Thursday, December 26, 2013

Balancing Strong and Eventual Consistency with Google Cloud Datastore

This document discusses achieving strong consistency for a positive user experience, while embracing Google Cloud Datastore’s eventual consistency model for handling large quantities of data and users.

This document is intended for software architects and engineers wanting to build solutions on Google Cloud Datastore. To help readers who are more familiar with relational databases than non-relational systems like Google Cloud Datastore, this document points out analogous concepts in relational databases.

NoSQL and Eventual Consistency

Non-relational databases, also known as NoSQL databases, have emerged in recent years as an alternative to relational databases. Google Cloud Datastore is one of the most widely used non-relational databases in the industry. In 2013 Google Cloud Datastore processed 4.5 trillion transactions per month (Google Cloud Platform blog post). It provides a simplified way for developers to store and access data. The flexible schema maps naturally to object-oriented and scripting languages. Google Cloud Datastore also provides a number of features that relational databases are not optimally suited to provide, including high-performance at a very large scale and high-reliability.

To developers more accustomed to relational databases, it may be challenging to design a system that leverages non-relational databases, as some characteristics and practices of non-relational databases may be relatively unfamiliar to them. Although the Google Cloud Datastore programming model is simple, it is important to be aware of these characteristics. Eventual consistency is one of these characteristics and programming for eventual consistency is the main subject of this document.

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