java注释代码规范

/**
* 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.
*/