DBMS > DolphinDB vs. Google Cloud Firestore vs. Graph Engine vs. H2 vs. TDengine

System Properties Comparison DolphinDB vs. Google Cloud Firestore vs. Graph Engine vs. H2 vs. TDengine

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 Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

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.

Time Series DBMS and big data platform

Primary database model

Time Series DBMS

Document store

Graph DBMS
Key-value store

Relational DBMS

Time Series DBMS

Secondary database models

Relational DBMS

Spatial DBMS

Relational DBMS

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

Score	4.23
Rank	#82	Overall
	#6	Time Series DBMS

Score	8.96
Rank	#48	Overall
	#8	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	2.68
Rank	#109	Overall
	#8	Time Series DBMS

Website

www.dolphindb.com

firebase.google.com/products/firestore

www.graphengine.io

www.h2database.com

github.com/taosdata/TDengine
tdengine.com

Technical documentation

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

firebase.google.com/docs/firestore

www.graphengine.io/docs/manual

www.h2database.com/html/main.html

docs.tdengine.com

Developer

DolphinDB, Inc

Google

Microsoft

Thomas Mueller

TDEngine, previously Taos Data

Initial release

2018

2017

2010

2005

2019

Current release

v2.00.4, January 2022

2.2.220, July 2023

3.0, August 2022

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

GPL V3, also commercial editions available

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

Server operating systems

Linux
Windows

hosted

.NET

All OS with a Java VM

Linux
Windows

Data scheme

yes

schema-free

yes

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

yes

Standard SQL with extensions for time-series applications

APIs and other access methods

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

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

RESTful HTTP API

JDBC
ODBC

JDBC
RESTful HTTP API

Supported programming languages

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

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

C#
C++
F#
Visual Basic

Java

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

Server-side scripts

Stored procedures

yes

yes, Firebase Rules & Cloud Functions

yes

Java Stored Procedures and User-Defined Functions

Triggers

yes, with Cloud Functions

yes

yes, via alarm monitoring

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

horizontal partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

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

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Using Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

fine grained access rights according to SQL-standard

yes

More information provided by the system vendor

Specific characteristics

TDengine™ is a next generation data historian purpose-built for Industry 4.0 and...
» more

Competitive advantages

High Performance at any Scale: TDengine is purpose-built for handling massive industrial...
» more

Typical application scenarios

TDengine is designed for Industrial IoT scenarios, including: Manufacturing Connected...
» more

Market metrics

TDengine has garnered over 22,500 stars on GitHub and is used in over 50 countries...
» more

Licensing and pricing models

TDengine OSS is an open source, cloud native time series database. It includes built-in...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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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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