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

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

Editorial information provided by DB-Engines
NameFatDB  Xexclude from comparisonInterSystems Caché  Xexclude from comparisonOracle Berkeley DB  Xexclude from comparisonQuestDB  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.
DescriptionA .NET NoSQL DBMS that can integrate with and extend SQL Server.A multi-model DBMS and application serverWidely used in-process key-value storeA high performance open source SQL database for time series dataSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelDocument 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
Time Series DBMSRelational DBMS
Secondary database modelsDocument storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.01
Rank#126  Overall
#21  Key-value stores
#3  Native XML DBMS
Score2.70
Rank#105  Overall
#8  Time Series DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitewww.intersystems.com/­products/­cachewww.oracle.com/­database/­technologies/­related/­berkeleydb.htmlquestdb.iospark.apache.org/­sql
Technical documentationdocs.intersystems.comdocs.oracle.com/­cd/­E17076_05/­html/­index.htmlquestdb.io/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperFatCloudInterSystemsOracle infooriginally developed by Sleepycat, which was acquired by OracleQuestDB Technology IncApache Software Foundation
Initial release20121997199420142014
Current release2018.1.4, May 202018.1.40, May 20203.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialcommercialOpen Source infocommercial license availableOpen Source infoApache 2.0Open 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, Java, C++ (depending on the Berkeley DB edition)Java (Zero-GC), C++, RustScala
Server operating systemsWindowsAIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows
AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
Linux
macOS
Windows
Linux
OS X
Windows
Data schemeschema-freedepending on used data modelschema-freeyes infoschema-free via InfluxDB Line Protocolyes
Typing infopredefined data types such as float or dateyesyesnoyesyes
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.yesyes infoonly with the Berkeley DB XML editionnono
Secondary indexesyesyesyesnono
SQL infoSupport of SQLno infoVia inetgration in SQL Serveryesyes infoSQL interfaced based on SQLite is availableSQL with time-series extensionsSQL-like DML and DDL statements
APIs and other access methods.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
.NET Client API
JDBC
ODBC
RESTful HTTP API
HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol
JDBC
ODBC
Supported programming languagesC#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
C infoPostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust infoover HTTP
Java
Python
R
Scala
Server-side scripts infoStored proceduresyes infovia applicationsyesnonono
Triggersyes infovia applicationsyesyes infoonly for the SQL APInono
Partitioning methods infoMethods for storing different data on different nodesShardingnonenonehorizontal partitioning (by timestamps)yes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replicationSource-replica replicationSource-replica replication with eventual consistencynone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDACID for single-table writesno
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.yesyesyes infothrough memory mapped filesno
User concepts infoAccess controlno infoCan implement custom security layer via applicationsAccess rights for users, groups and rolesnono
More information provided by the system vendor
FatDBInterSystems CachéOracle Berkeley DBQuestDBSpark SQL
Specific characteristicsRelational model with native time series support Column-based storage and time partitioned...
» more
Competitive advantagesHigh ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more
Typical application scenariosFinancial tick data Industrial IoT Application Metrics Monitoring
» more
Key customersBanks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more
Licensing and pricing modelsOpen source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more
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