DBMS > DuckDB vs. Google Cloud Firestore vs. Hazelcast vs. MySQL vs. Rockset

System Properties Comparison DuckDB vs. Google Cloud Firestore vs. Hazelcast vs. MySQL vs. Rockset

Editorial information provided by DB-Engines

Name

Description

An embeddable, in-process, column-oriented SQL OLAP RDBMS

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 widely adopted in-memory data grid

Widely used open source RDBMS

A scalable, reliable search and analytics service in the cloud, built on RocksDB

Primary database model

Key/Value like access via memcached API

Document store

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
Spatial DBMS

Relational DBMS
Search engine

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

Score	4.68
Rank	#76	Overall
	#41	Relational DBMS

Score	8.96
Rank	#48	Overall
	#8	Document stores

Score	6.87
Rank	#55	Overall
	#6	Key-value stores

Score	1087.72
Rank	#2	Overall
	#2	Relational DBMS

Score	0.84
Rank	#209	Overall
	#35	Document stores

Website

duckdb.org

firebase.google.com/products/firestore

hazelcast.com

www.mysql.com

rockset.com

Technical documentation

duckdb.org/docs

firebase.google.com/docs/firestore

hazelcast.org/imdg/docs

dev.mysql.com/doc

docs.rockset.com

Developer

Google

Hazelcast

Oracle

since 2010, originally MySQL AB, then Sun

Rockset

Initial release

2018

2017

2008

1995

2019

Current release

0.10, February 2024

5.3.6, November 2023

8.3.0, January 2024

License

Commercial or Open Source

Open Source

MIT License

commercial

Open Source

Apache Version 2; commercial licenses 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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Implementation language

C++

Java

C and C++

C++

Server operating systems

server-less

hosted

All OS with a Java VM

FreeBSD
Linux
OS X
Solaris
Windows

hosted

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

dynamic typing

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

the object must implement a serialization strategy

yes

ingestion from XML files supported

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

yes

SQL-like query language

yes

with proprietary extensions

Read-only SQL queries, including JOINs

APIs and other access methods

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

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

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP REST

Supported programming languages

C
C#

3rd party driver
C++
Crystal

3rd party driver
Go

3rd party driver
Java
Lisp

3rd party driver
Python
R
Ruby

3rd party driver
Rust
Swift
Zig

3rd party driver

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

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

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

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

yes

Event Listeners, Executor Services

yes

proprietary syntax

Triggers

yes, with Cloud Functions

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Automatic sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

yes

Replicated Map

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

yes

one or two-phase-commit; repeatable reads; read commited

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

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

Role-based access control

Users with fine-grained authorization concept

no user groups or roles

Access rights for users and organizations can be defined via Rockset console

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