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DBMS > Ehcache vs. EsgynDB vs. FatDB vs. Spark SQL

System Properties Comparison Ehcache vs. EsgynDB vs. FatDB vs. Spark SQL

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Editorial information provided by DB-Engines
NameEhcache  Xexclude from comparisonEsgynDB  Xexclude from comparisonFatDB  Xexclude from comparisonSpark SQL  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.
DescriptionA widely adopted Java cache with tiered storage optionsEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionA .NET NoSQL DBMS that can integrate with and extend SQL Server.Spark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelKey-value storeRelational DBMSDocument store
Key-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.89
Rank#67  Overall
#8  Key-value stores
Score0.16
Rank#329  Overall
#146  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitewww.ehcache.orgwww.esgyn.cnspark.apache.org/­sql
Technical documentationwww.ehcache.org/­documentationspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperTerracotta Inc, owned by Software AGEsgynFatCloudApache Software Foundation
Initial release2009201520122014
Current release3.10.0, March 20223.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2; commercial licenses availablecommercialcommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++, JavaC#Scala
Server operating systemsAll OS with a Java VMLinuxWindowsLinux
OS X
Windows
Data schemeschema-freeyesschema-freeyes
Typing infopredefined data types such as float or dateyesyesyesyes
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.nonono
Secondary indexesnoyesyesno
SQL infoSupport of SQLnoyesno infoVia inetgration in SQL ServerSQL-like DML and DDL statements
APIs and other access methodsJCacheADO.NET
JDBC
ODBC
.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
JDBC
ODBC
Supported programming languagesJavaAll languages supporting JDBC/ODBC/ADO.NetC#Java
Python
R
Scala
Server-side scripts infoStored proceduresnoJava Stored Proceduresyes infovia applicationsno
Triggersyes infoCache Event Listenersnoyes infovia applicationsno
Partitioning methods infoMethods for storing different data on different nodesSharding infoby using Terracotta ServerShardingShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoby using Terracotta ServerMulti-source replication between multi datacentersselectable replication factornone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyes
Consistency concepts infoMethods to ensure consistency in a distributed systemTunable Consistency (Strong, Eventual, Weak)Immediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayes infosupports JTA and can work as an XA resourceACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyes infousing a tiered cache-storage approachyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnono
User concepts infoAccess controlnofine grained access rights according to SQL-standardno infoCan implement custom security layer via applicationsno

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More resources
EhcacheEsgynDBFatDBSpark SQL
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