DBMS > Derby vs. Google Cloud Firestore vs. GridDB vs. MySQL vs. Snowflake

System Properties Comparison Derby vs. Google Cloud Firestore vs. GridDB vs. MySQL vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

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

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.

Scalable in-memory time series database optimized for IoT and Big Data

Widely used open source RDBMS

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Key-value store
Relational DBMS

Document store
Spatial DBMS

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

Score	4.60
Rank	#70	Overall
	#38	Relational DBMS

Score	7.36
Rank	#53	Overall
	#9	Document stores

Score	2.09
Rank	#120	Overall
	#10	Time Series DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

db.apache.org/derby

firebase.google.com/products/firestore

griddb.net

www.mysql.com

www.snowflake.com

Technical documentation

db.apache.org/derby/manuals/index.html

firebase.google.com/docs/firestore

docs.griddb.net

dev.mysql.com/doc

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

Google

Toshiba Corporation

Oracle

since 2010, originally MySQL AB, then Sun

Snowflake Computing Inc.

Initial release

1997

2017

2013

1995

2014

Current release

10.17.1.0, November 2023

5.1, August 2022

8.4.0, April 2024

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Cloud-based only

Only available as a cloud service

yes

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Database as a Service

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Implementation language

Java

C++

C and C++

Server operating systems

All OS with a Java VM

hosted

Linux

FreeBSD
Linux
OS X
Solaris
Windows

hosted

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL92, SQL-like TQL (Toshiba Query Language)

yes

with proprietary extensions

yes

APIs and other access methods

JDBC

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

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
Proprietary native API

CLI Client
JDBC
ODBC

Supported programming languages

Java

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

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Java Stored Procedures

yes, Firebase Rules & Cloud Functions

yes

proprietary syntax

user defined functions

Triggers

yes

yes, with Cloud Functions

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication

Source-replica replication

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

yes

ACID at container level

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

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

fine grained access rights according to SQL-standard

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

Access rights for users can be defined per database

Users with fine-grained authorization concept

no user groups or roles

Users with fine-grained authorization concept, user roles and pluggable authentication

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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