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characteristicsThere is more initial request overhead when using BatchPutAttributes as compared to PutAttributesAlthough you can use this operation to store anywhere from 1 to 25 items at a time, it is really only worth it when the number is closer to 25 than to 1 If you only have a few items to store, individual PutAttributes calls are likely to be faster and more efficient There is no rule set in stone dictating the minimum number of items needed to make BatchPutAttributes worth the overhead In fact, the important factors for deciding whether to batch up your uts have nothing to do with performance One factor is the way SimpleDB handles errorsAll of the items in a batch will be rejected or accepted as a unit Items in the batch will never be partially applied If your application needs to take individual actions based on the results of each item in the batch (for example, generating responses for application users), individual errors can be disruptive because just one of them causes the entire batch to failYou can avoid most of the errors that can occur with BatchPutAttributes by sending a request that respects the operation limits However, you may have no way of knowing ahead of time that one of the items in the batch is already at the 256-attributes limit and the whole operation will failThis makes BatchPutAttributes potentially cumbersome for updating existing items If you only have a few items at a time to store, if you are updating existing items or if you need to minimize latency, my recommendation is to use PutAttributes If you have a large quantity of new data to store and total throughput is more important than individual request latency, BatchPutAttributes is a better option.

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For maximum portability, you may think it is wise to take the thin-client approach when designing your application After all, this allows you not only to reach out to clients with limited resources (ie, memory and CPU) but also to avoid developing a Java client (which may be slow to download) or designing some other kind of custom client Even better, putting all of the application logic on the server side can give you the most flexibility as far as extending your application architecture to wireless or very low bandwidth deployments However, embracing the thin-client approach too tightly has important implications First of all, think about what you're sacrificing You have, say, 100,000 clients that access your Web application and do so with fully packed, powerful desktop computers Having them interact with your application through a very thin interface wastes the opportunity to really do some distributed processing that is, to put some of the computational work on the client side Instead, clients sit idle, just passing network packets back and forth Of more concern is that all of the computational burden is on the server side If the computational requirements for your application are high, accommodating sudden bunches of concurrent clients can lead to serious CPU contention There may be a compromise here, one that at least leans less heavily on the server Consider the common application need for client data validation Things like phone numbers and e-mail addresses need to be matched against a template to see if they conform to the right general "picture" Or numeric fields need to be checked to see that they contain numeric data, that their lengths are respected, and so on For these tasks, it's not unreasonable to use something like JavaScript or to put some thought into the HTML you're generating so the client does the work Admittedly, this may be a small amount of computation to push onto the client, but for sites with many forms or many clients the cycles saved can add up Never underestimate the leverage of 100,000 remote machines doing your processing for you Also keep in mind that things like clientside data validation not only save server-side CPU; they also cause fewer request/reply HTTP dialogues between client and server, thus allowing the Web server or request processor to handle the rest of its workload Still, there are problems with putting things even as minor as data validation on the client side One is that you might be replicating business logic For example, if you use the same application architecture to process batch transactions other than through your interactive Web site, all of a sudden you'll need to code the data validation logic in two places (batch transactions will ever execute your JavaScript data validation logic) Now what There's no one answer that fits all situations It depends on your application, your client base, and your mode of client interaction The main message here is not to blindly design your application with the "thinnest of clients" mindset, but to think carefully about the following issues when deciding how much (if any) business logic to put on the client side:.

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Performance is measured by the number of data blocks hat need to be sent to the redundant nodes for redundant data update or simply called redundant data update overhead The total number of data blocks is 40,000 and is xed throughout the simulation For simplicity the redundant data update overhead for updating one redundant node is presented We will return to the issue of updating multiple redundant data nodes in Section 155 In the rst experiment, we investigate the redundant data update overhead in continuous system growth We begin with a system of ve data nodes and one redundant node Then we add a new node to the system one by one, each time the redundant data blocks are completely updated using the SRDU algorithm This continues until the system grows to 400 data nodes Figure 15.

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