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The check for success primarily involves ensuring that the number of records output is roughly the same as the number of records input. Hadoop jobs are generally dealing with bulk realworld data, which is never 100% clean, so a small error rate is generally acceptable.

We use the set method to change the inner text of our new list item, and this will update the result like this: <ul id="list"> <li>Item A</li> <li>Item B</li> <li>Item C</li> </ul> While this works, there s an even easier way to do it The Element constructor actually allows a second argument, properties, which is an object that will automatically be passed to set We can remove the extra call to set in our example above and move the text setting to the Elements constructor: var newItem = new Element('li', {'text': 'Item C'}); newItem.

if (format != 0) { logger.warn( "There were " + format + " keys that were not " + "transformable to long values"); } /** Check to see if we had any unexpected exceptions. * This usually indicates some significant problem, * either with the machine running the task that had * the exception, or the map or reduce function code. * Log an error for each type of exception with the count. */ if (exceptions > 0 ) { Counters.Group exceptionGroup = jobCounters.getGroup( TransformKeysToLongMapper.EXCEPTIONS ); for (Counters.Counter counter : exceptionGroup) { logger.error( "There were " + counter.getCounter() + " exceptions of type " + counter.getDisplayName() ); } } if (total == parsed) { logger.info("The job completed successfully."); System.exit(0); } // // // if We had some failures in handling the input records. Did enough records process for this to be a successful job is 90% good enough (total * .9 <= parsed) { logger.warn( "The job completed with some errors, " + (total - parsed) + " out of " + total ); System.exit( 0 );

Another interesting capability is that you can set up multiple types of repeating sections to be inserted. They must all have the same data construction (to match the underlying schema), but you can customize the default data of the section. So, for example, you can offer the user the option to insert a new widget section for various types of widgets. Using nested advanced controls, this can become a very powerful feature. On the Display tab, you can indicate whether additional sections should be added vertically or horizontally. The Filter Data button allows you to add a filter to the repeating section so that only sections with data matching the filter are displayed. You can map the filter to another control so that you can use a control to dynamically filter the displayed data. Or you can use a fixed filter for a particular view to provide a less cluttered display.

inject('list'); This internal use of the set method makes it easy to define really complex elements: new Element('div', { 'id': 'wrapper', 'class': 'container', 'html': '<p>Hello!</p>', 'styles': { 'background': '#000', 'color': '#FFF', 'font-size': '12px' }, 'data-name': 'wrapper div' }); This snippet creates a new div element and automatically sets both its attributes and styles, as well as its innerHTML value Using the Element constructor this way makes it easy to create elements on the fly and decreases additional calls to the element modification methods Before we close this section, though, there s one more nifty trick with Element: you aren t limited to passing just an HTML tag for the first argument you can also use a full CSS selector Suppose you want to create a new link that looks like this: <a id="home-link" class="internal" href="home.

} logger.error( "The job did not complete successfully," +" too many errors processing the input, only " + parsed + " of " + total + "records completed" ); System.exit( 1 ); In this particular case, you would expect a small number of NumberFormatExceptions but no other exceptions. If the total number of input records is roughly the number of parsed input records, and you have no unexpected exceptions, this job is a success.

The reduce method is called once for each key, and passes the key and an iterator to all of the map output values that share that key. The reduce task is an ideal place for summarizing data and for doing basic duplicate suppression.