DBMS > Fauna vs. GBase vs. Google Cloud Datastore vs. Microsoft SQL Server vs. Rockset

System Properties Comparison Fauna vs. GBase vs. Google Cloud Datastore vs. Microsoft SQL Server vs. Rockset

Editorial information provided by DB-Engines

Name

Description

Fauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

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

Microsofts flagship relational DBMS

A scalable, reliable search and analytics service in the cloud, built on RocksDB

Primary database model

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Relational DBMS
Search engine

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

Score	1.52
Rank	#153	Overall
	#26	Document stores
	#14	Graph DBMS
	#71	Relational DBMS
	#13	Time Series DBMS

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.79
Rank	#211	Overall
	#35	Document stores

Website

fauna.com

www.gbase.cn

cloud.google.com/datastore

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

rockset.com

Technical documentation

docs.fauna.com

cloud.google.com/datastore/docs

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

docs.rockset.com

Developer

Fauna, Inc.

General Data Technology Co., Ltd.

Google

Microsoft

Rockset

Initial release

2014

2004

2008

1989

2019

Current release

GBase 8a, GBase 8s, GBase 8c

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

restricted free version is available

commercial

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

Scala

C, Java, Python

C++

Server operating systems

hosted

Linux

hosted

Linux
Windows

hosted

Data scheme

schema-free

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

yes

dynamic typing

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

ingestion from XML files supported

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

Standard with numerous extensions

SQL-like query language (GQL)

yes

Read-only SQL queries, including JOINs

APIs and other access methods

RESTful HTTP API

ADO.NET
C API
JDBC
ODBC

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

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

HTTP REST

Supported programming languages

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

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

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

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

using Google App Engine

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

Triggers

yes

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

consistent hashing

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

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

Automatic sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

Multi-source replication using Paxos

yes, but depending on the SQL-Server Edition

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

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

Eventual Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

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

Identity management, authentication, and access control

yes

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

fine grained access rights according to SQL-standard

Access rights for users and organizations can be defined via Rockset console

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