DBMS > Alibaba Cloud AnalyticDB for PostgreSQL vs. GBase vs. Google Cloud Datastore vs. Postgres-XL vs. Yanza

System Properties Comparison Alibaba Cloud AnalyticDB for PostgreSQL vs. GBase vs. Google Cloud Datastore vs. Postgres-XL vs. Yanza

Editorial information provided by DB-Engines

Name

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

Description

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

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

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Time Series DBMS for IoT Applications

Primary database model

Secondary database models

Document store
Spatial DBMS

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

Score	0.68
Rank	#231	Overall
	#105	Relational DBMS

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Website

www.alibabacloud.com/product/hybriddb-postgresql

www.gbase.cn

cloud.google.com/datastore

www.postgres-xl.org

yanza.com

Technical documentation

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

cloud.google.com/datastore/docs

www.postgres-xl.org/documentation

Developer

Alibaba / Pivotal Software Inc. / PostgreSQL Global Development Group

General Data Technology Co., Ltd.

Google

Yanza

Initial release

2016

2004

2008

2014

since 2012, originally named StormDB

2015

Current release

GBase 8a, GBase 8s, GBase 8c

10 R1, October 2018

License

Commercial or Open Source

commercial

Open Source

Mozilla public license

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, Java, Python

Server operating systems

hosted

Linux

hosted

Linux
macOS

Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

Standard with numerous extensions

SQL-like query language (GQL)

yes

distributed, parallel query execution

APIs and other access methods

JDBC
ODBC

ADO.NET
C API
JDBC
ODBC

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

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

HTTP API

Supported programming languages

C
Java
Perl
Python
R

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

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

any language that supports HTTP calls

Server-side scripts

Stored procedures

yes

user defined functions

using Google App Engine

user defined functions

Triggers

yes

Callbacks using the Google Apps Engine

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

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

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

Multi-source replication using Paxos

none

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

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

MVCC

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.

User concepts

Access control

fine grained access rights according to SQL-standard

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

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