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Interestingly, solutions to some request processing problems create new problems Such is the case with the kind of caching we've been discussing To understand how, consider load balancing, one popular solution for connection management in Web servers today, and its effect on data locality The basic idea of load balancing is to spread the work around in terms of incoming requests As requests arrive, they're farmed out to handlers (servers) using some distribution policy (such as round-robin) This helps reduce the load on any one server, but it creates a new problem if the application maintains state or establishes local caches Specifically, by effectively spreading the application over multiple machines, load balancing suddenly makes cache consistency an issue for all mutable data It also creates a new challenge: How can cached information be shared between request processors That is, how can the caching by one server be leveraged by another To illustrate, let's look at a farm of Web or application servers managed by a collection of loadbalancing hardware and software for a human resources application With this application, administrators can change employee addresses, phone numbers, titles, and the like

So, to recap, unless you can guarantee that all objects have been constructed within your component on the heap, and are not derivable,

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Not long ago, I received a call from a respected, but frustrated journalist from USA Today He was writing a story on a company that had experienced a high profile loss in a segment of their business, yet the financial statement disclosures were silent on this piece of business The company claimed the lack of disclosure was acceptable given the overall profitability of their business and lack of materiality for this segment This lack of transparency can, indeed, be frustrating to investors who might view this segment as the emerging driver of future growth for the company While the SEC requires certain segment disclosure, companies try to keep this type of disclosure to a minimum for competitive reasons Shareholder-friendly companies, however, will do all that is reasonable to disclose important information so that investors can make informed decisions In this case, the visibility of the loss-making business makes a case for disclosure While the company may have been in compliance with existing disclosure rules, they were not complying with its spirit All executives and directors should be polled to determine if they know of any information that should be considered for disclosure in the financial statements Human resources might know of a possible diversity suit that the general counsel has not yet been made aware, the COO might know of a possible worker grievance gaining steam, or the CIO may have just learned that important technology utilized by the entity will not be supported next year Accumulating this data is more important than identification of the source Such communications should be encouraged rather than punished and, ideally, a committee of knowledgeable executives and board members convened to evaluate them and determine which should be disclosed A thoughtful process where management is properly informed and uses business judgment will go along way to demonstrating reasonable assurance.

To make the application faster, we can design it so that employee data is cached on the local machine that services a given request For example, if a client calls up information on "Jane Hill," the application caches all of that employee's information so that future requests for it will be faster When the user modifies some part of that data (say, Jane Hill's address), the cache is updated and so is the database Now consider how that method of write-through caching affects our server farm Unless we change the architecture, replicating our application across multiple machines will cause multiple independent caches to be created And now, suddenly, we're forced to make sure that these independent caches are consistent Related to this is the new challenge I referred to: sharing cached data Generally speaking, we're interested in how work done by one server (to look up the data) can be leveraged by the other servers Of course, we don't have to share data between caches if it's read-only, nothing forces us to do this However, consider the inefficiency involved: We're effectively wasting our aggregate memory by replicating data Also, we're looking up data repeatedly, even though it has already been looked up once Dealing with this challenge as well as the consistency issue makes things increasingly complex We'll certainly have to do a lot of thinking during the design stages to make the system both correct and efficient And, if we don't design our system correctly, the problem of managing distributed cached data will turn out to be bigger than that of dealing with the normal disk latencies associated with retrieving this data in the first place

2 Class CBase also declares, but does not de ne, a private copy constructor (and corresponding assignment operator) to prevent the compiler generating the implicit versions that perform potentially unsafe shallow copies of member data Neither the copy constructor nor assignment operator are implemented, in order to prevent copy operations of CBase classes although of course you can implement your own, public, versions for derived classes where this is valid

It's tempting to think of request processing as something only a Web server does In fact, there are infinite request-processing scenarios any client can, conceivably, make a request of any other server or subsystem For all of these scenarios, there are in fact only two modes of request processing: synchronous and asynchronous We now turn our attention to understanding the pros and cons of both

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