/**

* 此类用来处理DNS原始日志：统计给定域名平均响应时延

* @param Input

* @param Output

* @param cacheUriListfilePath

* @param cacheIpNetTypefilePath

* <br>[文件cachefile需要上传HDFS,文件为K-V形式,多个V用;隔开]</br>

*

* <P><B>NOTE:</B>该类适合限定域名的时延统计,若统计所有域名的的平均时延此类不适用,因为reducer类使用集合进行汇聚,所有域名会导致内存溢出</p>

*/

/**

* This is an example Aggregated Hadoop Map/Reduce application. Computes the

* histogram of the words in the input texts.

*

* To run: bin/hadoop jar hadoop-*-examples.jar aggregatewordhist <i>in-dir</i>

* <i>out-dir</i> <i>numOfReducers</i> textinputformat

*

*/

/**

* Creates a <code>Statement</code> object for sending

* SQL statements to the database.

* SQL statements without parameters are normally

* executed using <code>Statement</code> objects. If the same SQL statement

* is executed many times, it may be more efficient to use a

* <code>PreparedStatement</code> object.

* <P>

* Result sets created using the returned <code>Statement</code>

* object will by default be type <code>TYPE_FORWARD_ONLY</code>

* and have a concurrency level of <code>CONCUR_READ_ONLY</code>.

* The holdability of the created result sets can be determined by

* calling {@link #getHoldability}.

*

* @return a new default <code>Statement</code> object

* @exception SQLException if a database access error occurs

* or this method is called on a closed connection

*/

/** Holds a <url, referrer, time > tuple */

static class AccessRecord implements Writable, DBWritable {.....}

/**

* <P>The basic service for managing a set of JDBC drivers.<br>

* <B>NOTE:</B> The {@link <code>DataSource</code>} interface, new in the

* JDBC 2.0 API, provides another way to connect to a data source.

* The use of a <code>DataSource</code> object is the preferred means of

* connecting to a data source.

*

* <P>As part of its initialization, the <code>DriverManager</code> class will

* attempt to load the driver classes referenced in the "jdbc.drivers"

* system property. This allows a user to customize the JDBC Drivers

* used by their applications. For example in your

* ~/.hotjava/properties file you might specify:

* <pre>

* <CODE>jdbc.drivers=foo.bah.Driver:wombat.sql.Driver:bad.taste.ourDriver</CODE>

* </pre>

*/

/**

* A tool interface that supports handling of generic command-line options.

*

* <p><code>Tool</code>, is the standard for any Map-Reduce tool/application.

* The tool/application should delegate the handling of

* <a href="{@docRoot}/../hadoop-project-dist/hadoop-common/CommandsManual.html#Generic_Options">

* standard command-line options</a> to {@link ToolRunner#run(Tool, String[])}

* and only handle its custom arguments.</p>

*

* <p>Here is how a typical <code>Tool</code> is implemented:</p>

* <p><blockquote><pre>

* public class MyApp extends Configured implements Tool {

*

* public int run(String[] args) throws Exception {

* // <code>Configuration</code> processed by <code>ToolRunner</code>

* Configuration conf = getConf();

*

* // Create a JobConf using the processed <code>conf</code>

* JobConf job = new JobConf(conf, MyApp.class);

*

* // Process custom command-line options

* Path in = new Path(args[1]);

* Path out = new Path(args[2]);

*

* // Specify various job-specific parameters

* job.setJobName("my-app");

* job.setInputPath(in);

* job.setOutputPath(out);

* job.setMapperClass(MyMapper.class);

* job.setReducerClass(MyReducer.class);

*

* // Submit the job, then poll for progress until the job is complete

* JobClient.runJob(job);

* return 0;

* }

*

* public static void main(String[] args) throws Exception {

* // Let <code>ToolRunner</code> handle generic command-line options

* int res = ToolRunner.run(new Configuration(), new MyApp(), args);

*

* System.exit(res);

* }

* }

* </pre></blockquote></p>

*

* @see GenericOptionsParser

* @see ToolRunner

*/

/**

* Rounding mode to round away from zero. Always increments the

* digit prior to a non-zero discarded fraction. Note that this

* rounding mode never decreases the magnitude of the calculated

* value.

*

*<p>Example:

*<table border>

*<tr valign=top><th>Input Number</th>

* <th>Input rounded to one digit<br> with {@code UP} rounding

*<tr align=right><td>5.5</td> <td>6</td>

*<tr align=right><td>2.5</td> <td>3</td>

*<tr align=right><td>1.6</td> <td>2</td>

*<tr align=right><td>1.1</td> <td>2</td>

*<tr align=right><td>1.0</td> <td>1</td>

*<tr align=right><td>-1.0</td> <td>-1</td>

*<tr align=right><td>-1.1</td> <td>-2</td>

*<tr align=right><td>-1.6</td> <td>-2</td>

*<tr align=right><td>-2.5</td> <td>-3</td>

*<tr align=right><td>-5.5</td> <td>-6</td>

*</table>

*/

/**

* This program uses map/reduce to just run a distributed job where there is

* no interaction between the tasks and each task write a large unsorted

* random binary sequence file of BytesWritable.

* In order for this program to generate data for terasort with 10-byte keys

* and 90-byte values, have the following config:

* <pre>{@code

* <?xml version="1.0"?>

* <?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

* <configuration>

* <property>

* <name>mapreduce.randomwriter.minkey</name>

* <value>10</value>

* </property>

* <property>

* <name>mapreduce.randomwriter.maxkey</name>

* <value>10</value>

* </property>

* <property>

* <name>mapreduce.randomwriter.minvalue</name>

* <value>90</value>

* </property>

* <property>

* <name>mapreduce.randomwriter.maxvalue</name>

* <value>90</value>

* </property>

* <property>

* <name>mapreduce.randomwriter.totalbytes</name>

* <value>1099511627776</value>

* </property>

* </configuration>}</pre>

* Equivalently, {@link RandomWriter} also supports all the above options

* and ones supported by {@link GenericOptionsParser} via the command-line.

*/