DBMS > DolphinDB vs. Firebase Realtime Database vs. Oracle Coherence

System Properties Comparison DolphinDB vs. Firebase Realtime Database vs. Oracle Coherence

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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.

Oracles in-memory data grid solution

Primary database model

Time Series DBMS
Vector DBMS

Document store

Key-value store

Secondary database models

Relational DBMS

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

Score	2.72
Rank	#100	Overall
	#8	Time Series DBMS
	#5	Vector DBMS

Score	13.60
Rank	#39	Overall
	#6	Document stores

Score	1.90
Rank	#127	Overall
	#21	Key-value stores

Website

www.dolphindb.com

firebase.google.com/products/realtime-database

www.oracle.com/java/coherence

Technical documentation

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

firebase.google.com/docs/database

docs.oracle.com/en/middleware/standalone/coherence

Developer

DolphinDB, Inc

Google

acquired by Google 2014

Oracle

Initial release

2018

2012

2007

Current release

v2.00.4, January 2022

14.1, August 2023

License

Commercial or Open Source

commercial

free community version available

commercial

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

Server operating systems

Linux
Windows

hosted

All OS with a Java VM

Data scheme

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-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

JCache
JPA
RESTful HTTP API

Supported programming languages

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

Java
JavaScript
Objective-C

.Net
C++
Java

Server-side scripts

Stored procedures

yes

limited functionality with using 'rules'

Triggers

Callbacks are triggered when data changes

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, with selectable consistency level

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

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

configurable

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

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

authentification to access the cache via certificates or http basic authentication

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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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