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DBMS > Brytlyt vs. FatDB vs. InterSystems Caché vs. Oracle Berkeley DB vs. Spark SQL

System Properties Comparison Brytlyt vs. FatDB vs. InterSystems Caché vs. Oracle Berkeley DB vs. Spark SQL

Editorial information provided by DB-Engines
NameBrytlyt  Xexclude from comparisonFatDB  Xexclude from comparisonInterSystems Caché  Xexclude from comparisonOracle Berkeley DB  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.Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.
DescriptionScalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQLA .NET NoSQL DBMS that can integrate with and extend SQL Server.A multi-model DBMS and application serverWidely used in-process key-value storeSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSDocument store
Key-value store
Key-value store
Object oriented DBMS
Relational DBMS
Key-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
Relational DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.38
Rank#276  Overall
#127  Relational DBMS
Score2.01
Rank#126  Overall
#21  Key-value stores
#3  Native XML DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitebrytlyt.iowww.intersystems.com/­products/­cachewww.oracle.com/­database/­technologies/­related/­berkeleydb.htmlspark.apache.org/­sql
Technical documentationdocs.brytlyt.iodocs.intersystems.comdocs.oracle.com/­cd/­E17076_05/­html/­index.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperBrytlytFatCloudInterSystemsOracle infooriginally developed by Sleepycat, which was acquired by OracleApache Software Foundation
Initial release20162012199719942014
Current release5.0, August 20232018.1.4, May 202018.1.40, May 20203.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source infocommercial license availableOpen Source infoApache 2.0
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, C++ and CUDAC#C, Java, C++ (depending on the Berkeley DB edition)Scala
Server operating systemsLinux
OS X
Windows
WindowsAIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows
AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
Linux
OS X
Windows
Data schemeyesschema-freedepending on used data modelschema-freeyes
Typing infopredefined data types such as float or dateyesyesyesnoyes
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.yes infospecific XML-type available, but no XML query functionality.yesyes infoonly with the Berkeley DB XML editionno
Secondary indexesyesyesyesyesno
SQL infoSupport of SQLyesno infoVia inetgration in SQL Serveryesyes infoSQL interfaced based on SQLite is availableSQL-like DML and DDL statements
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
.NET Client API
JDBC
ODBC
RESTful HTTP API
JDBC
ODBC
Supported programming languages.Net
C
C++
Delphi
Java
Perl
Python
Tcl
C#C#
C++
Java
.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
Server-side scripts infoStored proceduresuser defined functions infoin PL/pgSQLyes infovia applicationsyesnono
Triggersyesyes infovia applicationsyesyes infoonly for the SQL APIno
Partitioning methods infoMethods for storing different data on different nodesShardingnonenoneyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationselectable replication factorSource-replica replicationSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integrityyesnoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesno
User concepts infoAccess controlfine grained access rights according to SQL-standardno infoCan implement custom security layer via applicationsAccess rights for users, groups and rolesnono

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BrytlytFatDBInterSystems CachéOracle Berkeley DBSpark SQL
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