DBMS > Alibaba Cloud AnalyticDB for PostgreSQL vs. Alibaba Cloud Table Store vs. BigObject vs. Google Cloud Firestore vs. MySQL

System Properties Comparison Alibaba Cloud AnalyticDB for PostgreSQL vs. Alibaba Cloud Table Store vs. BigObject vs. Google Cloud Firestore vs. MySQL

Editorial information provided by DB-Engines

Name

Description

An online MPP (Massively Parallel Processing) data warehousing service based on Greenplum

A fully managed Wide Column Store for large quantities of semi-structured data with real-time access

Analytic DBMS for real-time computations and queries

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.

Widely used open source RDBMS

Primary database model

Relational DBMS

Wide column store

Relational DBMS

a hierachical model (tree) can be imposed

Document store

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store
Spatial DBMS

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

Score	0.76
Rank	#220	Overall
	#102	Relational DBMS

Score	0.31
Rank	#297	Overall
	#11	Wide column stores

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	7.36
Rank	#53	Overall
	#9	Document stores

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Website

www.alibabacloud.com/product/hybriddb-postgresql

www.alibabacloud.com/product/table-store

bigobject.io

firebase.google.com/products/firestore

www.mysql.com

Technical documentation

www.alibabacloud.com/help/en/analyticdb-for-postgresql/latest/product-introduction-overview

www.alibabacloud.com/help/en/tablestore

docs.bigobject.io

firebase.google.com/docs/firestore

dev.mysql.com/doc

Developer

Alibaba / Pivotal Software Inc. / PostgreSQL Global Development Group

Alibaba

BigObject, Inc.

Google

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2016

2015

2017

1995

Current release

8.4.0, April 2024

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C and C++

Server operating systems

hosted

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

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

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC

HTTP API

fluentd
ODBC
RESTful HTTP API

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

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C
Java
Perl
Python
R

Java
Python

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

yes

Lua

yes, Firebase Rules & Cloud Functions

yes

proprietary syntax

Triggers

yes

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

none

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

Implicit feature of the cloud service

none

Multi-source replication

Multi-source replication
Source-replica replication

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

none

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Atomic single-row operations

yes

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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 based on subaccounts and tokens

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

Users with fine-grained authorization concept

no user groups or roles

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