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DBMS > DolphinDB vs. FatDB vs. Google Cloud Datastore vs. Infobright vs. SQL.JS

System Properties Comparison DolphinDB vs. FatDB vs. Google Cloud Datastore vs. Infobright vs. SQL.JS

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

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.

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

Port of SQLite to JavaScript

Primary database model

Time Series DBMS

Document store
Key-value store

Relational DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	4.03
Rank	#78	Overall
	#6	Time Series DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	1.02
Rank	#192	Overall
	#90	Relational DBMS

Score	0.63
Rank	#241	Overall
	#112	Relational DBMS

Website

www.dolphindb.com

cloud.google.com/datastore

ignitetech.com/softwarelibrary/infobrightdb

Technical documentation

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

cloud.google.com/datastore/docs

sql.js.org/documentation/index.html

Developer

DolphinDB, Inc

FatCloud

Google

Ignite Technologies Inc.; formerly InfoBright Inc.

Alon Zakai

enhancements implemented by others

Initial release

2018

2012

2008

2005

2012

Current release

v2.00.4, January 2022

License

Commercial or Open Source

commercial

free community version available

commercial

commercial

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

Open Source

Cloud-based only

Only available as a cloud service

no

no

yes

no

no

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

C#

C

JavaScript

Server operating systems

Linux
Windows

Windows

hosted

Linux
Windows

server-less, requires a JavaScript environment (browser, Node.js)

Data scheme

yes

schema-free

schema-free

yes

yes

Typing

predefined data types such as float or date

yes

yes

yes, details here

yes

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.

no

no

no

no

Secondary indexes

yes

yes

yes

no

Knowledge Grid Technology used instead

SQL

Support of SQL

SQL-like query language

no

Via inetgration in SQL Server

SQL-like query language (GQL)

yes

yes

SQL-92 is not fully supported

APIs and other access methods

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

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

JavaScript API

Supported programming languages

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

C#

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

JavaScript

Server-side scripts

Stored procedures

yes

yes

via applications

using Google App Engine

no

no

Triggers

no

yes

via applications

Callbacks using the Google Apps Engine

no

no

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Sharding

none

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Multi-source replication using Paxos

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes

yes

using Google Cloud Dataflow

no

no

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

none

Foreign keys

Referential integrity

no

no

yes

via ReferenceProperties or Ancestor paths

no

yes

Transaction concepts

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

yes

no

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes

yes

yes

Durability

Support for making data persistent

yes

yes

yes

yes

no

except by serializing a db to a file

In-memory capabilities

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

yes

no

yes

yes

User concepts

Access control

Administrators, Users, Groups

no

Can implement custom security layer via applications

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

no

More information provided by the system vendor

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More resources

DB-Engines blog posts

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1 December 2014, Matthias Gelbmann

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