DBMS > FatDB vs. PostGIS vs. Spark SQL vs. TimescaleDB vs. XTDB

System Properties Comparison FatDB vs. PostGIS vs. Spark SQL vs. TimescaleDB vs. XTDB

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.

Spatial extension of PostgreSQL

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

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

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Document store
Key-value store

Secondary database models

Relational DBMS

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

Score	22.69
Rank	#29	Overall
	#1	Spatial DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

postgis.net

spark.apache.org/sql

www.timescale.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

postgis.net/documentation

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

docs.timescale.com

www.xtdb.com/docs

Developer

FatCloud

Apache Software Foundation

Timescale

Juxt Ltd.

Initial release

2012

2005

2014

2017

2019

Current release

3.4.2, February 2024

3.5.0 ( 2.13), September 2023

2.15.0, May 2024

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

GPL v2.0

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

MIT License

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Scala

Clojure

Server operating systems

Windows

Linux
OS X
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes

schema-free

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

yes, extensible-data-notation format

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

yes

SQL-like DML and DDL statements

yes

full PostgreSQL SQL syntax

limited SQL, making use of Apache Calcite

APIs and other access methods

.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

HTTP REST
JDBC

Supported programming languages

Java
Python
R
Scala

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Clojure
Java

Server-side scripts

Stored procedures

yes

via applications

user defined functions

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

based on PostgreSQL

yes, utilizing Spark Core

yes, across time and space (hash partitioning) attributes

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

based on PostgreSQL

none

Source-replica replication with hot standby and reads on replicas

yes, each node contains all data

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

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

yes

based on PostgreSQL

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

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