DBMS > DolphinDB vs. Firebase Realtime Database vs. H2 vs. HarperDB vs. MonetDB

System Properties Comparison DolphinDB vs. Firebase Realtime Database vs. H2 vs. HarperDB vs. MonetDB

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.

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

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

A relational database management system that stores data in columns

Primary database model

Secondary database models

Relational DBMS

Spatial DBMS

Document store
Spatial 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	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Website

www.dolphindb.com

firebase.google.com/products/realtime-database

www.h2database.com

www.harperdb.io

www.monetdb.org

Technical documentation

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

firebase.google.com/docs/database

www.h2database.com/html/main.html

docs.harperdb.io/docs

www.monetdb.org/Documentation

Developer

DolphinDB, Inc

Google

acquired by Google 2014

Thomas Mueller

HarperDB

MonetDB BV

Initial release

2018

2012

2005

2017

2004

Current release

v2.00.4, January 2022

2.2.220, July 2023

3.1, August 2021

Dec2023 (11.49), December 2023

License

Commercial or Open Source

commercial

free community version available

commercial

Open Source

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

commercial

free community edition available

Open Source

Mozilla Public License 2.0

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++

Java

Node.js

Server operating systems

Linux
Windows

hosted

All OS with a Java VM

Linux
OS X

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

dynamic schema

yes

Typing

predefined data types such as float or date

yes

JSON 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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

SQL-like data manipulation statements

yes

SQL 2003 with some extensions

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

JDBC
ODBC

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Supported programming languages

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

Java
JavaScript
Objective-C

Java

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

Server-side scripts

Stored procedures

yes

limited functionality with using 'rules'

Java Stored Procedures and User-Defined Functions

Custom Functions

since release 3.1

yes, in SQL, C, R

Triggers

Callbacks are triggered when data changes

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

A table resides as a whole on one (or more) nodes in a cluster

Sharding via remote tables

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

yes

the nodes on which a table resides can be defined

none

Source-replica replication available in experimental status

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 execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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

Access rights for users and roles

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

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