DBMS > DataFS vs. Dragonfly vs. Google BigQuery vs. Quasardb

System Properties Comparison DataFS vs. Dragonfly vs. Google BigQuery vs. Quasardb

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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 drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

Large scale data warehouse service with append-only tables

Distributed, high-performance timeseries database

Primary database model

Secondary database models

Graph DBMS

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Trend Chart

Score	0.09
Rank	#360	Overall
	#18	Object oriented DBMS

Score	0.49
Rank	#261	Overall
	#38	Key-value stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Website

newdatabase.com

github.com/dragonflydb/dragonfly
www.dragonflydb.io

cloud.google.com/bigquery

quasar.ai

Technical documentation

dev.mobiland.com/Overview.xsp

www.dragonflydb.io/docs

cloud.google.com/bigquery/docs

doc.quasar.ai/master

Developer

Mobiland AG

DragonflyDB team and community contributors

Google

quasardb

Initial release

2018

2023

2010

2009

Current release

1.1.263, October 2022

1.0, March 2023

3.14.1, January 2024

License

Commercial or Open Source

commercial

Open Source

BSL 1.1

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Server operating systems

Windows

Linux

hosted

BSD
Linux
OS X
Windows

Data scheme

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

scheme-free

yes

schema-free

Typing

predefined data types such as float or date

yes

strings, hashes, lists, sets, sorted sets, bit arrays

yes

integer and binary

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

with tags

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

Proprietary protocol

RESP - REdis Serialization Protocol

RESTful HTTP/JSON API

HTTP API

Supported programming languages

.Net
C
C#
C++
VB.Net

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

Lua

user defined functions

in JavaScript

Triggers

no, except callback-events from server when changes happened

publish/subscribe channels provide some trigger functionality

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

none

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic execution of command blocks and scripts

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

yes

Durability

Support for making data persistent

yes

by using LevelDB

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

Windows-Profile

Password-based authentication

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Cryptographically strong user authentication and audit trail

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