DBMS > DataFS vs. QuestDB vs. Sadas Engine vs. SWC-DB

System Properties Comparison DataFS vs. QuestDB vs. Sadas Engine vs. SWC-DB

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Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

A high performance open source SQL database for time series data

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

A high performance, scalable Wide Column DBMS

Primary database model

Secondary database models

Graph DBMS

Relational DBMS

Time Series DBMS

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

Score	0.09
Rank	#360	Overall
	#18	Object oriented DBMS

Score	2.70
Rank	#105	Overall
	#8	Time Series DBMS

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

newdatabase.com

questdb.io

www.sadasengine.com

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

Technical documentation

dev.mobiland.com/Overview.xsp

questdb.io/docs

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Mobiland AG

QuestDB Technology Inc

SADAS s.r.l.

Alex Kashirin

Initial release

2018

2014

2006

2020

Current release

1.1.263, October 2022

8.0

0.5, April 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

free trial version available

Open Source

GPL V3

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

Java (Zero-GC), C++, Rust

C++

Server operating systems

Windows

Linux
macOS
Windows

AIX
Linux
Windows

Linux

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

schema-free via InfluxDB Line Protocol

yes

schema-free

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

SQL

Support of SQL

SQL with time-series extensions

yes

SQL-like query language

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol

JDBC
ODBC
Proprietary protocol

Proprietary protocol
Thrift

Supported programming languages

.Net
C
C#
C++
VB.Net

PostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust

over HTTP

.Net
C
C#
C++
Groovy
Java
PHP
Python

C++

Server-side scripts

Stored procedures

Triggers

no, except callback-events from server when changes happened

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

horizontal partitioning (by timestamps)

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication with eventual consistency

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID for single-table writes

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.

yes

through memory mapped files

yes

managed by 'Learn by Usage'

User concepts

Access control

Windows-Profile

Access rights for users, groups and roles according to SQL-standard

More information provided by the system vendor

Specific characteristics

Relational model with native time series support Column-based storage and time partitioned...
» more

Competitive advantages

High ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more

Typical application scenarios

Financial tick data Industrial IoT Application Metrics Monitoring
» more

Key customers

Banks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more

Licensing and pricing models

Open source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more

News

Fluid real-time dashboards with Grafana and QuestDB
11 June 2024

QuestDB 8.0: Major Release
23 May 2024

QuestDB and Raspberry Pi 5 benchmark, a pocket-sized powerhouse
8 May 2024

Build your own resource monitor with QuestDB and Grafana
6 May 2024

Does "vpmovzxbd" Scare You? Here's Why it Doesn't Have To
12 April 2024

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources

Recent citations in the news

QuestDB snares $12M Series A with hosted version coming soon
3 November 2021, TechCrunch

SQL Extensions for Time-Series Data in QuestDB
11 January 2021, Towards Data Science

Comparing Different Time-Series Databases
10 February 2022, hackernoon.com

QuestDB Raises $12M in Series A Funding
8 November 2021, FinSMEs

Aquis Exchange goes live with QuestDB for real time monitoring
2 November 2022, FinanceFeeds

provided by Google News

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provided by Google News

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