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DBMS > EsgynDB vs. Quasardb vs. Solr vs. Spark SQL vs. Yaacomo

System Properties Comparison EsgynDB vs. Quasardb vs. Solr vs. Spark SQL vs. Yaacomo

Editorial information provided by DB-Engines
NameEsgynDB  Xexclude from comparisonQuasardb  Xexclude from comparisonSolr  Xexclude from comparisonSpark SQL  Xexclude from comparisonYaacomo  Xexclude from comparison
Yaacomo seems to be discontinued and is removed from the DB-Engines ranking
DescriptionEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionDistributed, high-performance timeseries databaseA widely used distributed, scalable search engine based on Apache LuceneSpark SQL is a component on top of 'Spark Core' for structured data processingOpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing
Primary database modelRelational DBMSTime Series DBMSSearch engineRelational DBMSRelational DBMS
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.25
Rank#312  Overall
#138  Relational DBMS
Score0.21
Rank#322  Overall
#29  Time Series DBMS
Score41.02
Rank#24  Overall
#3  Search engines
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitewww.esgyn.cnquasar.aisolr.apache.orgspark.apache.org/­sqlyaacomo.com
Technical documentationdoc.quasar.ai/­mastersolr.apache.org/­resources.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperEsgynquasardbApache Software FoundationApache Software FoundationQ2WEB GmbH
Initial release20152009200620142009
Current release3.14.1, January 20249.6.1, May 20243.5.0 ( 2.13), September 2023
License infoCommercial or Open Sourcecommercialcommercial infoFree community edition, Non-profit organizations and non-commercial usage are eligible for free licensesOpen Source infoApache Version 2Open Source infoApache 2.0commercial
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++, JavaC++JavaScala
Server operating systemsLinuxBSD
Linux
OS X
Windows
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)Linux
OS X
Windows
Android
Linux
Windows
Data schemeyesschema-freeyes infoDynamic Fields enables on-the-fly addition of new fieldsyesyes
Typing infopredefined data types such as float or dateyesyes infointeger and binaryyes infosupports customizable data types and automatic typingyesyes
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.nonoyesnono
Secondary indexesyesyes infowith tagsyes infoAll search fields are automatically indexednoyes
SQL infoSupport of SQLyesSQL-like query languageSolr Parallel SQL InterfaceSQL-like DML and DDL statementsyes
APIs and other access methodsADO.NET
JDBC
ODBC
HTTP APIJava API
RESTful HTTP/JSON API
JDBC
ODBC
JDBC
ODBC
Supported programming languagesAll languages supporting JDBC/ODBC/ADO.Net.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Java
Python
R
Scala
Server-side scripts infoStored proceduresJava Stored ProceduresnoJava pluginsno
Triggersnonoyes infoUser configurable commands triggered on index changesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoconsistent hashingShardingyes, utilizing Spark Corehorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication between multi datacentersSource-replica replication with selectable replication factoryesnoneSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyeswith Hadoop integrationspark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDoptimistic lockingnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyes infoby using LevelDByesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infoTransient modeyesnoyes
User concepts infoAccess controlfine grained access rights according to SQL-standardCryptographically strong user authentication and audit trailyesnofine grained access rights according to SQL-standard

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More resources
EsgynDBQuasardbSolrSpark SQLYaacomo
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