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Solution The key idea of this pattern is captured by the assembly line analogy, namely that the potential concurrency can be exploited by assigning each operation (stage of the pipeline) to a different worker and having them work simultaneously, with the data elements passing from one worker to the next as operations are completed In parallel programming terms, the idea is to assign each task (stage of the pipeline) to a UE and provide a mechanism whereby each stage of the pipeline can send data elements to the next stage This strategy is probably the most straightforward way to deal with this type of ordering constraints It allows the application to take advantage of special purpose hardware by appropriate mapping of pipeline stages to PEs and provides a reasonable mechanism for handling errors, described later It also is likely to yield a modular design that can later be extended or modified Before going further, it may help to illustrate how the pipeline is supposed to operate Let Ci represent a multistep computation on data element i Ci(j) is the jth step of the computation The idea is to map computation steps to pipeline stages so that each stage of the pipeline computes one step Initially, the first stage of the pipeline performs C1(1) After that completes, the second stage of the pipeline receives the first data item and computes C1(2) while the first stage computes the first step of the second item, C2(1) Next, the third stage computes C1(3), while the second stage computes C2(2) and the first stage C3(1) Fig 424 illustrates how this works for a pipeline consisting of four stages Notice that concurrency is initially limited and some resources remain idle until all the stages are occupied with useful work This is referred to as filling the pipeline At the end of the computation (draining the pipeline), again there is limited concurrency and idle resources as the final item works its way through the pipeline We want the time spent filling or draining the pipeline to be small compared to the total time of the computation This will be the case if the number of stages is small compared to the number of items to be processed Notice also that overall throughput/efficiency is maximized if the time taken to process a data element is roughly the same for each stage.

NOTE As mentioned in earlier chapters, you can use localhost as the hostname to refer to the local machine

This idea can be extended to include situations more general than a completely linear pipeline For example, Fig 425 illustrates two pipelines, each with four stages In the second pipeline, the third stage consists of two operations that can be performed concurrently

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When you need to access data that is many pages deep into a result set, the round-trip costs can really kill performanceA shortcut to quickly accessing these pages relies on the way NextToken worksA NextToken combines the SimpleDB internal search state with the WHERE and sort clauses Each NextToken can be used to resume the query by passing it back along with the original query string However, as long as the WHERE clause and the sort attribute do not change (and obviously the domain), you are free to change the return attributes and the LIMIT The technique for skipping ahead involves setting the LIMIT to the number immediately preceding the first value you need If you want to start at result number 1000, you

The previous example illustrates the technical details of how an individual packet may be sent and received But applications need a series of packets, not just one The next example shows how to build a UDP server, a long-running system that is capable of serving many requests during its lifetime The type of service that is provided is an echo service, which echoes back the contents of

a packet The echo service runs on a well-known port, port 7, and if it is known that a system has an echo server installed, the server may be accessed by clients to see if a system is up and running (similar to the ping application) The example below demonstrates how to write an echo client that will send packets to the server as well as read the results back NOTE Some systems have an echo service already running in the background, and security restrictions may prevent a service from binding to a well-known port If this is the case, you will need to change the port number to a different number (and in the case of security restrictions, you should select a number above port 1024) in both the client and the server The same port number must be used for communication

Normally each pipeline stage will correspond to one task Fig 426 shows the basic structure of each stage If the number of data elements to be processed is known in advance, then each stage can count the number of elements and terminate when these have been processed Alternatively, a sentinel indicating termination may be sent through the pipeline It is worthwhile to consider at this point some factors that affect performance

initially set the LIMIT to 999 Instead of including the actual attributes you want, you set the output listing to count(*)This has the effect of reducing both the query time and the response size Once the query returns with the desired count, you resubmit it with the actual attribute list and the desired LIMIT value Even though count(*) is faster, there is no guarantee that it will reach your target LIMIT on the first request In this situation, the response will contain both a count value and a NextTokenThe count value is the actual number of items counted in the current NextTokenTo make this tactic work, you will need to call your query in a loop and maintain a total count so far until you reach the desired LIMITAlthough you very well may reach the correct LIMIT on the first try, you have to be prepared for the cases where you do notAt each page along the way, reevaluate the temporary LIMIT and adjust as necessaryTo expand on the last example, if you need the actual query to start at result number 1000, your temporary LIMIT is 999 If the first response with count(*) comes back with a count value of 501, you will need to submit another request with count(*) and an updated LIMIT of 498 Tip

The following example involves building an echo service, which transmits any packet it receives straight back to the sender The code uses no new networking classes or methods, but employs a special technique It loops continuously to serve one client after another Though only one UDP packet will be processed at a time, the delay between receiving a packet and dispatching it again is negligible, resulting in the illusion of concurrent processing

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