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Let s start with the basic text box and use that to examine some of the common features of all controls. From the text box on the form you designed in Exercise 3-1, open the Text Box Properties dialog (either double-click the text box or right-click and select Text Box Properties). The Text Box Properties dialog is shown in Figure 3-13.

configure() constructor() configure() constructor() configure() reduce() Key0 map() Key0 map() Key0 reduce() Key1 map() Key1 map() Key1 reduce() Key2 map() Key2 map() Key2 reduce() Key3 map() Key3 map() Key3 reduce() Key4 map() Key4 map() Key4 close() close() close()

Now we re told that we need to find the room with the blue wallpaper We aren t told which room that is, so it could be any room: it could be the living room; it could be the kitchen; it could even be a small walk-in closet In this case, we can t use path selection since we don t know what path to take from the front door to the blue-wallpapered room In fact, we don t really know where this room is So how do we find it I m sure we d all probably do the same thing: check all the rooms until we find the one with the blue wallpaper We d start with the room closest to us and check the wallpaper If the wallpaper is blue, we re done we found our room.

In a traditional MapReduce job, the framework sorts all data for a reduce task before presenting the keys sequentially to the reduce task. If the input data is already sorted, traditional MapReduce requires that the full map shuffle and sort process take place before the reduce task receives the sorted keys. Map-side joins provide a way for a map task to receive keys in sequential order and to receive all the values associated with each key (very similar to a reduce task). The map task reads the data directly from HDFS and no reduce is needed, which greatly reduces cluster loading. The author processed a dataset that was reduced from 5 hours to 12 minutes by converting the job to use map-side joins. The map-side join provides a framework for performing operations on multiple sorted datasets. Although the individual map tasks in a join lose much of the advantage of data locality, the overall job gains due to the potential for the elimination of the reduce phase and/or the great reduction in the amount of data required for the reduce.

Field Name The field name refers to the name of the underlying element. If you are designing a blank form, you can change this and it will rename the underlying schema element (a good idea if you don t want a schema with elements named field1, field2, etc.) There are naming restrictions on this field: No spaces or special characters are allowed. The name must start with a letter or underscore. It can only contain letters, numbers, hyphens, underscores, and periods.

If not, we move on to the next room and check that, and so on until we find our blue-wallpapered room This same technique can be applied to solve our original problem If we want to find the node named C1, we can check each node in the tree, starting from the top and working our way down until we find the node in question var find = function(node, name){ if (nodename == name){ return node; } else if (nodechildren && nodechildrenlength){ for (var i = 0, y = nodechildrenlength; i < y; i++){ var found = find(nodechildren[i], name); if (found) return found; } } return null; }; var C1 = find(treeroot, 'C1'); consolelog(C1name === 'C1'); // true Here we implemented a recursive function called find to locate a particular node.

Caution There are several constraints on when map-side joins may be used, and the cluster loses capability to manage data locality for the map tasks (see Table 8-11). There are also bugs in the join code that cause unpredictable behavior if there are more than 31 tables in a join; see https://issues.apache.org/ jira/browse/HADOOP-5589 and https://issues.apache.org/jira/browse/HADOOP-5571.