DBMS > Databend vs. Fauna vs. GBase vs. Machbase Neo

System Properties Comparison Databend vs. Fauna vs. GBase vs. Machbase Neo

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Editorial information provided by DB-Engines

Name

Description

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

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.

TimeSeries DBMS for AIoT and BigData

Primary database model

Relational DBMS

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Relational DBMS

Time Series DBMS

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

Score	0.30
Rank	#287	Overall
	#130	Relational DBMS

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	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Website

github.com/datafuselabs/databend
www.databend.com

fauna.com

www.gbase.cn

machbase.com

Technical documentation

docs.databend.com

docs.fauna.com

machbase.com/dbms

Developer

Databend Labs

Fauna, Inc.

General Data Technology Co., Ltd.

Machbase

Initial release

2021

2014

2004

2013

Current release

1.0.59, April 2023

GBase 8a, GBase 8s, GBase 8c

V8.0, August 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free test version available

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

Rust

Scala

C, Java, Python

Server operating systems

hosted
Linux
macOS

hosted

Linux

Linux
macOS
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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

yes

Standard with numerous extensions

SQL-like query language

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

RESTful HTTP API

ADO.NET
C API
JDBC
ODBC

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

consistent hashing

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

yes

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

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

volatile and lookup table

User concepts

Access control

Users with fine-grained authorization concept, user roles

Identity management, authentication, and access control

yes

simple password-based access control

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