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Two possibilities come to mind for this sample MapReduce job: An indexed map file of search requests in the reduce task: For each search request key, the configure() method will open the relevant search space map file either the full map file for the entire search space or a partitioned file where the partition contains the networks that keys in this reduce task partition could match. The MapFile.getClosest() method would be used to find search space records that could match. Map-side join of the presorted search requests and a presorted search space: This method requires presorting the search request records and the search space records, and then using the map-side join techniques discussed in 8 and the classes for working with the IP address described in this chapter. Both require that the search space records be presorted. Also, in both cases, the search space records can either be partitioned as the search request records are partitioned, or the entire search space be present in each task, in Google Bigtable style (see http://labs.google. com/papers/bigtable.html). There are trade-offs between prepartitioning versus full replicas. The partitioned case reduces the data volume that must be scanned. Even with indexes, the amount of data that needs to be fetched from disk will be smaller in the partitioned case. The downsides are that search space needs to be repartitioned if the number of reduce tasks for the search requests is changed, and there is additional (though small) code complexity to ensure that the correct search space map file is opened in each search request reduce task. Both techniques lose the data being local for at least the search space records, and neither seem worth the bother at present, as it is not clear that there would be any performance gain.

}; var contacts = $('contacts').getElements('li'); contacts.each(function(contact){ contact.addEventListener('click', handler, false); }); Aside from type and target, there are a few other properties that events inherit from Event.prototype: bubbles is a Boolean property that denotes whether the event can be propagated by bubbling. Most events, like click, mouseover, or keypress, are bubbling events, but there are some events, like the load and unload events of windows, that do not bubble. If an event supports propagation by event bubbling, this property will be true and false otherwise. cancelable is a Boolean property that denotes whether the default action for an event can be canceled. Remember when we used the returnValue property of the IE event object to cancel the default action of the page This property of the standard model event object tells us whether cancelling the default action of an event is possible. currentTarget is a reference to the current element where the event is taking place during event propagation. This property is different from the target property, which always remains the same whether or not the currentTarget is the actual source of the event. eventPhase is a special property that can be compared with special browserdefined constants to check for the event propagation phase that s happening when the event handler was fired.

Now that you ve seen what s possible with basic managed code behind an InfoPath form, 10 will review some of the more advanced capabilities offered by Visual Studio.

They also require the search request records to be sorted, and the search space is expected to be relatively small.

All these properties are read-only, which means we can t set their values. This is different from the IE model, where some properties can be set to perform a particular action. The last two properties, however, deserve some more discussion, so we ll examine each of them in turn.

This chapter has walked you through the design and implementation of a nontrivial real-world Hadoop application. In the process, you have seen a number of design decisions made that become invalid as understanding arrives. The design and development process was deliberately oriented to provide initial functionality quickly so that this understanding could arrive sooner, rather than after a large and costly development cycle. A number of the advanced features, such as chaining and map-side joins, were used in the application, and a partitioner and several comparators were written. The tight coupling between the custom partitioner and the comparator allowed the application to perform range-based matching very efficiently using MapReduce techniques. The techniques that you have learned will allow you to efficiently and effectively tackle very complex problems that do not appear to fit the MapReduce framework, but in fact are ideally suited for MapReduce. Particularly in the rapidly evolving environment of today, you will never have time to build the perfect application just an application that works for yesterday s goals. Someone else will come along later and modify the application until it meets the new goals. Be kind to that person by leaving comments, testing, and keeping it simple. The person doing those future modifications may be you!