DBMS > Amazon DynamoDB vs. Google Cloud Firestore vs. HarperDB vs. OrigoDB

System Properties Comparison Amazon DynamoDB vs. Google Cloud Firestore vs. HarperDB vs. OrigoDB

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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.

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

A fully ACID in-memory object graph database

Primary database model

Document store
Key-value store

Document store

Document store
Object oriented DBMS

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

Score	77.57
Rank	#16	Overall
	#2	Document stores
	#2	Key-value stores

Score	8.96
Rank	#48	Overall
	#8	Document stores

Score	0.55
Rank	#252	Overall
	#38	Document stores

Score	0.03
Rank	#378	Overall
	#51	Document stores
	#18	Object oriented DBMS

Website

aws.amazon.com/dynamodb

firebase.google.com/products/firestore

www.harperdb.io

origodb.com

Technical documentation

docs.aws.amazon.com/dynamodb

firebase.google.com/docs/firestore

docs.harperdb.io/docs

origodb.com/docs

Developer

Amazon

Google

HarperDB

Robert Friberg et al

Initial release

2012

2017

2009

under the name LiveDB

Current release

3.1, August 2021

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

free community edition available

Open Source

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

Node.js

Server operating systems

hosted

Linux
OS X

Linux
Windows

Data scheme

schema-free

dynamic schema

yes

Typing

predefined data types such as float or date

yes

JSON data types

User defined using .NET types and collections

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.

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

SQL-like data manipulation statements

APIs and other access methods

RESTful HTTP API

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

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

.NET Client API
HTTP API
LINQ

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

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

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

.Net

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

Custom Functions

since release 3.1

yes

Triggers

yes

by integration with AWS Lambda

yes, with Cloud Functions

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

A table resides as a whole on one (or more) nodes in a cluster

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

yes

the nodes on which a table resides can be defined

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

Foreign keys

Referential integrity

depending on model

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

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

yes

Write ahead log

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.

Access rights for users and roles

Role based authorization

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