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DBMS > Dolt vs. EsgynDB vs. FatDB vs. Spark SQL vs. TimescaleDB

System Properties Comparison Dolt vs. EsgynDB vs. FatDB vs. Spark SQL vs. TimescaleDB

Editorial information provided by DB-Engines
NameDolt  Xexclude from comparisonEsgynDB  Xexclude from comparisonFatDB  Xexclude from comparisonSpark SQL  Xexclude from comparisonTimescaleDB  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.
DescriptionA MySQL compatible DBMS with Git-like versioning of data and schemaEnterprise-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 processingA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelRelational DBMSRelational DBMSDocument store
Key-value store
Relational DBMSTime Series DBMS
Secondary database modelsDocument storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.95
Rank#197  Overall
#93  Relational DBMS
Score0.23
Rank#319  Overall
#141  Relational DBMS
Score19.15
Rank#33  Overall
#20  Relational DBMS
Score4.87
Rank#74  Overall
#4  Time Series DBMS
Websitegithub.com/­dolthub/­dolt
www.dolthub.com
www.esgyn.cnspark.apache.org/­sqlwww.timescale.com
Technical documentationdocs.dolthub.comspark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.timescale.com
DeveloperDoltHub IncEsgynFatCloudApache Software FoundationTimescale
Initial release20182015201220142017
Current release3.5.0 ( 2.13), September 20232.13.0, November 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialOpen 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 languageGoC++, JavaC#ScalaC
Server operating systemsLinux
macOS
Windows
LinuxWindowsLinux
OS X
Windows
Linux
OS X
Windows
Data schemeyesyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesyesyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types
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.nononoyes
Secondary indexesyesyesyesnoyes
SQL infoSupport of SQLyesyesno infoVia inetgration in SQL ServerSQL-like DML and DDL statementsyes infofull PostgreSQL SQL syntax
APIs and other access methodsCLI Client
HTTP REST
ADO.NET
JDBC
ODBC
.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
JDBC
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesAda
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
All languages supporting JDBC/ODBC/ADO.NetC#Java
Python
R
Scala
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresyes infocurrently in alpha releaseJava Stored Proceduresyes infovia applicationsnouser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersyesnoyes infovia applicationsnoyes
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardingyes, utilizing Spark Coreyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesA database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.Multi-source replication between multi datacentersselectable replication factornoneSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integrityyesyesnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
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.nonono
User concepts infoAccess controlOnly one user is configurable, and must be specified in the config file at startupfine grained access rights according to SQL-standardno infoCan implement custom security layer via applicationsnofine grained access rights according to SQL-standard

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More resources
DoltEsgynDBFatDBSpark SQLTimescaleDB
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