DBMS > Apache Doris vs. CouchDB vs. Google Cloud Spanner vs. MySQL

System Properties Comparison Apache Doris vs. CouchDB vs. Google Cloud Spanner vs. MySQL

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Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

Widely used open source RDBMS

Primary database model

Relational DBMS

Document store

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Spatial DBMS

using the Geocouch extension

Document store
Spatial DBMS

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

Score	0.61
Rank	#235	Overall
	#110	Relational DBMS

Score	7.46
Rank	#51	Overall
	#7	Document stores

Score	2.69
Rank	#101	Overall
	#51	Relational DBMS

Score	1029.49
Rank	#2	Overall
	#2	Relational DBMS

Website

doris.apache.org
github.com/apache/doris

couchdb.apache.org

cloud.google.com/spanner

www.mysql.com

Technical documentation

github.com/apache/doris/wiki

docs.couchdb.org/en/stable

cloud.google.com/spanner/docs

dev.mysql.com/doc

Developer

Apache Software Foundation, originally contributed from Baidu

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Google

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2017

2005

2017

1995

Current release

1.2.2, February 2023

3.3.3, December 2023

9.0.0, July 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache version 2

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Erlang

C and C++

Server operating systems

Linux

Android
BSD
Linux
OS X
Solaris
Windows

hosted

FreeBSD
Linux
OS X
Solaris
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

via views

yes

SQL

Support of SQL

yes

Query statements complying to ANSI 2011

yes

with proprietary extensions

APIs and other access methods

JDBC
MySQL client

RESTful HTTP/JSON API

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Java

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

Go
Java
JavaScript (Node.js)
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

View functions in JavaScript

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

improved architecture with release 2.0

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

Multi-source replication with 3 replicas for regional instances.

Multi-source replication
Source-replica replication

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

atomic operations within a single document possible

ACID

Strict serializable isolation

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

table locks or row locks depending on storage engine

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

fine grained access rights according to SQL-standard

Access rights for users can be defined per database

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

Users with fine-grained authorization concept

no user groups or roles

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