.net pdf 417

.net pdf 417

.net pdf 417

.net pdf 417

.net pdf 417

.net pdf 417,

.net pdf 417,
.net pdf 417,

.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,

.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,
.net pdf 417,

The parameter dfs.datanode.handler.count (covered earlier in the Block Service Threads section) is the number of worker threads for storage requests. The following are some other DataNode parameters that may be adjusted: dfs.data.dir: This parameter sets the location(s) for block storage. It is a commaseparated list of directories that will be used for block storage by the DataNode. Blocks will be distributed in a round-robin manner among the directories. If multiple directories are to be used, it is best if each directory resides on an independent storage device. This will allow concurrent I/O operations to be active on all of the devices. The default value for this parameter is ${hadoop.tmp.dir}/dfs/data. dfs.replication: This parameter controls the number of copies of each data block the cluster attempts to maintain. HDFS is resilient in the face of individual DataNode failures, because individual blocks are replicated to more than one machine. The HDFS cluster can withstand the failure of one less than the value of dfs.replication before there will be service degradation, in that some files may not be served as all of the blocks are not available. In small to medium-sized clusters, 3 is a good value. For large clusters, it should be a number that is at least two larger than the expected number of machine failures per day. If you have a 1,000-machine cluster and expect to have no more than 5 machines fail at a time, setting the replication factor to 7 is reasonable. The disk storage requirements and the write network bandwidth used are multiplied by this number.

Figure 5-4. Publishing wizard screen for user location 11. Click Next. 12. The final screen verifies your publishing information. Click the Publish button. 13. Your form is now published and available from the URL you entered.

Here, the two last operations are equivalent: passing an option with the name onSleep is the same as attaching an event handler for the sleep event using addEvent.

note When a client writes data to HDFS, each block is written to dfs.replication count DataNodes. Each node writes to the next node, to mitigate the network load on the individual machines. The nodes are selected more or less on a random basis, with some simple rules. If the origination machine is a DataNode for the HDFS cluster being written, one replica will go to that DataNode. HDFS has some concept of network topology, but this does not appear to be generally used as yet.

dfs.block.size: This parameter controls the basic file system block size for HDFS. HDFS is highly optimized for a medium number of very large files. The default value for an HDFS block is 64MB (67,108,864 bytes). Each file data block in the file system will be in a single file in the dfs.data.dir directory. A file in HDFS is composed of one or more data blocks. The last data block of a file may have less than the dfs.block.size bytes of data present. Any prior blocks will have dfs.block.size bytes of data. The block size may be specified on a per-file basis when the file is created. The individual blocks of the file are replicated onto dfs.replication DataNodes. The default input split size for a file being used as input in a MapReduce job is the dfs.block.size for the file. HDFS currently does not deal with very large numbers of files or very large numbers of blocks per DataNode.

InfoPath 2003 had the capability to enable submitting a form by automatically posting the form into Outlook for the user to send. However, this was accomplished by generating an e-mail with some preformatted text and attaching the XML file that comprised the form data. InfoPath 2007, combined with Outlook 2007, gives form designers the capability to embed a form inside the e-mail body dynamically, so the recipient can fill out the form without opening InfoPath. In this manner, a form may be published by e-mail out to the recipients; when the recipient gets the form, they can fill it in inside their e-mail client, as shown in Figure 5-5.

One feature of some classical programming languages is the existence of static members. A static member is a property or method that is accessible via the class itself, but not from its instances. Revising our ooc example from 4: Person: class { // Properties name := "" age := 0 // Methods init: func (name: String, age: Int) { this name = name this age = age } log: func { printf("%s, %d", this name, this age) } // Static Property count := static 0 // Static Methods addPerson: static func { Person count += 1 } getCount: static func { printf("Person count: %d", Person count) } } // Creating a new Person instance mark := Person new("Mark", 23) mark log() // "Mark, 23" // Accessing the static method Person addPerson() Person getCount() // "Person count: 1" In this example, we added three new static members to our original ooc Person class: a static property count and the static methods addPerson and getCount. Because these members are static, we can t call them using our mark instance. Instead, we called them directly from the class itself, as seen in the last lines. You ll also notice that in our addPerson and getCount methods, we didn t use the this keyword. Instead, we accessed the count property through the class identifier itself.

note Application developers are encouraged to use archives for storing multiple small files instead of creating them directly in HDFS. In one of my applications, there was a three-order-of-magnitude speed increase in an application when a set of zip files were constructed once per reduce task, instead of large numbers of small files in the reduce task.