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The amount of concurrency in a full pipeline is limited by the number of stages Thus, a larger number of stages allows more concurrency However, the data sequence must be transferred between the stages, introducing overhead to the calculation Thus, w e need to organize the computation into stages such that the work done by a stage is large compared to the communication overhead What is "large enough" is highly dependent on the particular architecture Specialized hardware (such as vector processors) allows very fine grained parallelism The pattern works better if the operations performed by the various stages of the pipeline are all about equally computationally intensive If the stages in the pipeline vary widely in computational effort, the slowest stage creates a bottleneck for the aggregate throughput

import javanet*; import javaio*; // 5, Listing 3 public class EchoServer { // UDP port to which service is bound public static final int SERVICE_PORT = 7; // Max size of packet, large enough for almost any client public static final int BUFSIZE = 4096; // Socket used for reading and writing UDP packets private DatagramSocket socket; public EchoServer() { try { // Bind to the specified UDP port, to listen // for incoming data packets socket = new DatagramSocket( SERVICE_PORT ); Systemoutprintln ("Server active on port " + socketgetLocalPort() ); } catch (Exception e) { Systemerrprintln ("Unable to bind port"); } } public void serviceClients() { // Create a buffer large enough for incoming packets byte[] buffer = new byte[BUFSIZE];

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The maximum LIMIT you can specify within a normal Select expression is 2,500, but this limit does not apply when you use count(*) You can specify a LIMIT value as high as you need but the actual count value returned will depend on the complexity of the query and the data in your domain Even with simple queries on a large domain it is unusual to get back a count greater than 200,000, but sometimes you may get back zero Remember to use a loop and update the total count so far and compute a new LIMIT value during each iteration

Figure 426 Basic structure of a pipeline stage initialize while (more data) { receive data element from previous stage perform operation on data element send data element to next stage } finalize

for (;;) { try { // Create a DatagramPacket for reading // UDP packets DatagramPacket packet = new DatagramPacket ( buffer, BUFSIZE ); // Receive incoming packets socketreceive(packet); Systemoutprintln ("Packet received from " + packetgetAddress() + ":" + packetgetPort() + " of length " + packetgetLength() ); // Echo the packet back - address and port // are already set for us ! socketsend(packet); } catch (IOException ioe) { Systemerrprintln ("Error : " + ioe); } } } public static void main(String args[]) { EchoServer server = new EchoServer(); serverserviceClients(); } }

public void setNextToken(String nextToken) { PARAMSput("NextToken", encode(nextToken)); } public void setConsistentRead() { PARAMSput("ConsistentRead", "true"); } public void setItemName(String name) { PARAMSput("ItemName", encode(name)); } public void setExpectedAttribute(String name, String value) { PARAMSput("Expected1Name", encode(name)); PARAMSput("Expected1Value", encode(value)); } public void setExpectedToNotExist(String name) { PARAMSput("Expected1Name", encode(name)); PARAMSput("Expected1Exists", "false"); } public void addAttributeNames(String atts) { for (int i = 0; i < attslength; i++) { PARAMSput("AttributeName" + i, encode(atts[i])); } } public void addAttributes(Attribute atts) { addPrefixAttributes("", atts); } private void addPrefixAttributes(String prefix, Attribute atts){ for (int i = 0; i < attslength; i++) { String finalPrefix = prefix + "Attribute" + i + ""; PARAMSput(finalPrefix + "Value", encode(atts[i]getValue())); PARAMSput(finalPrefix + "Name", encode(atts[i]getName())); if (atts[i]isReplace()) { PARAMSput(finalPrefix + "Replace", "true"); } } } public void addItems(List<Item> items) {

The pattern works better if the time required to fill and drain the pipeline is small compared to the overall running time This time is influenced by the number of stages (more stages means more fill/drain time)

The following client can be used with the echo service and can easily be adapted to support other services Repeated packets are sent to the echo service, and a timeout is caught to prevent the service from stalling if a packet becomes lost, and the client then waits to receive it Remember that packet loss in an intranet environment is unlikely, but with slow network connections on the Internet it is quite possible

Therefore, it is worthwhile to consider whether the original decomposition into tasks should be revisited at this point, possibly combining lightly loaded adjacent pipeline stages into a single stage, or decomposing a heavily loaded stage into multiple stages It may also be worthwhile to parallelize a heavily loaded stage using one of the other Algorithm Structure patterns For example, if the pipeline is processing a sequence of images, it is often the case that each stage can be parallelized using the Task Parallelism pattern

import javanet*; import javaio*; // 5, Listing 4 public class EchoClient { // UDP port to which service is bound public static final int SERVICE_PORT = 7; // Max size of packet public static final int BUFSIZE = 256; public static void main(String args[]) { if (argslength != 1) {

for (int i = 0; i < itemssize(); i++) { String prefix = "Item" + i + ""; PARAMSput(prefix + "ItemName", encode(itemsget(i)getName())); addPrefixAttributes(prefix, itemsget(i)getAtts()); } }

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