Gora官方文档之二：Gora对Map-Reduce的支持分类： C_OHTERS 2015-01-31 11:27 232人阅读评论(0) 收藏

参考官方文档：http://gora.apache.org/current/tutorial.html

项目代码见：https://code.csdn.net/jediael_lu/mygorademo

另环境准备见： http://blog.csdn.net/jediael_lu/article/details/43272521

当着数据已通过之前的示例存储在hbase中，数据如下：

\x00\x00\x00\x00\x00\x00\x00D              column=common:ip, timestamp=1422529645469, value=85.100.75.104

 \x00\x00\x00\x00\x00\x00\x00D              column=common:timestamp, timestamp=1422529645469, value=\x00\x00\x01\x1F\xF1\xB5\x88\xA0

 \x00\x00\x00\x00\x00\x00\x00D              column=common:url, timestamp=1422529645469, value=/index.php?i=2&a=1__z_nccylulyu&k=238241

 \x00\x00\x00\x00\x00\x00\x00D              column=http:httpMethod, timestamp=1422529645469, value=GET

 \x00\x00\x00\x00\x00\x00\x00D              column=http:httpStatusCode, timestamp=1422529645469, value=\x00\x00\x00\xC8

 \x00\x00\x00\x00\x00\x00\x00D              column=http:responseSize, timestamp=1422529645469, value=\x00\x00\x00+

 \x00\x00\x00\x00\x00\x00\x00D              column=misc:referrer, timestamp=1422529645469, value=http://www.buldinle.com/index.php?i=2&a=1__Z_nccYlULyU&k=238241

 \x00\x00\x00\x00\x00\x00\x00D              column=misc:userAgent, timestamp=1422529645469, value=Mozilla/5.0 (Windows; U; Windows NT 5.1; tr; rv:1.9.0.7) Gecko/2009021

                                            910 Firefox/3.0.7

 \x00\x00\x00\x00\x00\x00\x00E              column=common:ip, timestamp=1422529645469, value=85.100.75.104

 \x00\x00\x00\x00\x00\x00\x00E              column=common:timestamp, timestamp=1422529645469, value=\x00\x00\x01\x1F\xF1\xB5\xBFP

 \x00\x00\x00\x00\x00\x00\x00E              column=common:url, timestamp=1422529645469, value=/index.php?i=7&a=1__yxs0vome9p8&k=4924961

 \x00\x00\x00\x00\x00\x00\x00E              column=http:httpMethod, timestamp=1422529645469, value=GET

 \x00\x00\x00\x00\x00\x00\x00E              column=http:httpStatusCode, timestamp=1422529645469, value=\x00\x00\x00\xC8

 \x00\x00\x00\x00\x00\x00\x00E              column=http:responseSize, timestamp=1422529645469, value=\x00\x00\x00+

 \x00\x00\x00\x00\x00\x00\x00E              column=misc:referrer, timestamp=1422529645469, value=http://www.buldinle.com/index.php?i=7&a=1__YxS0VoME9P8&k=4924961

 \x00\x00\x00\x00\x00\x00\x00E              column=misc:userAgent, timestamp=1422529645469, value=Mozilla/5.0 (Windows; U; Windows NT 5.1; tr; rv:1.9.0.7) Gecko/2009021

                                            910 Firefox/3.0.7

本例将使用MR读取hbase中的数据，并进行分析，分析每个url，一天时间内有多少人在访问，输出结果保存在hbase中，表中的key为“url+时间”格式的String，value包括三列，分别是url，时间，访问次数。

0、创建java project及gora.properties，内容如下：

##gora.datastore.default is the default detastore implementation to use

##if it is not passed to the DataStoreFactory#createDataStore() method.

gora.datastore.default=org.apache.gora.hbase.store.HBaseStore

##whether to create schema automatically if not exists.

gora.datastore.autocreateschema=true

1、创建用于对应输入数据的json文件，并生成相应的类。

上个示例已经完成，见passview.json与PageView.java

{

  "type": "record",

  "name": "Pageview", "default":null,

  "namespace": "org.apache.gora.tutorial.log.generated",

  "fields" : [

    {"name": "url", "type": ["null","string"], "default":null},

    {"name": "timestamp", "type": "long", "default":0},

    {"name": "ip", "type": ["null","string"], "default":null},

    {"name": "httpMethod", "type": ["null","string"], "default":null},

    {"name": "httpStatusCode", "type": "int", "default":0},

    {"name": "responseSize", "type": "int", "default":0},

    {"name": "referrer", "type": ["null","string"], "default":null},

    {"name": "userAgent", "type": ["null","string"], "default":null}

  ]

}

2、创建输入数据的类与表映射文件

<?xml version="1.0" encoding="UTF-8"?>

<!--

  Gora Mapping file for HBase Backend

-->

<gora-otd>

  <table name="Pageview"> <!-- optional descriptors for tables -->

    <family name="common"/> <!-- This can also have params like compression, bloom filters -->

    <family name="http"/>

    <family name="misc"/>

  </table>

  <class name="org.apache.gora.tutorial.log.generated.Pageview" keyClass="java.lang.Long" table="AccessLog">

    <field name="url" family="common" qualifier="url"/>

    <field name="timestamp" family="common" qualifier="timestamp"/>

    <field name="ip" family="common" qualifier="ip" />

    <field name="httpMethod" family="http" qualifier="httpMethod"/>

    <field name="httpStatusCode" family="http" qualifier="httpStatusCode"/>

    <field name="responseSize" family="http" qualifier="responseSize"/>

    <field name="referrer" family="misc" qualifier="referrer"/>

    <field name="userAgent" family="misc" qualifier="userAgent"/>

  </class>

</gora-otd>

3、创建用于对于输出数据的json文件，并生成相应的类。

{

  "type": "record",

  "name": "MetricDatum",

  "namespace": "org.apache.gora.tutorial.log.generated",

  "fields" : [

    {"name": "metricDimension", "type": "string"},

    {"name": "timestamp", "type": "long"},

    {"name": "metric", "type" : "long"}

  ]

}

liaoliuqingdeMacBook-Air:MyGoraDemo liaoliuqing$ gora goracompiler avro/metricdatum.json src/

Compiling: /Users/liaoliuqing/99_Project/git/MyGoraDemo/avro/metricdatum.json

Compiled into: /Users/liaoliuqing/99_Project/git/MyGoraDemo/src

Compiler executed SUCCESSFULL.

4、创建输出数据的类与表映射内容，并将之加入第2步创建的文件中。

  <class name="org.apache.gora.tutorial.log.generated.MetricDatum" keyClass="java.lang.String" table="Metrics">

    <field name="metricDimension" family="common"  qualifier="metricDimension"/>

    <field name="timestamp" family="common" qualifier="ts"/>

    <field name="metric" family="common" qualifier="metric"/>

  </class>

5、写主类文件

程序处理的关键步骤：

（1）获取输入、输出DataStore

    if(args.length > 0) {

      String dataStoreClass = args[0];

      inStore = DataStoreFactory.

          getDataStore(dataStoreClass, Long.class, Pageview.class, conf);

      if(args.length > 1) {

        dataStoreClass = args[1];

      }

      outStore = DataStoreFactory.

          getDataStore(dataStoreClass, String.class, MetricDatum.class, conf);

    } else {

	    inStore = DataStoreFactory.getDataStore(Long.class, Pageview.class, conf);

	    outStore = DataStoreFactory.getDataStore(String.class, MetricDatum.class, conf);

    }

（2）设置job的一些基本属性

    Job job = new Job(getConf());

    job.setJobName("Log Analytics");

    log.info("Creating Hadoop Job: " + job.getJobName());

    job.setNumReduceTasks(numReducer);

    job.setJarByClass(getClass());

（3）定义job相关的Map类及mapr的输入输出信息。

GoraMapper.initMapperJob(job, inStore, TextLong.class, LongWritable.class,

        LogAnalyticsMapper.class, true);

（4）定义job相关的Reduce类及reduce的输入输出信息。

    GoraReducer.initReducerJob(job, outStore, LogAnalyticsReducer.class);

（5）定义map类

public static class LogAnalyticsMapper extends GoraMapper<Long, Pageview, TextLong,

      LongWritable> {

    private LongWritable one = new LongWritable(1L);

    private TextLong tuple;

    @Override

    protected void setup(Context context) throws IOException ,InterruptedException {

      tuple = new TextLong();

      tuple.setKey(new Text());

      tuple.setValue(new LongWritable());

    };

    @Override

    protected void map(Long key, Pageview pageview, Context context)

        throws IOException ,InterruptedException {

      CharSequence url = pageview.getUrl();

      long day = getDay(pageview.getTimestamp());

      tuple.getKey().set(url.toString());

      tuple.getValue().set(day);

      context.write(tuple, one);

    };

    /** Rolls up the given timestamp to the day cardinality, so that

     * data can be aggregated daily */

    private long getDay(long timeStamp) {

      return (timeStamp / DAY_MILIS) * DAY_MILIS;

    }

  }

（6）定义reduce类

public static class LogAnalyticsReducer extends GoraReducer<TextLong, LongWritable,

      String, MetricDatum> {

    private MetricDatum metricDatum = new MetricDatum();

    @Override

    protected void reduce(TextLong tuple, Iterable<LongWritable> values, Context context)

      throws IOException ,InterruptedException {

      long sum = 0L; //sum up the values

      for(LongWritable value: values) {

        sum+= value.get();

      }

      String dimension = tuple.getKey().toString();

      long timestamp = tuple.getValue().get();

      metricDatum.setMetricDimension(new Utf8(dimension));

      metricDatum.setTimestamp(timestamp);

      String key = metricDatum.getMetricDimension().toString();

      key += "_" + Long.toString(timestamp);

      metricDatum.setMetric(sum);

      context.write(key, metricDatum);

    };

  }

（8）使用输入输出DataStore来创建一个job，并执行

    Job job = createJob(inStore, outStore, 3);

    boolean success = job.waitForCompletion(true);

其实使用Gora与一般的MR程序的主要区别在于：

（1）继承于GoraMapper/GoraReducer，而不是Mapper/Reducer。

（2）使用GoraMapper.initMapperJob(), GoraReducer.initReducerJob()设置输入输出类型，而且可以使用一个DataSource类对象表示输入/输出的KEY-VALUE。

如本例中的mapper，使用instroe来代替指定了输入KV类型为Long，Pageview，本例中的reducer，使用outstore来代替指定了输出类型为String, MetricDatum。

对比http://blog.csdn.net/jediael_lu/article/details/43416751中所描述的运行一个job所需的基本属性：

GoraMapper.initMapperJob(job, inStore, TextLong.class, LongWritable.class,  LogAnalyticsMapper.class, true);

GoraReducer.initReducerJob(job, outStore, LogAnalyticsReducer.class);

以上语句同时完成了2、3、4、5步，即

指定了2、Map/Reduce的类：LogAnalyticsMapper.class与LogAnalyticsReducer.class

指定了3、4、输入格式及内容及5、reduce的输出类型：即输入输出均为DataSource格式，内容为inStore与outStore中的内容。

指定了5、指定了map的输出类型，这也是reduce的输入类型。

附详细代码：

（1）KeyValueWritable.java

package org.apache.gora.tutorial.log;

import java.io.DataInput;

import java.io.DataOutput;

import java.io.IOException;

import org.apache.hadoop.io.WritableComparable;

/**

 * A WritableComparable containing a key-value WritableComparable pair.

 * @param <K> the class of key

 * @param <V> the class of value

 */

public class KeyValueWritable<K extends WritableComparable, V extends WritableComparable>

  implements WritableComparable<KeyValueWritable<K,V>> {

  protected K key = null;

  protected V value =  null;

  public KeyValueWritable() {

  }

  public KeyValueWritable(K key, V value) {

    this.key = key;

    this.value = value;

  }

  public K getKey() {

    return key;

  }

  public void setKey(K key) {

    this.key = key;

  }

  public V getValue() {

    return value;

  }

  public void setValue(V value) {

    this.value = value;

  }

  @Override

  public void readFields(DataInput in) throws IOException {

    if(key == null) {

    }

    key.readFields(in);

    value.readFields(in);

  }

  @Override

  public void write(DataOutput out) throws IOException {

    key.write(out);

    value.write(out);

  }

  @Override

  public int hashCode() {

    final int prime = 31;

    int result = 1;

    result = prime * result + ((key == null) ? 0 : key.hashCode());

    result = prime * result + ((value == null) ? 0 : value.hashCode());

    return result;

  }

  @Override

  public boolean equals(Object obj) {

    if (this == obj)

      return true;

    if (obj == null)

      return false;

    if (getClass() != obj.getClass())

      return false;

    KeyValueWritable other = (KeyValueWritable) obj;

    if (key == null) {

      if (other.key != null)

        return false;

    } else if (!key.equals(other.key))

      return false;

    if (value == null) {

      if (other.value != null)

        return false;

    } else if (!value.equals(other.value))

      return false;

    return true;

  }

  @Override

  public int compareTo(KeyValueWritable<K, V> o) {

    int cmp = key.compareTo(o.key);

    if(cmp != 0)

      return cmp;

    return value.compareTo(o.value);

  }

}

(2) TextLong.java

package org.apache.gora.tutorial.log;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

/**

 * A {@link KeyValueWritable} of {@link Text} keys and

 * {@link LongWritable} values.

 */

public class TextLong extends KeyValueWritable<Text, LongWritable> {

  public TextLong() {

    key = new Text();

    value = new LongWritable();

  }

}

(3) LogAnalytics.java

package org.apache.gora.tutorial.log;

import java.io.IOException;

import org.apache.avro.util.Utf8;

import org.slf4j.Logger;

import org.slf4j.LoggerFactory;

import org.apache.gora.mapreduce.GoraMapper;

import org.apache.gora.mapreduce.GoraReducer;

import org.apache.gora.store.DataStore;

import org.apache.gora.store.DataStoreFactory;

import org.apache.gora.tutorial.log.generated.MetricDatum;

import org.apache.gora.tutorial.log.generated.Pageview;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.conf.Configured;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Job;

import org.apache.hadoop.util.Tool;

import org.apache.hadoop.util.ToolRunner;

/**

 * LogAnalytics is the tutorial class to illustrate Gora MapReduce API.

 * The analytics mapreduce job reads the web access data stored earlier by the

 * {@link LogManager}, and calculates the aggregate daily pageviews. The

 * output of the job is stored in a Gora compatible data store.

 *

 * <p>See the tutorial.html file in docs or go to the

 * <a href="http://incubator.apache.org/gora/docs/current/tutorial.html">

 * web site</a>for more information.</p>

 */

public class LogAnalytics extends Configured implements Tool {

  private static final Logger log = LoggerFactory.getLogger(LogAnalytics.class);

  /** The number of miliseconds in a day */

  private static final long DAY_MILIS = 1000 * 60 * 60 * 24;

  /**

   * The Mapper takes Long keys and Pageview objects, and emits

   * tuples of <url, day> as keys and 1 as values. Input values are

   * read from the input data store.

   * Note that all Hadoop serializable classes can be used as map output key and value.

   *

   */

  //6、定义map类

  public static class LogAnalyticsMapper extends GoraMapper<Long, Pageview, TextLong,

      LongWritable> {

    private LongWritable one = new LongWritable(1L);

    private TextLong tuple;

    @Override

    protected void setup(Context context) throws IOException ,InterruptedException {

      tuple = new TextLong();

      tuple.setKey(new Text());

      tuple.setValue(new LongWritable());

    };

    @Override

    protected void map(Long key, Pageview pageview, Context context)

        throws IOException ,InterruptedException {

      CharSequence url = pageview.getUrl();

      long day = getDay(pageview.getTimestamp());

      tuple.getKey().set(url.toString());

      tuple.getValue().set(day);

      context.write(tuple, one);

    };

    /** Rolls up the given timestamp to the day cardinality, so that

     * data can be aggregated daily */

    private long getDay(long timeStamp) {

      return (timeStamp / DAY_MILIS) * DAY_MILIS;

    }

  }

  /**

   * The Reducer receives tuples of <url, day> as keys and a list of

   * values corresponding to the keys, and emits a combined keys and

   * {@link MetricDatum} objects. The metric datum objects are stored

   * as job outputs in the output data store.

   */

  //7、定义reduce类

  public static class LogAnalyticsReducer extends GoraReducer<TextLong, LongWritable,

      String, MetricDatum> {

    private MetricDatum metricDatum = new MetricDatum();

    @Override

    protected void reduce(TextLong tuple, Iterable<LongWritable> values, Context context)

      throws IOException ,InterruptedException {

      long sum = 0L; //sum up the values

      for(LongWritable value: values) {

        sum+= value.get();

      }

      String dimension = tuple.getKey().toString();

      long timestamp = tuple.getValue().get();

      metricDatum.setMetricDimension(new Utf8(dimension));

      metricDatum.setTimestamp(timestamp);

      String key = metricDatum.getMetricDimension().toString();

      key += "_" + Long.toString(timestamp);

      metricDatum.setMetric(sum);

      context.write(key, metricDatum);

    };

  }

  /**

   * Creates and returns the {@link Job} for submitting to Hadoop mapreduce.

   * @param inStore

   * @param outStore

   * @param numReducer

   * @return

   * @throws IOException

   */

  public Job createJob(DataStore<Long, Pageview> inStore,

      DataStore<String, MetricDatum> outStore, int numReducer) throws IOException {

	 //3、设置job的一些基本属性

    Job job = new Job(getConf());

    job.setJobName("Log Analytics");

    log.info("Creating Hadoop Job: " + job.getJobName());

    job.setNumReduceTasks(numReducer);

    job.setJarByClass(getClass());

    /* Mappers are initialized with GoraMapper.initMapper() or

     * GoraInputFormat.setInput()*/

    //4、定义job相关的Map类及mapr的输入输出信息。

    GoraMapper.initMapperJob(job, inStore, TextLong.class, LongWritable.class,

        LogAnalyticsMapper.class, true);

    //4、定义job相关的Reduce类及reduce的输入输出信息。

    /* Reducers are initialized with GoraReducer#initReducer().

     * If the output is not to be persisted via Gora, any reducer

     * can be used instead. */

    GoraReducer.initReducerJob(job, outStore, LogAnalyticsReducer.class);

    return job;

  }

  @Override

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

    DataStore<Long, Pageview> inStore;

    DataStore<String, MetricDatum> outStore;

    Configuration conf = new Configuration();

    //1、获取输入、输出DataStore。

    if(args.length > 0) {

      String dataStoreClass = args[0];

      inStore = DataStoreFactory.

          getDataStore(dataStoreClass, Long.class, Pageview.class, conf);

      if(args.length > 1) {

        dataStoreClass = args[1];

      }

      outStore = DataStoreFactory.

          getDataStore(dataStoreClass, String.class, MetricDatum.class, conf);

    } else {

	    inStore = DataStoreFactory.getDataStore(Long.class, Pageview.class, conf);

	    outStore = DataStoreFactory.getDataStore(String.class, MetricDatum.class, conf);

    }

    //2、使用输入输出DataStore来创建一个job

    Job job = createJob(inStore, outStore, 3);

    boolean success = job.waitForCompletion(true);

    inStore.close();

    outStore.close();

    log.info("Log completed with " + (success ? "success" : "failure"));

    return success ? 0 : 1;

  }

  private static final String USAGE = "LogAnalytics <input_data_store> <output_data_store>";

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

    if(args.length < 2) {

      System.err.println(USAGE);

      System.exit(1);

    }

    //run as any other MR job

    int ret = ToolRunner.run(new LogAnalytics(), args);

    System.exit(ret);

  }

}

6、运行程序

（1）导出程序—>runnable jar file，并将其上传到服务器

（2）运行程序

$ java -jar MyGoraDemo.jar org.apache.gora.hbase.store.HBaseStore org.apache.gora.hbase.store.HBaseStore

（3）查看hbase中的结果

hbase(main):001:0> list

TABLE

AccessLog

Jan2814_webpage

Jan2819_webpage

Jan2910_webpage

Jan2920_webpage

Metrics

Passwd

member

8 row(s) in 2.6450 seconds

hbase(main):002:0> scan 'Metrics'

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Gora官方文档之二：Gora对Map-Reduce的支持分类： C_OHTERS 2015-01-31 11:27 232人阅读评论(0) 收藏

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