DBMS > Amazon DocumentDB vs. FatDB vs. Google Cloud Datastore vs. H2GIS vs. Microsoft SQL Server

System Properties Comparison Amazon DocumentDB vs. FatDB vs. Google Cloud Datastore vs. H2GIS vs. Microsoft SQL Server

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Spatial extension of H2

Microsofts flagship relational DBMS

Primary database model

Document store

Document store
Key-value store

Document store

Spatial DBMS

Relational DBMS

Secondary database models

Relational DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Website

aws.amazon.com/documentdb

cloud.google.com/datastore

www.h2gis.org

www.microsoft.com/en-us/sql-server

Technical documentation

aws.amazon.com/documentdb/resources

cloud.google.com/datastore/docs

www.h2gis.org/docs/home

learn.microsoft.com/en-US/sql/sql-server

Developer

FatCloud

Google

CNRS

Microsoft

Initial release

2019

2012

2008

2013

1989

Current release

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

Open Source

LGPL 3.0

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

Server operating systems

hosted

Windows

hosted

Linux
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

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.

yes

Secondary indexes

yes

SQL

Support of SQL

Via inetgration in SQL Server

SQL-like query language (GQL)

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes

via applications

using Google App Engine

yes

based on H2

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

via applications

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

selectable replication factor

Multi-source replication using Paxos

yes

based on H2

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

Atomic single-document operations

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

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 implement custom security layer via applications

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

yes

based on H2

fine grained access rights according to SQL-standard

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