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Create services. Create queues. Create contracts. Create conversations. Create message types. Send messages to a service. Route a message to a service. Receive messages from a service.
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Lessons in this chapter:
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Lesson 1: Exploring the Service Broker Architecture . . . . . . . . . . . . . . . . . . . . 778 Lesson 2: Creating Message Types and Contracts . . . . . . . . . . . . . . . . . . . . . . . 784 Lesson 3: Creating Queues and Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 790 Lesson 4: Creating Conversations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 798 Lesson 5: Sending and Receiving Messages . . . . . . . . . . . . . . . . . . . . . . . . . . . . 803
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To complete the lessons in this chapter, you must have
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SQL Server 2005 installed. A copy of the AdventureWorks sample database installed in the instance.
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Michael Hotek Recently, I worked on one of the most amazing SQL Server systems that I have ever seen. There was nothing really unique about the technical implementation the table structures, the code within the application, or how transactions were processed. What was unique was the sheer scale of processing that was occurring. This database application was literally the largest in existence on a SQL Server platform and possibly on any database platform. Some people might want to argue this point. But when you use numbers with 11 zeros to start measuring the
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database transactions you are processing daily, you might begin to be in the ballpark of this application. Performance wasn t the problem with this application. The company was expanding its environment into additional markets, with the first market targeting an expected growth of almost double the database application s current load. All users had to have access to all data as well. So the company needed a mechanism to cache data close to the users while also maintaining a centralized database with at least half of the initial writes coming from a few thousand miles away. The organization could not use distributed data techniques such as log shipping because the databases were never offline, and developers would have to write a significant amount of code to extract just the incremental changes. Although replication can move incremental changes at a rapid enough rate to handle virtually any load, it simply did not have the capacity to move the volume of data necessary. Because the data would be cached locally, and users would generally find everything they needed in the local cache, it was not critical to have writes committed to the central database in a synchronous manner. So the developers began architecting a reasonably straightforward mechanism to queue changes locally and then send them back to the corporate data center in an asynchronous manner. However, they then realized other issues they had to overcome: issues of durability, backups, synchronization, ensuring that a change is sent only once, and hundreds of other issues that any queue with multiple readers would have. This scenario was a perfect fit for Service Broker. Although the volume of changes was staggering, the developers could create as many queues and brokers as they needed. Multiple brokers could then read from a single queue, and the volume of changes could be spread across multiple queues. The Service Broker architecture would guarantee that a change was sent once and only once while providing all the other infrastructure elements necessary to manage the changes. SQL Server would provide the means to ensure that the changes were durable and could survive even the complete loss of the queue due to disaster. With Service Broker providing all the architecture they needed, not only could the developers avoid months of architecting, coding, and testing, but the company could also take advantage of having a robust, distributed, high-performance message queue at no extra cost because it is included with SQL Server 2005. I can t wait to see this Service Broker application go into production processing several billion messages per day.
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