azure storage_公共云存储服务的数据一致性模型：Amazon S3，Google Cloud Storage和Windows Azure Storage...

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概述

azure storage

The public cloud storage services like Amazon S3, Google Cloud Storage and Windows Azure Storage replicate the data to ensure high availability. On the other hand, with data being replicated, the storage services exhibits certain data consistency models. Different cloud service providers employ different data consistency models nowadays. In this post, we survey the data consistency models provided by the solutions from the three big players: Amazon S3 and DynamoDB, Google Cloud Storage and Windows Azure Storage.

Amazon S3，Google Cloud Storage和Windows Azure Storage等公共云存储服务可复制数据以确保高可用性。另一方面，在复制数据的情况下，存储服务表现出某些数据一致性模型。如今，不同的云服务提供商采用了不同的数据一致性模型。在本文中，我们调查了来自三个主要参与者的解决方案提供的数据一致性模型：Amazon S3和DynamoDB，Google Cloud Storage和Windows Azure Storage。

亚马逊S3∞ (Amazon S3 ∞)

Amazon S3 is a simple key-based object store service for the Internet. Amazon S3 buckets in all Regions provide read-after-write consistency for PUTS of new objects and eventual consistency for overwrite PUTS and DELETES. However, there is one exception: if a HEAD or GET request to a key name is made to find if the object exists before the object is create, Amazon S3 provides only eventual consistency. Updates to a single key are atomic.

Amazon S3是用于Internet的简单的基于密钥的对象存储服务。所有区域中的Amazon S3存储桶为新对象的PUTS提供写后读取一致性，并为覆盖PUTS和DELETES最终提供一致性 。但是，有一个例外：如果在创建对象之前发出了对键名的HEAD或GET请求以查找对象是否存在，则Amazon S3仅提供最终的一致性。单个密钥的更新是原子的。

More information on Amazon S3’s data consistency model is available at http://aws.amazon.com/s3/faqs/

有关Amazon S3数据一致性模型的更多信息，请访问http://aws.amazon.com/s3/faqs/

亚马逊DynamoDB ∞ (Amazon DynamoDB ∞)

DynamoDB is an Internet-scale NoSQL database service provided by AWS. Different from Amazon S3, DynamoDB allows the users to choose between a strongly consistent read and an eventually consistent read based on the needs when the users get an item. Of course, the strongly consistent read consumes more (2x) resource than the eventually consistent read. Read more about DynamoDB at http://aws.typepad.com/aws/2012/01/amazon-dynamodb-internet-scale-data-storage-the-nosql-way.html

DynamoDB是AWS提供的Internet规模的NoSQL数据库服务。与Amazon S3不同，DynamoDB允许用户在获得商品时根据需要在强一致性读取和最终一致性读取之间进行选择。当然，强一致性读取比最终一致性读取消耗更多（2x）资源。在http://aws.typepad.com/aws/2012/01/amazon-dynamodb-internet-scale-data-storage-the-nosql-way.html上了解有关DynamoDB的更多信息。

Google Cloud Storage∞ (Google Cloud Storage ∞)

Briefly, Google Cloud Storage provides strong global consistency for upload and delete operations and list operations in a region, and eventual consistency for object list operations across regions. For access controlling, granting is strongly consistent while revoking is eventual consistent. Additionally, the upload operations to Google Cloud Storage are atomic.

简而言之，Google Cloud Storage为区域中的上载和删除操作以及列表操作提供了强大的全局一致性 ，并最终为跨区域的对象列表操作提供了一致性 。对于访问控制，授予是高度一致的，而撤销最终是一致的 。此外，上传到Google Cloud Storage的操作是原子性的。

Caches, as usually, have a different consistency model from the storage itself. Cached objects from Google Cloud Storage that are publicly readable might not exhibit strong consistency.

通常，缓存与存储本身具有不同的一致性模型。来自Google Cloud Storage的可公开读取的缓存对象可能不具有很强的一致性。

For more information on Google Cloud Storage’s consistency model, please check https://cloud.google.com/storage/docs/consistency.

有关Google Cloud Storage一致性模型的更多信息，请检查https://cloud.google.com/storage/docs/consistency 。

Windows Azure存储∞ (Windows Azure Storage ∞)

Windows Azure Storage provides three properties that the CAP theorem claims are difficult to achieve at the same time: strong consistency, high availability, and partition tolerance. Brad Calder et al. published the design of the Windows Azure Storage in the paper Windows Azure Storage: A Highly Available Cloud Storage Service with Strong Consistency at SOSP’11.

Windows Azure存储提供CAP定理声称难以同时实现的三个属性：高度一致性 ，高可用性和分区容限。布拉德·卡尔德（Brad Calder）等人。在SOSP'11上的Windows Azure存储：具有高度一致性的高可用性云存储服务一文中发布了Windows Azure存储的设计。