nutch与起点R3集成之笔记（四）

        通过“nutch与起点R3集成之笔记（一、二、三）”中的步骤，我们可以建立起一个行业内部网的搜索引擎，但搜索引擎一个最重要的功能，就是必须能搜索到网络中最新的内容。这就要求nutch能及时采集到网络中的最新网页信息，同时将这些新采集到的信息更新到solr索引库中。故本篇介绍如何实现索引的更新和去重问题。

       我们随时可以使用nutch的crawl命令来爬行网站，例如，今天上午8：00采集一次新浪网站http://www.sina.com.cn，并通过nutch 的solrindex索引到solr索引库中，然后上午10:00再采集一次新浪网站，再通过solrindex索引到solr库中。这样做，可以保证用户能搜索到网络中较新的信息。在solr中实现没有什么问题，因为solr使用的是update方式更新索引库，因为索引field名为id的是主键，只要id是唯一的，update到索引库的操作就是成功的。   

       我们回过头来看看 《nutch与起点R3集成之笔记（二）》，在该篇中，我们是定义了用digest作为id，而没有采用在nutch的conf 的solrconfig.xml中定义用url作为id。其原因在于，如果用url作为id，在起点R3中会出现看不到索引数据等一些问题，我分析了很久，估计是由于url中会出现： /  ?  & 字符,这些字符不通过escape转换，在solr的q串提交会出现问题。

       我们来看一个solr的查询日志：

[search] webapp=null path=/select params={hl.snippets=3&q=id%3A20679dc38f64730579a1b2538727f76f&hl.simple.pre=%3Cfont+color%3D%27red%27%3E&hl.simple.post=%3C%2Ffont%3E&hl.fl=title&hl.fl=text&hl.usePhraseHighlighter=false&hl=true} hits=1 status=0 QTime=2

   其中：q=id%3A20679dc38f64730579a1b2538727f76f 是表示指定ID的查询串为 20679dc38f64730579a1b2538727f76f 的查询，如果用url作为id，而url也存在&字符，比如这样会导致将url中&后接内容作为查询参数，会导致不能正确出现查询结果。所以，在nutch向solr进行索引时，用url定义为ID是不合适的。

       nutch中digest是对采集的每一个网页内容的32位哈希值，如果两个网页内容完全一样，它们的digest值肯定会一样，但哪怕其中之一多或少一个空格，它们的digest值就会不一样。所以，我认为，用digest做id是一个非常不错的选择。

        如果nutch在两次不同的时间抓某个网页，例如还有新浪首页http://www.sina.com.cn，如果在两次抓取这段时间，首页没有什么变化，nutch计算出的两次抓取的首页的digest肯定是一样的，这样，nutch在第二次向solr进行索引时，发现digest在solr的索引库中已存在（id是唯一的），自然就写不进去。这样，避免了索引库的重复记录。

       同时，采用digest作为Id，也避免了一个行业内部网有镜像网站在solr索引库的重复记录。

       总之，采用digest作为Id，防止了nutch在向solr索引库写入时写入重复记录的问题，有天然去重功能。

       但是，还必须解决一个问题：如果nutch在两次不同的时间抓某个网页，如果这个网页有变化，两次的digest值肯定不同，这样，nutch在向solr索引库update时，会将同一个url（如http://www.sina.com.cn)写入两条记录。那么，在搜索界面键入：新浪，肯定会出现两条新浪网站数据。如此类推，nutch采集了同一url网页 n 次，就有可能在solr索引库中有n条相似的记录。这样的情况出现让我很抓狂，陷于困局之中。

        其实，这就是对同一个url进行去重的问题。nutch有一个去重的模块org.apache.nutch.indexer.solr.SolrDeleteDuplicates.java，命令为： bin/nutch solrdedup，分析发现，它是对digest相同的记录进行去重。估计是nutch把url作为solr的主键id，所以没有考虑到对url相同的记录进行去重。代码如下：

    

package org.apache.nutch.indexer.solr;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.net.MalformedURLException;
import java.text.SimpleDateFormat;
import java.util.Iterator;
import java.util.Date;

import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.mapred.InputFormat;
import org.apache.hadoop.mapred.InputSplit;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.RecordReader;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.lib.IdentityMapper;
import org.apache.hadoop.mapred.lib.NullOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import org.apache.nutch.util.NutchConfiguration;
import org.apache.nutch.util.NutchJob;
import org.apache.nutch.util.TimingUtil;
import org.apache.solr.client.solrj.SolrQuery;
import org.apache.solr.client.solrj.SolrServer;
import org.apache.solr.client.solrj.SolrServerException;
import org.apache.solr.client.solrj.impl.CommonsHttpSolrServer;
import org.apache.solr.client.solrj.request.UpdateRequest;
import org.apache.solr.client.solrj.response.QueryResponse;
import org.apache.solr.common.SolrDocument;
import org.apache.solr.common.SolrDocumentList;

/** 
 * Utility class for deleting duplicate documents from a solr index.
 *
 * The algorithm goes like follows:
 * 
 * Preparation:
 * <ol>
 * <li>Query the solr server for the number of documents (say, N)</li>
 * <li>Partition N among M map tasks. For example, if we have two map tasks
 * the first map task will deal with solr documents from 0 - (N / 2 - 1) and
 * the second will deal with documents from (N / 2) to (N - 1).</li>
 * </ol>
 * 
 * MapReduce:
 * <ul>
 * <li>Map: Identity map where keys are digests and values are {@link SolrRecord}
 * instances(which contain id, boost and timestamp)</li>
 * <li>Reduce: After map, {@link SolrRecord}s with the same digest will be
 * grouped together. Now, of these documents with the same digests, delete
 * all of them except the one with the highest score (boost field). If two
 * (or more) documents have the same score, then the document with the latest
 * timestamp is kept. Again, every other is deleted from solr index.
 * </li>
 * </ul>
 * 
 * Note that unlike {@link DeleteDuplicates} we assume that two documents in
 * a solr index will never have the same URL. So this class only deals with
 * documents with <b>different</b> URLs but the same digest. 
 */
public class SolrDeleteDuplicates
implements Reducer<Text, SolrDeleteDuplicates.SolrRecord, Text, SolrDeleteDuplicates.SolrRecord>,
Tool {

  public static final Log LOG = LogFactory.getLog(SolrDeleteDuplicates.class);

  private static final String SOLR_GET_ALL_QUERY = SolrConstants.ID_FIELD + ":[* TO *]";

  private static final int NUM_MAX_DELETE_REQUEST = 1000;

  public static class SolrRecord implements Writable {

    private float boost;
    private long tstamp;
    private String id;

    public SolrRecord() { }
    
    public SolrRecord(SolrRecord old) {
	this.id = old.id;
	this.boost = old.boost;
	this.tstamp = old.tstamp;
    }

    public SolrRecord(String id, float boost, long tstamp) {
      this.id = id;
      this.boost = boost;
      this.tstamp = tstamp;
    }

    public String getId() {
      return id;
    }

    public float getBoost() {
      return boost;
    }

    public long getTstamp() {
      return tstamp;
    }

    public void readSolrDocument(SolrDocument doc) {
      id = (String)doc.getFieldValue(SolrConstants.ID_FIELD);
      boost = (Float)doc.getFieldValue(SolrConstants.BOOST_FIELD);

      Date buffer = (Date)doc.getFieldValue(SolrConstants.TIMESTAMP_FIELD);
      tstamp = buffer.getTime();
    }

    public void readFields(DataInput in) throws IOException {
      id = Text.readString(in);
      boost = in.readFloat();
      tstamp = in.readLong();
    }

    public void write(DataOutput out) throws IOException {
      Text.writeString(out, id);
      out.writeFloat(boost);
      out.writeLong(tstamp);
    } 
  }

  public static class SolrInputSplit implements InputSplit {

    private int docBegin;
    private int numDocs;

    public SolrInputSplit() { }

    public SolrInputSplit(int docBegin, int numDocs) {
      this.docBegin = docBegin;
      this.numDocs = numDocs;
    }

    public int getDocBegin() {
      return docBegin;
    }

    public int getNumDocs() {
      return numDocs;
    }

    public long getLength() throws IOException {
      return numDocs;
    }

    public String[] getLocations() throws IOException {
      return new String[] {} ;
    }

    public void readFields(DataInput in) throws IOException {
      docBegin = in.readInt();
      numDocs = in.readInt();
    }

    public void write(DataOutput out) throws IOException {
      out.writeInt(docBegin);
      out.writeInt(numDocs);
    }
  }

  public static class SolrInputFormat implements InputFormat<Text, SolrRecord> {

    /** Return each index as a split. */
    public InputSplit[] getSplits(JobConf job, int numSplits) throws IOException {
      SolrServer solr = new CommonsHttpSolrServer(job.get(SolrConstants.SERVER_URL));

      final SolrQuery solrQuery = new SolrQuery(SOLR_GET_ALL_QUERY);
      solrQuery.setFields(SolrConstants.ID_FIELD);
      solrQuery.setRows(1);

      QueryResponse response;
      try {
        response = solr.query(solrQuery);
      } catch (final SolrServerException e) {
        throw new IOException(e);
      }

      int numResults = (int)response.getResults().getNumFound();
      int numDocsPerSplit = (numResults / numSplits); 
      int currentDoc = 0;
      SolrInputSplit[] splits = new SolrInputSplit[numSplits];
      for (int i = 0; i < numSplits - 1; i++) {
        splits[i] = new SolrInputSplit(currentDoc, numDocsPerSplit);
        currentDoc += numDocsPerSplit;
      }
      splits[splits.length - 1] = new SolrInputSplit(currentDoc, numResults - currentDoc);

      return splits;
    }

    public RecordReader<Text, SolrRecord> getRecordReader(final InputSplit split,
        final JobConf job, 
        Reporter reporter)
        throws IOException {

      SolrServer solr = new CommonsHttpSolrServer(job.get(SolrConstants.SERVER_URL));
      SolrInputSplit solrSplit = (SolrInputSplit) split;
      final int numDocs = solrSplit.getNumDocs();
      
      SolrQuery solrQuery = new SolrQuery(SOLR_GET_ALL_QUERY);
      solrQuery.setFields(SolrConstants.ID_FIELD, SolrConstants.BOOST_FIELD,
                          SolrConstants.TIMESTAMP_FIELD,
                          SolrConstants.DIGEST_FIELD);
      solrQuery.setStart(solrSplit.getDocBegin());
      solrQuery.setRows(numDocs);

      QueryResponse response;
      try {
        response = solr.query(solrQuery);
      } catch (final SolrServerException e) {
        throw new IOException(e);
      }

      final SolrDocumentList solrDocs = response.getResults();

      return new RecordReader<Text, SolrRecord>() {

        private int currentDoc = 0;

        public void close() throws IOException { }

        public Text createKey() {
          return new Text();
        }

        public SolrRecord createValue() {
          return new SolrRecord();
        }

        public long getPos() throws IOException {
          return currentDoc;
        }

        public float getProgress() throws IOException {
          return currentDoc / (float) numDocs;
        }

        public boolean next(Text key, SolrRecord value) throws IOException {
          if (currentDoc >= numDocs) {
            return false;
          }

          SolrDocument doc = solrDocs.get(currentDoc);
          String digest = (String) doc.getFieldValue(SolrConstants.DIGEST_FIELD);
          key.set(digest);
          value.readSolrDocument(doc);

          currentDoc++;
          return true;
        }    
      };
    }
  }

  private Configuration conf;

  private SolrServer solr;

  private int numDeletes = 0;

  private UpdateRequest updateRequest = new UpdateRequest();

  public Configuration getConf() {
    return conf;
  }

  public void setConf(Configuration conf) {
    this.conf = conf;
  }

  public void configure(JobConf job) {
    try {
      solr = new CommonsHttpSolrServer(job.get(SolrConstants.SERVER_URL));
    } catch (MalformedURLException e) {
      throw new RuntimeException(e);
    }
  }


  public void close() throws IOException {
    try {
      if (numDeletes > 0) {
        LOG.info("SolrDeleteDuplicates: deleting " + numDeletes + " duplicates");
        updateRequest.process(solr);
        solr.commit();
      }
    } catch (SolrServerException e) {
      throw new IOException(e);
    }
  }

  public void reduce(Text key, Iterator<SolrRecord> values,
      OutputCollector<Text, SolrRecord> output, Reporter reporter)
  throws IOException {
    SolrRecord recordToKeep = new SolrRecord(values.next());
    while (values.hasNext()) {
      SolrRecord solrRecord = values.next();
      if (solrRecord.getBoost() > recordToKeep.getBoost() ||
          (solrRecord.getBoost() == recordToKeep.getBoost() && 
              solrRecord.getTstamp() > recordToKeep.getTstamp())) {
        updateRequest.deleteById(recordToKeep.id);
        recordToKeep = new SolrRecord(solrRecord);
      } else {
        updateRequest.deleteById(solrRecord.id);
      }
      numDeletes++;
      if (numDeletes >= NUM_MAX_DELETE_REQUEST) {
        try {
          LOG.info("SolrDeleteDuplicates: deleting " + numDeletes + " duplicates");
          updateRequest.process(solr);
        } catch (SolrServerException e) {
          throw new IOException(e);
        }
        updateRequest = new UpdateRequest();
        numDeletes = 0;
      }
    }
  }

  public void dedup(String solrUrl) throws IOException {
    SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
    long start = System.currentTimeMillis();
    LOG.info("SolrDeleteDuplicates: starting at " + sdf.format(start));
    LOG.info("SolrDeleteDuplicates: Solr url: " + solrUrl);
    
    JobConf job = new NutchJob(getConf());

    job.set(SolrConstants.SERVER_URL, solrUrl);
    job.setInputFormat(SolrInputFormat.class);
    job.setOutputFormat(NullOutputFormat.class);
    job.setMapOutputKeyClass(Text.class);
    job.setMapOutputValueClass(SolrRecord.class);
    job.setMapperClass(IdentityMapper.class);
    job.setReducerClass(SolrDeleteDuplicates.class);

    JobClient.runJob(job);

    long end = System.currentTimeMillis();
    LOG.info("SolrDeleteDuplicates: finished at " + sdf.format(end) + ", elapsed: " + TimingUtil.elapsedTime(start, end));
  }

  public int run(String[] args) throws IOException {
    if (args.length != 1) {
      System.err.println("Usage: SolrDeleteDuplicates <solr url>");
      return 1;
    }

    dedup(args[0]);
    return 0;
  }

  public static void main(String[] args) throws Exception {
    int result = ToolRunner.run(NutchConfiguration.create(),
        new SolrDeleteDuplicates(), args);
    System.exit(result);
  }

}

 在上面MAPReduce注释说明中，谈到了如何在分布式服务Hadoop的MAPReduce中 实现删除相同的digest算法如下：

1.将相同的digest文档放在一组，然后只留下最高分（boost值）文档，将其它相同的digest文档删除掉（去重）;

2.如果最高分（boost值）有两个以上的文档，则将最新的时戳（timestamp）的文档留下，将其余的删除掉。

         分析代码发现， 只需要将代码中的有两个地方的DIGEST_FIELD替换为URL_FIELD ，就可以实现对相同的url去重。修改后，在eclipse中编译，找到主函数org.apache.nutch.indexer.solr.SolrDeleteDuplicates，建立起SolrDeleteDuplicates运行程序：

其对应的自变量设置为：

点击“运行”,在eclipse控制台上出现运行结果：

    表示url去重成功。

       总结如下：本文提出，在nutch形成solr索引时，采用digest作为ID，避免了nutch采用url作为ID出现的查询不到结果的问题。通过修改nutch去重模块org.apache.nutch.indexer.solr.SolrDeleteDuplicates.java，实现了对url的去重和更新。

 

来源：oschina

链接：https://my.oschina.net/u/164278/blog/29258