DBMS > DolphinDB vs. Firebase Realtime Database vs. Quasardb

System Properties Comparison DolphinDB vs. Firebase Realtime Database vs. Quasardb

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Editorial information provided by DB-Engines

Name

Description

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

Distributed, high-performance timeseries database

Primary database model

Time Series DBMS

Document store

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

www.dolphindb.com

firebase.google.com/products/realtime-database

quasar.ai

Technical documentation

docs.dolphindb.cn/en/help200/index.html

firebase.google.com/docs/database

doc.quasar.ai/master

Developer

DolphinDB, Inc

Google

acquired by Google 2014

quasardb

Initial release

2018

2012

2009

Current release

v2.00.4, January 2022

3.14.1, January 2024

License

Commercial or Open Source

commercial

free community version available

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

Linux
Windows

hosted

BSD
Linux
OS X
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

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

SQL-like query language

APIs and other access methods

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

Android
iOS
JavaScript API
RESTful HTTP API

HTTP API

Supported programming languages

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

Java
JavaScript
Objective-C

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

Server-side scripts

Stored procedures

yes

limited functionality with using 'rules'

Triggers

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

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

Administrators, Users, Groups

yes, based on authentication and database rules

Cryptographically strong user authentication and audit trail

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More resources

DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

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