DBMS > Alibaba Cloud AnalyticDB for MySQL vs. Amazon Redshift vs. EJDB vs. Google Cloud Datastore vs. Hypertable

System Properties Comparison Alibaba Cloud AnalyticDB for MySQL vs. Amazon Redshift vs. EJDB vs. Google Cloud Datastore vs. Hypertable

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

A real-time data warehousing service that can process petabytes of data with high concurrency and low latency. It is fully compatible with the MySQL protocol.

Large scale data warehouse service for use with business intelligence tools

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

An open source BigTable implementation based on distributed file systems such as Hadoop

Primary database model

Relational DBMS

Document store

Wide column store

Secondary database models

Document store

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

Score	0.85
Rank	#207	Overall
	#96	Relational DBMS

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Website

www.alibabacloud.com/product/analyticdb-for-mysql

aws.amazon.com/redshift

github.com/Softmotions/ejdb

cloud.google.com/datastore

Technical documentation

www.alibabacloud.com/help/doc-detail/93776.htm

docs.aws.amazon.com/redshift

github.com/Softmotions/ejdb/blob/master/README.md

cloud.google.com/datastore/docs

Developer

Alibaba

Amazon (based on PostgreSQL)

Softmotions

Google

Hypertable Inc.

Initial release

2012

2008

2009

Current release

0.9.8.11, March 2016

License

Commercial or Open Source

commercial

Open Source

GPLv2

commercial

Open Source

GNU version 3. Commercial license 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

C++

Server operating systems

hosted

server-less

hosted

Linux
OS X
Windows

an inofficial Windows port is available

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes, details here

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

restricted

yes

restricted

only exact value or prefix value scans

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language (GQL)

APIs and other access methods

JDBC
ODBC

in-process shared library

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

C++ API
Thrift

Supported programming languages

C#
Java
PHP
Python

All languages supporting JDBC/ODBC

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

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

C++
Java
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions

in Python

using Google App Engine

Triggers

yes

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

none

Multi-source replication using Paxos

selectable replication factor on file system level

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

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

informational only, not enforced by the system

typically not needed, however similar functionality with collection joins possible

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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

fine grained access rights according to SQL-standard

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

More information provided by the system vendor

Specific characteristics

A real-time data warehousing service that can process PB data with high concurrency...
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Competitive advantages

TPC Benchmark: The world leading result in TPC-DS benchmark . TPC-H benchmark for...
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Licensing and pricing models

Available regions: America US Virginia US Silicon Valley Asia China Hong Kong India...
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