DBMS > Amazon DynamoDB vs. Google Cloud Firestore vs. HugeGraph vs. InfinityDB vs. OrigoDB

System Properties Comparison Amazon DynamoDB vs. Google Cloud Firestore vs. HugeGraph vs. InfinityDB vs. OrigoDB

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.

A fast-speed and highly-scalable Graph DBMS

A Java embedded Key-Value Store which extends the Java Map interface

A fully ACID in-memory object graph database

Primary database model

Document store
Key-value store

Document store

Graph DBMS

Key-value store

Document store
Object oriented DBMS

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Website

aws.amazon.com/dynamodb

firebase.google.com/products/firestore

github.com/hugegraph
hugegraph.apache.org

boilerbay.com

origodb.com

Technical documentation

docs.aws.amazon.com/dynamodb

firebase.google.com/docs/firestore

hugegraph.apache.org/docs

boilerbay.com/infinitydb/manual

origodb.com/docs

Developer

Amazon

Google

Baidu

Boiler Bay Inc.

Robert Friberg et al

Initial release

2012

2017

2018

2002

2009

under the name LiveDB

Current release

0.9

4.0

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

Apache Version 2.0

commercial

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

Java

Server operating systems

hosted

Linux
macOS
Unix

All OS with a Java VM

Linux
Windows

Data scheme

schema-free

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

also supports composite index and range index

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

APIs and other access methods

RESTful HTTP API

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

Java API
RESTful HTTP API
TinkerPop Gremlin

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

.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

Groovy
Java
Python

Java

.Net

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

asynchronous Gremlin script jobs

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

yes

depending on used storage backend, e.g. Cassandra and HBase

none

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

yes

depending on used storage backend, e.g. Cassandra and HBase

none

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

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Eventual Consistency

Immediate Consistency

READ-COMMITTED or SERIALIZED

Foreign keys

Referential integrity

yes

edges in graph

manual creation possible, using inversions based on multi-value capability

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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.

Users, roles and permissions

Role based authorization

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