Microsoft SQL Azure - Scaling Out with SQL Azure Whitepaper
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SQL Azure Database is a cloud database service from Microsoft. SQL Azure provides web-facing database functionality as a utility service. Cloud-based database solutions such as SQL Azure can provide many benefits, including rapid provisioning, cost-effective scalability, high availability, and reduced management overhead. This paper provides an overview on some scale out strategies, challenges with scaling out on-premise and how you can benefit with scaling out with SQL Azure.
![-400050-619125-990600-600075-990600-914400<br />Scaling out with SQL Azure<br />Author<br />Dinakar Nethi<br />Technical Reviewers<br />Cihan Biyikoglu, Michael Thomassy<br />PublishedJune 2010<br />SummarySQL Azure Database is a cloud database service from Microsoft. SQL Azure provides web-facing database functionality as a utility service. Cloud-based database solutions such as SQL Azure can provide many benefits, including rapid provisioning, cost-effective scalability, high availability, and reduced management overhead. This paper provides an overview on some scale out strategies, challenges with scaling out on-premise and how you can benefit with scaling out with SQL Azure.<br />Copyright<br />This is a preliminary document and may be changed substantially prior to final commercial release of the software described herein.<br />The information contained in this document represents the current view of Microsoft Corporation on the issues discussed as of the date of publication. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information presented after the date of publication.<br />This white paper is for informational purposes only. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED, OR STATUTORY, AS TO THE INFORMATION IN THIS DOCUMENT.<br />Complying with all applicable copyright laws is the responsibility of the user. Without limiting the rights under copyright, no part of this document may be reproduced, stored in, or introduced into a retrieval system, or transmitted in any form or by any means (electronic, mechanical, photocopying, recording, or otherwise), or for any purpose, without the express written permission of Microsoft Corporation. <br />Microsoft may have patents, patent applications, trademarks, copyrights, or other intellectual property rights covering subject matter in this document. Except as expressly provided in any written license agreement from Microsoft, the furnishing of this document does not give you any license to these patents, trademarks, copyrights, or other intellectual property.<br />© 2010 Microsoft Corporation. All rights reserved.<br />Microsoft, ADO.NET Data Services, Cloud Services, Live Services, .NET Services, SharePoint Services, SQL Azure, SQL Azure Database, SQL Server, SQL Server Express, Sync Framework, Visual Studio, Windows Live, and Windows Server are trademarks of the Microsoft group of companies.<br />All other trademarks are property of their respective owners.<br />Scaling out with SQL Azure<br />Partitioning is a technique of splitting up data into smaller subsets across multiple databases for better manageability, availability and scalability. As customers’ data needs grow, they have the option to either scale up or scale out. <br />The primary advantage of scaling out vs. scaling up is that you can get more work done by parallelizing the workload over distributed servers and improving throughput. Scaling up always has a ceiling as opposed to Scale out with virtually no limits. Depending on how the application is architected, scale out techniques require minimal downtime where you can provision servers while managing the availability of servers through a configuration file. While hot add memory and processor options exist in high end systems, typically they do end up taking downtime as a precaution. Scale out techniques are being successfully applied in several applications such as eBay, Flickr, YouTube, Facebook etc.<br />Common Partitioning Techniques<br />Partitioning can be achieved in several ways, the two major categories being Horizontal Partitioning and Vertical Partitioning. While the benefits of partitioning seem well understood, successfully implementing a partitioning technique and achieving the partitioning benefits depends on the nature of workload of businesses and a very carefully planned and tested partitioning key.<br />Vertical Partitioning: In this technique, data is split across tables with fewer columns through the process of Normalization with infrequently used columns in a separate table. A table is split into multiple smaller tables with fewer columns and can be linked back together by primary key and foreign key relationships. Another way of vertical partitioning is to put a single table/entity into its own database. For example, some tables may be so large that putting them to a different database makes sense. For instance, taking the entire customer table into another database is a form of vertical partitioning. Typically, rows from all tables using unique rowid(s) are re-constituted to form an entire row. For example, if a customer has multiple shipping addresses in his or her profile, the table can be partitioned by putting the primary address in one table and secondary addresses in another table. Most queries will directly be done against the primary address table and very few requiring the application to display all the addresses.<br />PrimaryAddressCustomerIdAddressIdCustomerCustomerIDSecondaryAddressesCustomerIdAddressId<br />Horizontal Partitioning: In this technique, data is distributed across tables by a key, similar to Database Partitioning in SQL Server 2005. Typically, there is a master table that maps the data to the partition. Depending on the query that the user submits, the application queries this master table to find out which partition has the required data and routes the query to the particular database. Typically, horizontal partitioning splits data within an instance of a database server. Sometimes this approach is taken further where these partitions are distributed across multiple instances. Each of these partitions can be in a separate database on a physically separate server. The database schema is similar across all partitions.<br />For example, if you consider a typical online store, all inventories belonging to a particular country can be put into one database. Similarly, sales, customer information belonging can be distributed based on the country, country being the partitioning key here. Another partitioning key in the above scenario could be an alphabetical list of countries. Further partitioning can also be done by using a state as a partitioning key. A combination of criteria can also be considered for a partitioning key, depending on the type of application and the nature of workload.<br />Typically, data is normalized for better performance. In horizontal partitioning, logical groups of data are stored together. There are some key considerations to achieve the benefits of horizontal database partitioning.<br />,[object Object]](https://image.slidesharecdn.com/microsoftsqlazure-scalingoutwithsqlazurewhitepaper-100816070939-phpapp02/85/microsoft-sql-azure-scaling-out-with-sql-azure-whitepaper-1-320.jpg)
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Microsoft SQL Azure - Scaling Out with SQL Azure Whitepaper
- 2. Partition management: After the partitioning keys have been identified, the next major challenge lies in designing the application layer to manage the partitions. The application needs to have a proper understanding of the partitions and their data distribution. As business grows and data capacity requirements grow, and new partitions are allocated, the application should be able to quickly and seamlessly be able to manage switching in and out partitions with minimal to no downtimes. Depending on how the partitioning keys are chosen, sometimes adding new partitions may involve reallocation of data which may require a downtime.
- 3. Avoid cross-database joins: Enforcing referential integrity and joining data across instances are inefficient in partitioning. Hence, the application should be designed in such a way that querying data across partitions is avoided. In cases where data from multiple partitions is required, the application should be smart enough to process the query asynchronously querying the data from each partition separately and aggregating the data in the application layer.
- 4. Database partitioning can be very rewarding for businesses with highly variable workloads. Businesses usually evaluate their peak usage patterns and add some room for growth when planning for their annual hardware budgets. This means a lot of unused hardware for highly elastic workloads. With database partitioning, the databases can be distributed over multiple commodity hardware which is usually cheaper than the high end multi-processor machines. As usages increases or decreases, new servers can be provisioned or removed without heavy infrastructure cost investments.
- 5. High Availability: With data partitioned across so many databases over multiple instances, having a high availability solution is almost a must.
- 7. Administration with SQL Azure: Scaling out in an on-premise scenario can create considerable management overhead for all the instances of SQL Server involved. SQL Azure abstracts the logical administration from physical administration. Microsoft handles all physical server level tasks such as server patching, hard drives, storage etc. while customers handle logical administration such as administering the databases, logins, users and optimizing the queries and indexes. DBAs only need to manage schema creation, index tuning, query optimization and security administration.
- 9. SQL Azure Developer Center
- 10. SQL Azure Team Blog