Figure 4-1 Caching to reduce latencies caused by distance in Java

Generator QR Code in Java Figure 4-1 Caching to reduce latencies caused by distance
Figure 4-1 Caching to reduce latencies caused by distance
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There's another compelling reason for caching: better access time If the access time of a cache is quicker than that for the same information on the originating source, the cache may be more efficient, even if it's more remote You see this all the time on the Web Suppose there's a great Web site hosted in your city but its server is slow or the network infrastructure connecting it to an Internet backbone is lightweight A content distributor like Akamai or a publicly available cache like Google may have a copy of that information that, although remotely located (perhaps it's even more remote than the original source), is still quicker to access because the servers are snappier and the bandwidth for clients is higher Then there's what I call the cut-to-the-chase aspect of querying that makes a cache quicker to access Suppose I want to find out the vice president of an employee's division I have the employee ID, but to get the VP information I need to query an EMPLOYEE table to find the department ID for that employee, query a DEPARTMENT table to find the division ID for that department, and finally query a DIVISION table to find the division information I want In contrast, a cache can simply map employee IDs to department VP information, as shown in Figure 4-2 The effect is the same: Caches can provide faster access than the original source Figure 4-2 Caching to reduce access time
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In summary, there's a tradeoff in the caching benefit equation, which can be shown as
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Thus, response time is a function of cache proximity as well as cache efficiency, both relative to the originating source As the equation indicates, that's just the performance benefit Caching can also provide scalability benefits because it reduces demand for server resources in the forms of less bandwidth, fewer CPUs, and fewer connections to manage By copying information to a more local or more efficient source, we delegate the rising resource demands in an effort to provide consistent performance Generally speaking, then, caching improves both performance and scalability Caching is not without its problems The most obvious among them is guaranteeing data consistency: ensuring that the cache contains the same information as the server contains and that multiple caches are kept in sync For example, if your application maintains a main memory cache of session data (such as user information) normally stored in a database, it's important that any changes to the data be reflected in both It's even worse if you have multiple caches and/or multiple databases; then you need to make sure that the data is consistent across all sources Clearly, this can get tricky Suppose you have a banking application that keeps track of checking and savings accounts For improved performance, the application caches account data in memory as accounts are accessed So, while Joe Smith (an account holder) is logged in, his checking balance of $100 and savings balance of $0 are kept in a memory cache
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Let's say that this is a write-through cache, meaning that it forwards all updates to the database as they're made Now, what will happen if at the same time that Joe is online the bank processes a check he's written for $50 Check processing has nothing do with the online application (or so it would seem), so some sort of check-processing software updates the database with Joe's new balance Of course, if Joe uses his online session to transfer money from checking to savings, there can be a serious problem because the cache is unaware of the changes made directly to the database This isn't as unlikely as you might think Valuable data often has many interfaces and problems like this can occur unless these interfaces share a process for interacting with the database The extent of the damage in this example may depend on the write-through nature of the cache Are two SQL UPDATE statements taking place, blindly ignoring the current database values and trusting the cache alone If not, where's the logic that ensures that Joe won't have a negative balance In any case, either Joe will get an error or the bank will give him some money back! Obviously, neither of these is a satisfactory solution The consistency problem gets even worse as we try to make our application more scalable Say an organization provides scalable access to its information by deploying its application across several servers and uses load balancing to distribute the load among Web servers and application hosts (Figure 4-3 shows this deployment) Now, suppose that the application uses a memory cache to retain account information; however, as Figure 4-4 shows, since the application is distributed there are multiple caches Figure 4-3 Load balancing for scalability
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