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DBMS > FatDB vs. LevelDB vs. Microsoft Azure Table Storage vs. Solr vs. Yaacomo

System Properties Comparison FatDB vs. LevelDB vs. Microsoft Azure Table Storage vs. Solr vs. Yaacomo

Editorial information provided by DB-Engines
NameFatDB  Xexclude from comparisonLevelDB  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonSolr  Xexclude from comparisonYaacomo  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.Yaacomo seems to be discontinued and is removed from the DB-Engines ranking
DescriptionA .NET NoSQL DBMS that can integrate with and extend SQL Server.Embeddable fast key-value storage library that provides an ordered mapping from string keys to string valuesA Wide Column Store for rapid development using massive semi-structured datasetsA widely used distributed, scalable search engine based on Apache LuceneOpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing
Primary database modelDocument store
Key-value store
Key-value storeWide column storeSearch engineRelational DBMS
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.25
Rank#115  Overall
#19  Key-value stores
Score4.04
Rank#77  Overall
#6  Wide column stores
Score41.02
Rank#24  Overall
#3  Search engines
Websitegithub.com/­google/­leveldbazure.microsoft.com/­en-us/­services/­storage/­tablessolr.apache.orgyaacomo.com
Technical documentationgithub.com/­google/­leveldb/­blob/­main/­doc/­index.mdsolr.apache.org/­resources.html
DeveloperFatCloudGoogleMicrosoftApache Software FoundationQ2WEB GmbH
Initial release20122011201220062009
Current release1.23, February 20219.6.1, May 2024
License infoCommercial or Open SourcecommercialOpen Source infoBSDcommercialOpen Source infoApache Version 2commercial
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC#C++Java
Server operating systemsWindowsIllumos
Linux
OS X
Windows
hostedAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)Android
Linux
Windows
Data schemeschema-freeschema-freeschema-freeyes infoDynamic Fields enables on-the-fly addition of new fieldsyes
Typing infopredefined data types such as float or dateyesnoyesyes infosupports customizable data types and automatic typingyes
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.nonoyesno
Secondary indexesyesnonoyes infoAll search fields are automatically indexedyes
SQL infoSupport of SQLno infoVia inetgration in SQL ServernonoSolr Parallel SQL Interfaceyes
APIs and other access methods.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
RESTful HTTP APIJava API
RESTful HTTP/JSON API
JDBC
ODBC
Supported programming languagesC#C++
Go
Java info3rd party binding
JavaScript (Node.js) info3rd party binding
Python info3rd party binding
.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 proceduresyes infovia applicationsnonoJava plugins
Triggersyes infovia applicationsnonoyes infoUser configurable commands triggered on index changesyes
Partitioning methods infoMethods for storing different data on different nodesShardingnoneSharding infoImplicit feature of the cloud serviceShardinghorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factornoneyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yesSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnonospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonooptimistic lockingoptimistic lockingACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
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.noyesyes
User concepts infoAccess controlno infoCan implement custom security layer via applicationsnoAccess rights based on private key authentication or shared access signaturesyesfine grained access rights according to SQL-standard

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More resources
FatDBLevelDBMicrosoft Azure Table StorageSolrYaacomo
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