DBMS > Amazon DynamoDB vs. Google Cloud Firestore vs. H2GIS vs. TerminusDB vs. Vitess

System Properties Comparison Amazon DynamoDB vs. Google Cloud Firestore vs. H2GIS vs. TerminusDB vs. Vitess

Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

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

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Document store
Key-value store

Secondary database models

Relational DBMS

Document store
RDF store

Document store
Spatial DBMS

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

Score	74.45
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	7.36
Rank	#53	Overall
	#9	Document stores

Score	0.08
Rank	#368	Overall
	#7	Spatial DBMS

Score	0.23
Rank	#316	Overall
	#27	Graph DBMS

Score	0.88
Rank	#203	Overall
	#95	Relational DBMS

Website

aws.amazon.com/dynamodb

firebase.google.com/products/firestore

www.h2gis.org

terminusdb.com

vitess.io

Technical documentation

docs.aws.amazon.com/dynamodb

firebase.google.com/docs/firestore

www.h2gis.org/docs/home

terminusdb.github.io/terminusdb/#

vitess.io/docs

Developer

Amazon

Google

CNRS

DataChemist Ltd.

The Linux Foundation, PlanetScale

Initial release

2012

2017

2013

2018

2013

Current release

11.0.0, January 2023

15.0.2, December 2022

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

LGPL 3.0

Open Source

GPL V3

Open Source

Apache Version 2.0, commercial licenses 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

Java

Prolog, Rust

Server operating systems

hosted

Linux

Docker
Linux
macOS

Data scheme

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 (WOQL)

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP API

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

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

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

Java

JavaScript
Python

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

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

yes

based on H2

yes

proprietary syntax

Triggers

yes

by integration with AWS Lambda

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Graph Partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

yes

based on H2

Journaling Streams

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Using Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

yes

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

in-memory journaling

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 and roles can be defined via the AWS Identity and Access Management (IAM)

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

Role-based access control

Users with fine-grained authorization concept

no user groups or roles

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