyesno

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More resources
Apache PhoenixLevelDBRealmSolrYanza
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