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

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

Editorial information provided by DB-Engines
NameArcadeDB  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.
DescriptionFast and scalable multi-model DBMS, originally forked from OrientDB but most of the code has been rewrittenEnterprise-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 modelDocument store
Graph DBMS
Key-value store
Time Series DBMS infoin next version
Relational DBMSDocument store
Key-value store
Relational DBMSTime Series DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.02
Rank#366  Overall
#50  Document stores
#38  Graph DBMS
#53  Key-value stores
#36  Time Series DBMS
Score0.16
Rank#329  Overall
#146  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Websitearcadedb.comwww.esgyn.cnspark.apache.org/­sqlwww.timescale.com
Technical documentationdocs.arcadedb.comspark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.timescale.com
DeveloperArcade DataEsgynFatCloudApache Software FoundationTimescale
Initial release20212015201220142017
Current releaseSeptember 20213.5.0 ( 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

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Implementation languageJavaC++, JavaC#ScalaC
Server operating systemsAll OS with a Java VMLinuxWindowsLinux
OS X
Windows
Linux
OS X
Windows
Data schemeschema-freeyesschema-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 SQLSQL-like query language, no joinsyesno infoVia inetgration in SQL ServerSQL-like DML and DDL statementsyes infofull PostgreSQL SQL syntax
APIs and other access methodsJDBC
MongoDB API
OpenCypher
PostgreSQL wire protocol
Redis API
RESTful HTTP/JSON API
TinkerPop Gremlin
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 languagesJavaAll 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 proceduresJava 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
Triggersnoyes infovia applicationsnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes, utilizing Spark Coreyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationMulti-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 ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integrityyes inforelationship in graphsyesnonoyes
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 controlfine 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
ArcadeDBEsgynDBFatDBSpark SQLTimescaleDB
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