DBMS > FatDB vs. Spark SQL vs. SWC-DB vs. TimescaleDB

System Properties Comparison FatDB vs. Spark SQL vs. SWC-DB vs. TimescaleDB

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Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Spark SQL is a component on top of 'Spark Core' for structured data processing

A high performance, scalable Wide Column DBMS

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Document store
Key-value store

Relational DBMS

Wide column store

Time Series DBMS

Secondary database models

Time Series DBMS

Relational DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Score	0.01
Rank	#387	Overall
	#13	Wide column stores

Score	4.87
Rank	#74	Overall
	#4	Time Series DBMS

Website

spark.apache.org/sql

github.com/kashirin-alex/swc-db
www.swcdb.org

www.timescale.com

Technical documentation

spark.apache.org/docs/latest/sql-programming-guide.html

docs.timescale.com

Developer

FatCloud

Apache Software Foundation

Alex Kashirin

Timescale

Initial release

2012

2014

2020

2017

Current release

3.5.0 ( 2.13), September 2023

0.5, April 2021

2.13.0, November 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GPL V3

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Scala

C++

Server operating systems

Windows

Linux
OS X
Windows

Linux

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

SQL

Support of SQL

Via inetgration in SQL Server

SQL-like DML and DDL statements

SQL-like query language

yes

full PostgreSQL SQL syntax

APIs and other access methods

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

JDBC
ODBC

Proprietary protocol
Thrift

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

Java
Python
R
Scala

C++

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

via applications

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Sharding

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

Can implement custom security layer via applications

fine grained access rights according to SQL-standard

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