DBMS > Amazon DynamoDB vs. Google Cloud Firestore vs. Manticore Search vs. Titan vs. ToroDB

System Properties Comparison Amazon DynamoDB vs. Google Cloud Firestore vs. Manticore Search vs. Titan vs. ToroDB

Editorial information provided by DB-Engines

Name

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.

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

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.

Multi-storage database for search, including full-text search.

Titan is a Graph DBMS optimized for distributed clusters.

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Document store
Key-value store

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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	0.22
Rank	#312	Overall
	#21	Search engines

Website

aws.amazon.com/dynamodb

firebase.google.com/products/firestore

manticoresearch.com

github.com/thinkaurelius/titan

github.com/torodb/server

Technical documentation

docs.aws.amazon.com/dynamodb

firebase.google.com/docs/firestore

manual.manticoresearch.com

github.com/thinkaurelius/titan/wiki

Developer

Amazon

Google

Manticore Software

Aurelius, owned by DataStax

8Kdata

Initial release

2012

2017

2012

2016

Current release

6.0, February 2023

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

GPL version 2

Open Source

Apache license, version 2.0

Open Source

AGPL-V3

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++

Java

Server operating systems

hosted

FreeBSD
Linux
macOS
Windows

Linux
OS X
Unix
Windows

All OS with a Java 7 VM

Data scheme

schema-free

Fixed schema

yes

schema-free

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

yes

string, integer, double, boolean, date, object_id

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 index from XML

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like query language

APIs and other access methods

RESTful HTTP API

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

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

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

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

Clojure
Java
Python

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

user defined functions

yes

Triggers

yes

by integration with AWS Lambda

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Partitioning is done manually, search queries against distributed index is supported

yes

via pluggable storage backends

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

Synchronous replication based on Galera library

yes

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

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

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

isolated transactions for atomic changes and binary logging for safe writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

yes

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

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.

User authentification and security via Rexster Graph Server

Access rights for users and roles

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