DBMS > Amazon DynamoDB vs. Google Cloud Firestore vs. IRONdb vs. Titan

System Properties Comparison Amazon DynamoDB vs. Google Cloud Firestore vs. IRONdb vs. Titan

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Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Titan has been decommisioned after the takeover by Datastax. It will be removed from the DB-Engines ranking. A fork has been open-sourced as JanusGraph.

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 distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Titan is a Graph DBMS optimized for distributed clusters.

Primary database model

Document store
Key-value store

Document store

Time Series DBMS

Graph 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

Website

aws.amazon.com/dynamodb

firebase.google.com/products/firestore

www.circonus.com/solutions/time-series-database/

github.com/thinkaurelius/titan

Technical documentation

docs.aws.amazon.com/dynamodb

firebase.google.com/docs/firestore

docs.circonus.com/irondb/category/getting-started

github.com/thinkaurelius/titan/wiki

Developer

Amazon

Google

Circonus LLC.

Aurelius, owned by DataStax

Initial release

2012

2017

2012

Current release

V0.10.20, January 2018

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

Apache license, version 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

C and C++

Java

Server operating systems

hosted

Linux

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

SQL-like query language (Circonus Analytics Query Language: CAQL)

APIs and other access methods

RESTful HTTP API

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

HTTP API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

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++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

Clojure
Java
Python

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

yes, in Lua

yes

Triggers

yes

by integration with AWS Lambda

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

yes

via pluggable storage backends

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

configurable replication factor, datacenter aware

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Using Cloud Dataflow

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graph

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

In-memory capabilities

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

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.

User authentification and security via Rexster Graph Server

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