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The following are some tuning points for the reduce phase: Shuffle/sort time: The shuffle and sort cannot complete until all of the map output data is available. If this is an issue, you can try the following: Use a combiner class. Increase the number of tasktracker.http.threads. Increase the ipc.server.listen.queue.size. Set mapred.compress.map.output to true. Vary the compression codec stored in mapred.map.output.compression.codec. Experiment with io.seqfile.compression.type as RECORD or BLOCK. Change your algorithm so that less data needs to pass to the reduce phase. Try more reduce tasks, to reduce the volume of data that each reduce phase must sort. Network saturation: The pull of the map outputs should just saturate your network. If the reduce tasks are timing out while trying to fetch outputs, increase the tasktracker.http.threads. If the network is saturated, enable compression, reduce the number of map tasks, improve the combiner class, or restructure the job to reduce the data passed to the reduce phase.

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Note I once had a job where part of the value associated with each key was a large block of XML data that

Closes the session, and, hence, the underlying database connection; releases other resources (such as the cache). This is not essential if you have called disconnect, but it is usually a good idea. You must not perform operations on the session after calling close as they will generate exceptions. Disconnects the underlying database connection, but maintains the cached Session objects. Reconnects the underlying database connection. Determines if the underlying database connection is connected.

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Function that creates a path for a tree canopy function createCanopyPath(context) { // Draw the tree canopy contextbeginPath(); contextmoveTo(-25, -50); contextlineTo(-10, -80); contextlineTo(-20, -80); contextlineTo(-5, -110); contextlineTo(-15, -110); // Top of the tree contextlineTo(0, -140); contextlineTo(15, -110); contextlineTo(5, -110); contextlineTo(20, -80); contextlineTo(10, -80); contextlineTo(25, -50); // Close the path back to its start point contextclosePath(); } As you can see from the code, we used the same move and line commands from before, but more of them These lines form the branches of a simple tree shape, and we close the path back at the end Our.

was unused by the reduce phase. Modifying the map to drop the XML data provided a tenfold improvement.

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Actual reduce time: You may find that the time to actually reduce the data, after the shuffle and sort are done, is too long. If you are using MultipleOutputFormat, ensure that the number of files being created is small. If many small files must be created, write them as a zip archive. The Ganglia gmetric value FilesCreated will give you an idea of the rate of HDFS file creation. Write time: The write time may be too long if the volume of data or the number of files are large. Enable output compression via setting mapred.output.compress to true. Experiment with codecs. Pack multiple files into zip files or other archive formats.

tree will leave a notable gap at the bottom, and we will use this in future sections to draw the trunk. Listing 2-9 shows how to use that canopy drawing function to actually render our simple tree shape onto a canvas. Listing 2-9. Function that draws a tree on the canvas function drawTrails() { var canvas = document.getElementById('trails'); var context = canvas.getContext('2d'); context.save(); context.translate(130, 250); // Create the shape for our canopy path createCanopyPath(context); // Stroke the current path context.stroke(); context.restore(); } All the calls in this routine should be familiar to you already. We fetch the canvas context, save it for future reference, translate our position to a new location, draw the canopy, stroke it onto the canvas, and then restore our state. Figure 2-6 shows the results of our handiwork, a simply line representation of a tree canopy. We ll expand on this as we go forward, but it s a good first step.

Transactions and locking are intimately related the locking techniques chosen to enforce a transaction can determine both the performance and likelihood of the success of a transaction. The type of transaction selected dictates, to some extent, the type of locking that it must use. You are not obliged to use transactions if they do not suit your needs. Though, you will need to invoke the flush() method on the session to ensure that your changes are persisted to the database if you choose not to use them.

Note I had one job that needed to create many tens of thousands of small files. Writing the files as a zip

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