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Many input streams are provided by the javaio package, and choosing the right low-level stream is a fairly straightforward task, since the name of the stream matches the data source it will read from As shown in Table 4-1, there are six low-level streams to choose from, each of which performs an entirely different task There are other streams that programmers cannot create directly but that are nonetheless low-level streams A good example of such a stream is one that represents a network connection to a TCP socket You can create the socket, but not the input stream to read it These types of streams will be discussed where appropriate throughout the text Such streams are not directly instantiated by developers, and are instead returned by invoking a method of a networking object Table 4-1 Low-Level Input Streams of the javaio Package Low-Level Input Stream Purpose of Stream ByteArrayInputStream Reads bytes of data from an in-memory array FileInputStream Reads bytes of data from a file on the local file system PipedInputStream Reads bytes of data from a thread pipe StringBufferInputStream Reads bytes of data from a string SequenceInputStream Reads bytes of data from two or more low-level streams, switching from one stream to the next when the end of the

Notice that this query is only requesting three specific attributesThis query matches up with the data stored in the BatchPutAttributes code sampleWith this query, only the items with a rating greater than 85 are requested and a limit of two items is specified Because only three of the four items meet these criteria, the first select will contain two items, and a NextToken and a second call, which includes that token, will contain the final item A loop is used when calling Select() to handle an unknown number of pages During each loop, results are printed, and the NextToken is attached to the existing request The existing request is reused since it is still configured with the same SelectExpressionWhen no token is returned in the response, the loop falls through Also, notice that the SelectExpression value calls for results to be sorted by rating, from high to lowWhen you run this sample code, look to see that all the ratings are greater than 85 and that they are sorted

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A task decomposition that identifies tasks that can execute concurrently A data decomposition that identifies data local to each task A way of grouping tasks and ordering the groups to satisfy temporal constraints An analysis of dependencies among tasks

As in the lightbulb server, the client application must be careful to catch any exceptions thrown at runtime, if the server or rmiregistry is unavailable It also needs to create a URL to the registry and service name, which by default will point to the local machine, but can be overridden by a command-line parameter The code for this is similar to that in the server, and for this reason is not repeated here Once a URL to the registry entry has been created, the application attempts to look up the location of the service and obtain an object reference to it, by using the Naminglookup(String) method An explicit cast is made to the RMILightBulb interface; if no such interface is found, a NotBoundException will be thrown and caught

// Lookup the service in the registry, and obtain a remote // service Remote remoteService = Naminglookup ( registration ); // Cast to a RMILightBulb interface RMILightBulb bulbService = (RMILightBulb) remoteService;

When you use SimpleDB, you give up some features you might otherwise have, but as a trade-off, you gain some important benefits, as follows:

It is these four items that will guide the designer's work in the next design space (the Algorithm Structure patterns) Therefore, getting these items right and finding the best problem decomposition is important for producing a high quality design In some cases, the concurrency is straightforward and there is clearly a single best way to decompose a problem More often, however, multiple decompositions are possible Hence, it is important before proceeding too far into the design process to evaluate the emerging design and make sure it meets the application's needs Remember that algorithm design is an inherently iterative process, and designers should not expect to produce an optimum design on the first pass through the Finding Concurrency patterns Forces The design needs to be evaluated from three perspectives

Here the networking code ends and the service code begins Three methods were defined in the service interface:

Suitability for the target platform Issues such as number of processors and how data structures are shared will influence the efficiency of any design, but the more the design depends on the target architecture, the less flexible it will be Design quality Simplicity, flexibility, and efficiency are all desirable but possibly conflicting attributes Preparation for the next phase of the design Are the tasks and dependencies regular or irregular (that is, are they similar in size, or do they vary) Is the interaction between tasks synchronous or asynchronous (that is, do the interactions occur at regular intervals or highly variable or even random times) Are the tasks aggregated in an effective way Understanding these issues will help choose an appropriate solution from the patterns in the Algorithm Structure design space

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