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DBMS > EsgynDB vs. Quasardb vs. Spark SQL vs. Tkrzw

System Properties Comparison EsgynDB vs. Quasardb vs. Spark SQL vs. Tkrzw

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Editorial information provided by DB-Engines
NameEsgynDB  Xexclude from comparisonQuasardb  Xexclude from comparisonSpark SQL  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
DescriptionEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionDistributed, high-performance timeseries databaseSpark SQL is a component on top of 'Spark Core' for structured data processingA concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet
Primary database modelRelational DBMSTime Series DBMSRelational DBMSKey-value store
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
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.07
Rank#372  Overall
#57  Key-value stores
Websitewww.esgyn.cnquasar.aispark.apache.org/­sqldbmx.net/­tkrzw
Technical documentationdoc.quasar.ai/­masterspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperEsgynquasardbApache Software FoundationMikio Hirabayashi
Initial release2015200920142020
Current release3.14.1, January 20243.5.0 ( 2.13), September 20230.9.3, August 2020
License infoCommercial or Open Sourcecommercialcommercial infoFree community edition, Non-profit organizations and non-commercial usage are eligible for free licensesOpen Source infoApache 2.0Open Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++, JavaC++ScalaC++
Server operating systemsLinuxBSD
Linux
OS X
Windows
Linux
OS X
Windows
Linux
macOS
Data schemeyesschema-freeyesschema-free
Typing infopredefined data types such as float or dateyesyes infointeger and binaryyesno
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.nononono
Secondary indexesyesyes infowith tagsno
SQL infoSupport of SQLyesSQL-like query languageSQL-like DML and DDL statementsno
APIs and other access methodsADO.NET
JDBC
ODBC
HTTP APIJDBC
ODBC
Supported programming languagesAll languages supporting JDBC/ODBC/ADO.Net.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R
Java
Python
R
Scala
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresJava Stored Proceduresnonono
Triggersnononono
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoconsistent hashingyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication between multi datacentersSource-replica replication with selectable replication factornonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyeswith Hadoop integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes infoby using LevelDByesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infoTransient modenoyes infousing specific database classes
User concepts infoAccess controlfine grained access rights according to SQL-standardCryptographically strong user authentication and audit trailnono

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EsgynDBQuasardbSpark SQLTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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