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A key aspect of a queue is how it manages its messages and their visibility. This is how the queue implements the message durability developers are looking for. The goal is to protect against a consumer getting a message, and then failing to process and delete that message. If that happened, the message would be lost, and this isn t good news for any processing system. Visibility timeouts and idempotency are the two best tools for making sure your messages are never lost. Understanding how these concepts relate to the queue and understanding the lifecycle of a message are important to the success of your code.

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methods (continued) create methods 472 CreateDirectory 131 createObjectEx 727 delete methods 473 474 DeleteDirectory 130 DeleteFile 130 Deserialize 364 domain services 472 476 DragMove 124 DragResize 125 EndGetResponse 354 Environment.GetFolderPath 113 Execute 439 FindName 146, 653 Focus 189, 236 GetDefaultVideoCaptureDevice 609 GetDiagnosticAsync 596 GetDirectoryNames 129 GetElementById 77 GetElementsByTagName 77 GetErrors 321 GetFileNames 130 GetIsNetworkAvailable 106 GetLayoutClip 157 GetLayoutSlot 157 GetLeft 173 GetOriginal 493 GetPosition 194 GetPropertyValue 231 GetResourceStream 680 GetResponseStream 354 GetSampleAsync 597, 601 GetStyleAttribute 78 GetStylusPoints 200 GetTemplateChild 714 GetTop 174 GetUserStoreForApplication 129 GetUserStoreForSite 133 GetValue 146, 703 GoBack 394 GoForward 394 GoToState 695 ignoring 476 IncreaseQuotaTo 132 insert methods 472, 474, 502 Install 105 Invoke 85 invoke methods 473, 475, 491 InvokeScript 91 Load 43, 365 MapUri 397

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} else { lEquals = lEquals && this.type.equals( that.type ); } return lEquals; } else { return false; } } public int hashCode(){ int result = 17; result = 37*result + ((this.iD != null) this.iD.hashCode() : 0); result = 37*result + ((this.type != null) this.type.hashCode() : 0); return result; } }

Web applications followed many of the attributes of loose coupling. When we moved toward machine-to-machine communication over the web, we retained the loose coupling and created the concept of SOA. Here, a remote service publishes its interface (via a WSDL), and a consuming service has to abide by that interface to consume the service. SOA was an important evolutionary step to get to the cloud. Let s look much more closely at how SOA works.

@Column(name="ZIP_CODE", nullable=false) protected String zipCode; @Column(name="COUNTRY", nullable=false) protected String country; }

on each, you ll be staring at two tables of information, and your job is to figure out if there are any differences between the two. Because these are processes that you re looking at, you re always going to see differences in things like CPU and memory utilization numbers, so we ll ignore those columns. In fact, just focus on the Name column, because we really want to see if the difference computer contains any additional, or any fewer, processes than the reference computer. It might take you a while to compare all the process names from both tables, but you don t have to that s exactly what Diff will do for you. Let s say you sit down at the reference computer and run this:

Databases on the system be found by running the following:

A SELECT statement generated by the find method to retrieve Item entities would not load data from the ITEMS.PICTURE column into the picture field. Instead, the picture data will be automatically loaded from the database when the property is first accessed through the getPicture method. Be advised, however, that lazy fetching is a double-edged sword. Specifying that a column be lazily fetched means that the EntityManager will issue an additional SELECT statement just to retrieve the picture data when the lazily loaded field is first accessed. In the extreme case, imagine what would happen if all entity data in an application is lazily loaded. This would mean that the database would be flooded with a large number of frivolous SELECT statements as entity data is accessed. Also, lazy fetching is an optional EJB 3 feature, which means not every

server under extreme load. Because the web server is under extreme load, it attempts to service all requests at once until it s so loaded that it can t effectively service any requests. What you need to do now is scale out your application.

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