DBMS > Databend vs. Google Cloud Firestore vs. H2GIS vs. Lovefield

System Properties Comparison Databend vs. Google Cloud Firestore vs. H2GIS vs. Lovefield

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Editorial information provided by DB-Engines

Name

Description

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

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.

Spatial extension of H2

Embeddable relational database for web apps written in pure JavaScript

Primary database model

Secondary database models

Relational DBMS

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

Score	0.34
Rank	#283	Overall
	#130	Relational DBMS

Score	7.36
Rank	#53	Overall
	#9	Document stores

Score	0.08
Rank	#368	Overall
	#7	Spatial DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Website

github.com/datafuselabs/databend
www.databend.com

firebase.google.com/products/firestore

www.h2gis.org

google.github.io/lovefield

Technical documentation

docs.databend.com

firebase.google.com/docs/firestore

www.h2gis.org/docs/home

github.com/google/lovefield/blob/master/docs/spec_index.md

Developer

Databend Labs

Google

CNRS

Google

Initial release

2021

2017

2013

2014

Current release

1.0.59, April 2023

2.1.12, February 2017

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

LGPL 3.0

Open Source

Apache 2.0

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

Rust

Java

JavaScript

Server operating systems

hosted
Linux
macOS

hosted

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

tested with Chrome, Firefox, IE, Safari

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

yes

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

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

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

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

Java

JavaScript

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

yes

based on H2

Triggers

yes, with Cloud Functions

yes

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

yes

based on H2

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

yes, by using IndexedDB or the cloud service Firebase Realtime Database

In-memory capabilities

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

yes

using MemoryDB

User concepts

Access control

Users with fine-grained authorization concept, user roles

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

yes

based on H2

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More resources

DB-Engines blog posts

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12 December 2019, Paul Andlinger

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