DBMS > Google Cloud Firestore vs. Kingbase vs. Memcached vs. Microsoft SQL Server vs. Yanza

System Properties Comparison Google Cloud Firestore vs. Kingbase vs. Memcached vs. Microsoft SQL Server vs. Yanza

Editorial information provided by DB-Engines

Name

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

Description

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.

An enterprise-class RDBMS compatible with PostgreSQL and Oracle and widely used in China.

In-memory key-value store, originally intended for caching

Microsofts flagship relational DBMS

Time Series DBMS for IoT Applications

Primary database model

Secondary database models

Document store
Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	8.96
Rank	#48	Overall
	#8	Document stores

Score	0.45
Rank	#265	Overall
	#124	Relational DBMS

Score	20.74
Rank	#32	Overall
	#4	Key-value stores

Score	829.80
Rank	#3	Overall
	#3	Relational DBMS

Website

firebase.google.com/products/firestore

www.kingbase.com.cn

www.memcached.org

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

yanza.com

Technical documentation

firebase.google.com/docs/firestore

github.com/memcached/memcached/wiki

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

Developer

Google

BeiJing KINGBASE Information technologies inc.

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Microsoft

Yanza

Initial release

2017

1999

2003

1989

2015

Current release

V8.0, August 2021

1.6.25, March 2024

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

Open Source

BSD license

commercial

restricted free version is available

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C and Java

C++

Server operating systems

hosted

Linux
Windows

FreeBSD
Linux
OS X
Unix
Windows

Linux
Windows

Windows

Data scheme

schema-free

yes

schema-free

yes

schema-free

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

Standard with numerous extensions

yes

APIs and other access methods

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

ADO.NET
gokb
JDBC
kdbndp
ODBC
PDI
PDO
Pro*C
psycopg2
QT

Proprietary protocol

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

HTTP API

Supported programming languages

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

.Net
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

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

any language that supports HTTP calls

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

user defined functions

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

Triggers

yes, with Cloud Functions

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

none

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

none

Repcached, a Memcached patch, provides this functionallity

yes, but depending on the SQL-Server Edition

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

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

User concepts

Access control

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

fine grained access rights according to SQL-standard

yes

using SASL (Simple Authentication and Security Layer) protocol

fine grained access rights according to SQL-standard

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