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DBMS > Ehcache vs. Kinetica vs. Oracle Berkeley DB vs. Spark SQL vs. TerarkDB

System Properties Comparison Ehcache vs. Kinetica vs. Oracle Berkeley DB vs. Spark SQL vs. TerarkDB

Editorial information provided by DB-Engines
NameEhcache  Xexclude from comparisonKinetica  Xexclude from comparisonOracle Berkeley DB  Xexclude from comparisonSpark SQL  Xexclude from comparisonTerarkDB  Xexclude from comparison
DescriptionA widely adopted Java cache with tiered storage optionsFully vectorized database across both GPUs and CPUsWidely used in-process key-value storeSpark SQL is a component on top of 'Spark Core' for structured data processingA key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB
Primary database modelKey-value storeRelational DBMSKey-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
Relational DBMSKey-value store
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.64
Rank#68  Overall
#8  Key-value stores
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score2.01
Rank#126  Overall
#21  Key-value stores
#3  Native XML DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.08
Rank#367  Overall
#56  Key-value stores
Websitewww.ehcache.orgwww.kinetica.comwww.oracle.com/­database/­technologies/­related/­berkeleydb.htmlspark.apache.org/­sqlgithub.com/­bytedance/­terarkdb
Technical documentationwww.ehcache.org/­documentationdocs.kinetica.comdocs.oracle.com/­cd/­E17076_05/­html/­index.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.htmlbytedance.larkoffice.com/­docs/­doccnZmYFqHBm06BbvYgjsHHcKc
DeveloperTerracotta Inc, owned by Software AGKineticaOracle infooriginally developed by Sleepycat, which was acquired by OracleApache Software FoundationByteDance, originally Terark
Initial release20092012199420142016
Current release3.10.0, March 20227.1, August 202118.1.40, May 20203.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2; commercial licenses availablecommercialOpen Source infocommercial license availableOpen Source infoApache 2.0commercial inforestricted open source version available
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 languageJavaC, C++C, Java, C++ (depending on the Berkeley DB edition)ScalaC++
Server operating systemsAll OS with a Java VMLinuxAIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
Linux
OS X
Windows
Data schemeschema-freeyesschema-freeyesschema-free
Typing infopredefined data types such as float or dateyesyesnoyesno
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.nonoyes infoonly with the Berkeley DB XML editionnono
Secondary indexesnoyesyesnono
SQL infoSupport of SQLnoSQL-like DML and DDL statementsyes infoSQL interfaced based on SQLite is availableSQL-like DML and DDL statementsno
APIs and other access methodsJCacheJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
C++ API
Java API
Supported programming languagesJavaC++
Java
JavaScript (Node.js)
Python
.Net infoFigaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others infoThird-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js) info3rd party binding
Perl
Python
Tcl
Java
Python
R
Scala
C++
Java
Server-side scripts infoStored proceduresnouser defined functionsnonono
Triggersyes infoCache Event Listenersyes infotriggers when inserted values for one or more columns fall within a specified rangeyes infoonly for the SQL APInono
Partitioning methods infoMethods for storing different data on different nodesSharding infoby using Terracotta ServerShardingnoneyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoby using Terracotta ServerSource-replica replicationSource-replica replicationnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononono
Consistency concepts infoMethods to ensure consistency in a distributed systemTunable Consistency (Strong, Eventual, Weak)Immediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynoyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayes infosupports JTA and can work as an XA resourcenoACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyes infousing a tiered cache-storage approachyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes infoGPU vRAM or System RAMyesnoyes
User concepts infoAccess controlnoAccess rights for users and roles on table levelnonono

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More resources
EhcacheKineticaOracle Berkeley DBSpark SQLTerarkDB
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