DBMS > Drizzle vs. FatDB vs. GeoMesa vs. TimescaleDB vs. Transwarp KunDB

System Properties Comparison Drizzle vs. FatDB vs. GeoMesa vs. TimescaleDB vs. Transwarp KunDB

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

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

Description

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

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

OLTP DBMS based on a distributed architecture and highly compatible with MySQL and Oracle

Primary database model

Relational DBMS

Document store
Key-value store

Spatial DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	0.78
Rank	#214	Overall
	#4	Spatial DBMS

Score	4.06
Rank	#73	Overall
	#5	Time Series DBMS

Score	0.04
Rank	#362	Overall
	#154	Relational DBMS

Website

www.geomesa.org

www.timescale.com

www.transwarp.cn/en/product/kundb

Technical documentation

www.geomesa.org/documentation/stable/user/index.html

docs.timescale.com

Developer

Drizzle project, originally started by Brian Aker

FatCloud

CCRi and others

Timescale

Transwarp

Initial release

2008

2012

2014

2017

Current release

7.2.4, September 2012

5.0.1, July 2024

2.15.0, May 2024

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Open Source

Apache License 2.0

Open Source

Apache 2.0

commercial

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

C++

Scala

Server operating systems

FreeBSD
Linux
OS X

Windows

Linux
OS X
Windows

Data scheme

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

yes

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

yes

with proprietary extensions

Via inetgration in SQL Server

yes

full PostgreSQL SQL syntax

yes

APIs and other access methods

JDBC

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

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

Supported programming languages

C
C++
Java
PHP

.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

yes

Triggers

hooks for callbacks inside the server can be used.

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

depending on storage layer

yes, across time and space (hash partitioning) attributes

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication factor

depending on storage layer

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

depending on storage layer

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.

depending on storage layer

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Can implement custom security layer via applications

yes

depending on the DBMS used for storage

fine grained access rights according to SQL-standard

yes

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More resources

DB-Engines blog posts

MySQL won the April ranking; did its forks follow?
1 April 2015, Paul Andlinger

Has MySQL finally lost its mojo?
1 July 2013, Matthias Gelbmann

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6 April 2021, Matthias Gelbmann

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Recent citations in the news

General availability: Latest version of the TimeScaleDB extension on Azure Database for PostgreSQL - Flexible Server
8 May 2024, azure.microsoft.com

Timescale Acquires PopSQL to Bring a Modern, Collaborative SQL GUI to PostgreSQL Developers
4 April 2024, PR Newswire

TimescaleDB Is a Vector Database Now, Too
25 September 2023, Datanami

PostgreSQL is Now Faster than Pinecone, 75% Cheaper, with New Open Source Extensions
11 June 2024, PR Newswire

TimescaleDB goes distributed; implements ‘Chunking’ over ‘Sharding’ for scaling-out
22 August 2019, Packt Hub

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