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Most problems for which this pattern is applicable fall into the following two categories Embarrassingly parallel problems are those in which there are no dependencies among the tasks A wide range of problems fall into this category, ranging from rendering frames in a motion picture to statistical sampling in computational physics Because there are no dependencies to manage, the focus is on scheduling the tasks to maximize efficiency In many cases, it is possible to define schedules that automatically and dynamically balance the load among UEs Replicated data or reduction problems are those in which dependencies can be managed by "separating them from the tasks" as described earlier replicating the data at the beginning of computation and combining results when the termination condition is met (usually "all tasks complete") For these problems, the overall solution consists of three phases, one to replicate the data into local variables, one to solve the now independent tasks (using the same techniques used for embarrassingly parallel problems), and one to recombine the results into a single result Examples We will consider two examples of this pattern The first example, an image construction example, is embarrassingly parallel The second example will build on the molecular dynamics example used in several of the Finding Concurrency patterns

The final clause in a Select is the optional LIMIT clauseThe function of this clause is to cap the number of results that will come back from a single call to SelectThe syntax for this clause calls for the keyword LIMIT followed by a number from 1 to 2500 Here is an example of LIMIT used in a query:

To illustrate the use of input streams and readers together, we'll examine how an input stream may be converted to a reader for easy character reading without using a DataInputStream Code for InputStreamToReaderDemo

In many image construction problems, each pixel in the image is independent of all the other pixels For example, consider the well known Mandelbrot set [Dou86] This famous image is constructed by coloring each pixel according to the behavior of the quadratic recurrence relation Equation 41

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import javaio*; // 4, Listing 3 public class InputStreamToReaderDemo { public static void main(String args[]) { try { Systemoutprint ("Please enter your name : "); // Get the input stream representing standard input InputStream input = Systemin; // Create an InputStreamReader InputStreamReader reader = new InputStreamReader ( input ); // Connect to a buffered reader, to use the // readLine() method BufferedReader bufReader = new BufferedReader ( reader ); String name = bufReaderreadLine(); Systemoutprintln ("Pleased to meet you, " + name); } catch (IOException ioe) { Systemerrprintln ("I/O error : " + ioe); } } }

Every Select call is subject to limit processing; if you do not include a limit clause, SimpleDB uses the default LIMIT value of 100As the search results list is built during query processing, if the size of the list reaches the limit value, processing stops and the results are returnedWhen this happens, SimpleDB returns a NextToken along with the search resultsThe NextToken is an encoded value that holds the state information necessary to allow processing to continue where it left off without the need for SimpleDB to maintain the state on the back-endThis allows SimpleDB, internally, to handle any query using any node that has a replica of the target domain From the client side, query processing can be resumed by making the same Select call and passing the NextToken The LIMIT clause is most obviously useful in those cases where you truly care about only a certain number of results For instance, when you want the name of the student with the highest test score:

How InputStreamToReaderDemo Works The code is fairly self-explanatory We start by connecting an InputStreamReader to standard input (represented by Systemin) We connect a BufferedReader, to maximize performance, and then read a single line of text Running InputStreamToReaderDemo Running this example is straightforward To run the application, type in your name at the prompt and hit enter To run the example, use:

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Another common use of the LIMIT clause is to display search results one page at a time Setting the LIMIT to the size of the page lets you fetch the exact number of results required for a single page In this case, storing the NextToken will enable you to fetch the next page more conveniently in response to a future request It is true that a query LIMIT is always in effect, whether default or specified, but there are three cases where the Select call returns early without reaching the LIMITThe first case is when the number of items matching the query is smaller than the LIMIT valueYou will know this has occurred when the search response does not contain a NextTokenThe lack of a NextToken always signals that the full set of matching items has been returned The other two cases where Select returns before the LIMIT is reached involve other service limits that take precedenceTo maintain quality of service, SimpleDB enforces a per-request restriction on the size of the response it is willing to send, and on the length of time it is willing to spend If the response size grows to 1MB or takes 5 seconds, the call will return immediately with the results thus far, if any, and a NextToken Consequently, when you use a LIMIT clause to retrieve a specific number of items, you must check for the presence of a NextToken and the number of items in the response to determine if you need to issue another call to fetch the outstanding results

where registry_hostname is the hostname or IP address of a rmiregistry, registry_port is an optional port field for nonstandard registry locations, and servicename is the name of an RMI service The default port for an rmiregistry 1099 Methods All methods provided by the Naming class have public access modifiers The Naming class defines the following methods:

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