DBMS > DolphinDB vs. Firebase Realtime Database vs. Graph Engine vs. H2 vs. RethinkDB

System Properties Comparison DolphinDB vs. Firebase Realtime Database vs. Graph Engine vs. H2 vs. RethinkDB

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.

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Primary database model

Time Series DBMS

Document store

Graph DBMS
Key-value store

Relational DBMS

Document store

Secondary database models

Relational DBMS

Spatial DBMS

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

Score	4.23
Rank	#82	Overall
	#6	Time Series DBMS

Score	15.00
Rank	#39	Overall
	#6	Document stores

Score	0.62
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	8.22
Rank	#50	Overall
	#32	Relational DBMS

Score	2.84
Rank	#106	Overall
	#19	Document stores

Website

www.dolphindb.com

firebase.google.com/products/realtime-database

www.graphengine.io

www.h2database.com

rethinkdb.com

Technical documentation

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

firebase.google.com/docs/database

www.graphengine.io/docs/manual

www.h2database.com/html/main.html

rethinkdb.com/docs

Developer

DolphinDB, Inc

Google

acquired by Google 2014

Microsoft

Thomas Mueller

The Linux Foundation

since July 2017

Initial release

2018

2012

2010

2005

2009

Current release

v2.00.4, January 2022

2.2.220, July 2023

2.4.1, August 2020

License

Commercial or Open Source

commercial

free community version available

commercial

Open Source

MIT License

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

Apache Version 2

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++

.NET and C

Java

C++

Server operating systems

Linux
Windows

hosted

.NET

All OS with a Java VM

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

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-like query language

yes

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

RESTful HTTP API

JDBC
ODBC

Supported programming languages

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

Java
JavaScript
Objective-C

C#
C++
F#
Visual Basic

Java

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Server-side scripts

Stored procedures

yes

limited functionality with using 'rules'

yes

Java Stored Procedures and User-Defined Functions

Triggers

Callbacks are triggered when data changes

yes

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

Sharding

range based

Replication methods

Methods for redundantly storing data on multiple nodes

yes

With clustering: 2 database servers on different computers operate on identical copies of a database

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

yes

Transaction concepts

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

yes

ACID

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

MVCC based

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

yes

In-memory capabilities

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

yes

User concepts

Access control

Administrators, Users, Groups

yes, based on authentication and database rules

fine grained access rights according to SQL-standard

yes

users and table-level permissions

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More resources

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