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The Hadoop framework is responsible for loading the job input and converting that input into key/value pairs that are passed to the map method of the mapper, passing the key/value pairs output by the map method of the mapper to the reduce() method of the reducer, and taking the key/value pairs output by the reduce() method of the reducer and writing them to the job output. The class that loads and transforms the input into key/value pairs is derived from InputFormat and requires that the type of the key and the type of the value be specified. The class that handles loading the input is responsible for producing keys and values of the correct type. A commonly used class is the KeyValueTextInput class, which parses the input as text files, with each record on a single line and the key and value separated by the first tab character. The key type is org.apache.hadoop.io.Text, and the value type is org.apache.hadoop.io.Text. If the job does not explicitly configure the map output class as org.apache.hadoop.io.Text or the job output class as org.apache.hadoop.io.Text, the reduce will fail with a key type mismatch error. An InputFormat object has an associated RecordReader object. The RecordReader must provide createKey and createValue objects. The types of these objects will be used to define the mapper class input key and value types. The class to receive and transform the output key/value pairs is derived from OutputFormat. The default value for the key class is LongWritable, and the default value for the value class is org. apache.hadoop.io.Text. The job may specify different classes via the setOutputKeyClass and setOutputValueClass methods, respectively. By default, the expected map output types are the same as the expected reduce input and output types.

The job may specify that the map output key type and or the map output value type is different from the job output key and value type. The setMapOutputKeyClass method allows the job to specify the map output key class and the reduce input key class as being different from the job output key class. The setMapOutputValueClass method allows the job to specify the map output value class and reduce input value class as being different that the job output key class. The class specified under the key map.sort.class in the configuration will be used to sort the key objects if a reduce has been requested by the job. The default value for this key is org.apache.hadoop.util.QuickSort, an implementation of org.apache.hadoop.util.IndexedSorter. The key and value classes can be any type as long as the framework is provided with serializer classes and deserializer classes that implement org.apache.hadoop.io.serializer.Serializer and org.apache.hadoop.io.serializer.Deserializer, and the class names are added to the list stored in the configuration under the key io.serialization.

var notify = $('notify'); var request = new Request({ url: 'http://foo.com/comment/', data: { 'name': 'Mark', 'age': 23 } }); request.addEvents({ 'request': function(){ notify.set('html', '<strong>Request sent, please wait.</strong>'); }, 'success': function(){ notify.set('html', this.response.text); } }); In this snippet, we removed the formal parameters for our success event handler, and instead accessed the raw response body using the response object property of the request.

Now that you have all the InfoPath bits and pieces you need, you can get your workflow hooked up so that you can publish it and take it for a test spin! You need to add the event handlers to the activities you re using. I m going to walk through the workflow activities you ve laid out and explain the configuration necessary for each one.

public void setMapOutputKeyClass(Class< > theClass)

In contrast to the success event, the failure event s handler functions receive only one argument: the wrapped native XHR object. MooTools passes the native XHR object so we can handle the failure ourselves. Take note, though, that even if we re not passed the response text and response xml values, we can still access them using the response property object in our failure event handler. var notify = $('notify'); var request = new Request({ url: 'http://foo.com/comment/', data: { 'name': 'Mark', 'age': 23 } }); request.addEvents({ 'request': function(){ notify.set('html', '<strong>Request sent, please wait.</strong>'); }, 'success': function(){ notify.set('html', this.response.text); }, 'failure': function(){

This method stores the class name of theClass in the configuration under the key mapred. mapoutput.key.class. The type of this class will be used as the type of the map method output keys and the Reducer.reduce() method input keys. This class must be serializable and deserializable by a class defined in the list of serializers specified in the value of the configuration key io.serializations. theClass must be sortable by the class returned by getOutputKeyComparator().

notify.set('html', '<strong>Request failed, please try again.</strong>'); } }); We didn t need any fancy error handling in our original native example, so we just attached a basic failure event handler.

public Class< > getMapOutputKeyClass()

1. Click in the Invoked handler, type onInvoked, and then press Enter to create the handler for this event. 2. Add class variables for your instructions by adding the following code before the workflow constructor:

This method looks up the value of the key mapred.mapoutput.key.class in the configuration. If the value is unset, null is returned. If the value cannot be instantiated as a class, a RuntimeException is thrown. This class will also be the Reducer.reduce() method input key class. The default class for this is the job output key class, getOutputKeyClass(), and the default for it is LongWritable.