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packageJobJar: [/tmp/wordCount.pl, /tmp/hadoop-0.19.0-jason/hadoop-unjar57851/] [] /tmp/streamjob57852.jar tmpDir=null 09/03/15 15:20:40 INFO mapred.FileInputFormat: Total input paths to process : 1 09/03/15 15:20:40 INFO streaming.StreamJob: getLocalDirs(): [/tmp/hadoop-0.19.0-jason/mapred/local] 09/03/15 15:20:40 INFO streaming.StreamJob: Running job: job_200902221346_0139 09/03/15 15:20:40 INFO streaming.StreamJob: To kill this job, run: 09/03/15 15:20:40 INFO streaming.StreamJob: /home/jason/src/hadoop-0.19.0/bin/../bin/hadoop job -Dmapred.job.tracker=cloud9:8021 -kill job_200902221346_0139 09/03/15 15:20:40 INFO streaming.StreamJob: Tracking URL: http://192.168.1.2:50030/jobdetails.jsp jobid=job_200902221346_0139 09/03/15 15:20:41 INFO streaming.StreamJob: map 0% reduce 0% 09/03/15 15:20:53 INFO streaming.StreamJob: map 50% reduce 0% 09/03/15 15:20:55 INFO streaming.StreamJob: map 100% reduce 0% 09/03/15 15:21:13 INFO streaming.StreamJob: map 100% reduce 100% 09/03/15 15:21:14 INFO streaming.StreamJob: Job complete: job_200902221346_0139 09/03/15 15:21:14 INFO streaming.StreamJob: Output: next

// retrieve the data consolelog(elementget('items')); // '[1,2,3]' Here we tried storing an array in our element object using an attribute Because attributes need to be strings, our array was first cast to a string value before it was attached to our element When we retrieve the data again using get, we get back a string and not the original array Because associating data with elements is a common operation, MooTools provides a special feature called element storage that enables us to attach data directly to an element We can do this using two element methods, store and retrieve var element = $('item'); // store the data elementstore('price', 20); // retrieve the data consolelog(elementretrieve('price')); // 20 In this snippet we modified our original example to use the store and retrieve methods from the element storage API.

#! /usr/bin/perl -w use strict; # The reduce is just passed the same key with # each value in the value group, so keep track of # the last key seen to determine when a new value group has started. my $lastSeenKey; my $currentCount = 0; while( <> ) { chomp; my $currentKey = $_; if ($lastSeenKey && $lastSeenKey ne $currentKey) { # emit the record for the previous key print $currentCount, "\t", $lastSeenKey, "\n"; $currentCount = 1; $lastSeenKey = $currentKey;

Like in our original example, the price data was attached to the element, which makes it possible for us to retrieve it later Unlike using attributes for data storage, the MooTools element storage API is much more flexible because it does not actually store anything on the element Instead, the element storage API uses a private object store that s accessible only to store and retrieve This private object store uses the uid property of an element to determine the data associated with the object, which it then exposes to store and retrieve for processing Because the element storage API uses a JavaScript object, it s possible to store any kind of data inside it without losing references var element = $('item'); var array = [1, 2, 3]; // store the data elementstore('items', array); // retrieve the data consolelog(element.

After reading this chapter, you ll be able to take an existing form written in Word, import it into InfoPath, tweak it as necessary (including drop-down lists that filter other lists), and create a custom task pane for context-specific information. Then you can publish the form to SharePoint and designate it as a content type (reusable forms) and set information management security policies for the form library. Finally, you ll be able to designate how to merge form data files and embed the form into your own applications if you choose to a veritable cornucopia of powerful form template features!

} else { # save currentKey away just in case it hasn't been saved $lastSeenKey = $currentKey; $currentCount++; } } # make sure that the count for the last key in the input is emitted if ($lastSeenKey) { print $currentCount, "\t", $lastSeenKey, "\n"; } Hadoop streaming is a wonderful tool. I had a large dataset composed of many input files in one compression format, and the data needed to be compressed in a different format. The author ran a streaming job, with the map executable set to /bin/cat and the minimum input split size set to Long.MAX_VALUE, and enabled map output compression of the required type. In a few minutes the cluster had uncompressed and recompressed the data files. The following streaming example takes input from the directory words, uses /bin/ cat as the map executable, has no reduce phase, and compresses the job output using the GzipCodec. The IdentityMapper could have been used just as easily, but the use of /bin/cat is just plain fun. bin/hadoop jar ./contrib/streaming/hadoop-0.19.0-streaming.jar -D mapred.output.compress=true -D mapred.output.compression.codec=org.apache.hadoop.io.compress.GzipCodec -D mapred.min.split.size=111111111111 -input words -mapper /bin/cat -numReduceTasks 0 -output next