Gora官方文档之二：Gora对Map-Reduce的支持

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'