Nutch 2.0 抓取流程介绍

1. 整体流程
InjectorJob => GeneratorJob => FetcherJob => ParserJob => DbUpdaterJob => SolrIndexerJob
InjectorJob : 从文件中得到一批种子网页，把它们放到抓取数据库中去
GeneratorJob: 从抓取数据库中产生要抓取的页面放到抓取队列中去
FetcherJob:   对抓取队列中的网页进行抓取,在reducer中使用了生产/消费者模型
ParserJob:    对抓取完成的网页进行解析，产生一些新的链接与网页内容的解析结果
DbUpdaterJob: 把新产生的链接更新到抓取数据库中去
SolrIndexerJob: 对解析后的内容进行索引建立

2. InjectorJob分析  下面是InjectorJob的启动函数，代码如下

public Map<String,Object> run(Map<String,Object> args) throws Exception {
getConf().setLong("injector.current.time", System.currentTimeMillis());
Path input;
Object path = args.get(Nutch.ARG_SEEDDIR);
if (path instanceof Path) {
input = (Path)path;
} else {
input = new Path(path.toString());
}
numJobs = 2;
currentJobNum = 0;
status.put(Nutch.STAT_PHASE, "convert input");
currentJob = new NutchJob(getConf(), "inject-p1 " + input);
FileInputFormat.addInputPath(currentJob, input);
// mapper方法，从文件中解析出url，写入数据库
currentJob.setMapperClass(UrlMapper.class);
currentJob.setMapOutputKeyClass(String.class);
// map 的输出为WebPage，它是用Gora compile生成的，可以通过Gora把它映射到不同的数据库中，
currentJob.setMapOutputValueClass(WebPage.class);
// 输出到GoraOutputFormat
currentJob.setOutputFormatClass(GoraOutputFormat.class);
DataStore<String, WebPage> store = StorageUtils.createWebStore(currentJob.getConfiguration(),
String.class, WebPage.class);
GoraOutputFormat.setOutput(currentJob, store, true);
currentJob.setReducerClass(Reducer.class);
currentJob.setNumReduceTasks(0);
currentJob.waitForCompletion(true);
ToolUtil.recordJobStatus(null, currentJob, results);
currentJob = null;


status.put(Nutch.STAT_PHASE, "merge input with db");
status.put(Nutch.STAT_PROGRESS, 0.5f);
currentJobNum = 1;
currentJob = new NutchJob(getConf(), "inject-p2 " + input);
StorageUtils.initMapperJob(currentJob, FIELDS, String.class,
WebPage.class, InjectorMapper.class);
currentJob.setNumReduceTasks(0);
ToolUtil.recordJobStatus(null, currentJob, results);
status.put(Nutch.STAT_PROGRESS, 1.0f);
return results;
}
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因为InjectorJob扩展自NutchTool，实现了它的run方法。
   我们可以看到，这里有两个MR任务，第一个主要是从文件中读入种子网页，写到DataStore数据库中，第二个MR任务主要是对数据库中的WebPage对象做一个分数与抓取间隔的设置。它使用到一个initMapperJob方法，代码如下

public static <K, V> void initMapperJob(Job job,
Collection<WebPage.Field> fields,
Class<K> outKeyClass, Class<V> outValueClass,
Class<? extends GoraMapper<String, WebPage, K, V>> mapperClass,
Class<? extends Partitioner<K, V>> partitionerClass, boolean reuseObjects)
throws ClassNotFoundException, IOException {
// 这里是生成一个DataStore的抽象，这里的DataStore用户可以不同的模块，如Hbase，MySql等
DataStore<String, WebPage> store = createWebStore(job.getConfiguration(),
String.class, WebPage.class);
if (store==null) throw new RuntimeException("Could not create datastore");
Query<String, WebPage> query = store.newQuery();
query.setFields(toStringArray(fields));
GoraMapper.initMapperJob(job, query, store,
outKeyClass, outValueClass, mapperClass, partitionerClass, reuseObjects);
GoraOutputFormat.setOutput(job, store, true);
}
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3. GeneratorJob 源代码分析   下面是GeneratorJob的run方法代码

public Map<String,Object> run(Map<String,Object> args) throws Exception {
// map to inverted subset due for fetch, sort by score
Long topN = (Long)args.get(Nutch.ARG_TOPN);
Long curTime = (Long)args.get(Nutch.ARG_CURTIME);
if (curTime == null) {
curTime = System.currentTimeMillis();
}
Boolean filter = (Boolean)args.get(Nutch.ARG_FILTER);
Boolean norm = (Boolean)args.get(Nutch.ARG_NORMALIZE);
// map to inverted subset due for fetch, sort by score
getConf().setLong(GENERATOR_CUR_TIME, curTime);
if (topN != null)
getConf().setLong(GENERATOR_TOP_N, topN);
if (filter != null)
getConf().setBoolean(GENERATOR_FILTER, filter);
int randomSeed = Math.abs(new Random().nextInt());
batchId = (curTime / 1000) + "-" + randomSeed;
getConf().setInt(GENERATOR_RANDOM_SEED, randomSeed);
getConf().set(BATCH_ID, batchId);
getConf().setLong(Nutch.GENERATE_TIME_KEY, System.currentTimeMillis());
if (norm != null)
getConf().setBoolean(GENERATOR_NORMALISE, norm);
String mode = getConf().get(GENERATOR_COUNT_MODE, GENERATOR_COUNT_VALUE_HOST);
if (GENERATOR_COUNT_VALUE_HOST.equalsIgnoreCase(mode)) {
getConf().set(URLPartitioner.PARTITION_MODE_KEY, URLPartitioner.PARTITION_MODE_HOST);
} else if (GENERATOR_COUNT_VALUE_DOMAIN.equalsIgnoreCase(mode)) {
getConf().set(URLPartitioner.PARTITION_MODE_KEY, URLPartitioner.PARTITION_MODE_DOMAIN);
} else {
LOG.warn("Unknown generator.max.count mode '" + mode + "', using mode=" + GENERATOR_COUNT_VALUE_HOST);
getConf().set(GENERATOR_COUNT_MODE, GENERATOR_COUNT_VALUE_HOST);
getConf().set(URLPartitioner.PARTITION_MODE_KEY, URLPartitioner.PARTITION_MODE_HOST);
}


// 上面是设置一些要使用要的常量
numJobs = 1;
currentJobNum = 0;
// 生成一个job
currentJob = new NutchJob(getConf(), "generate: " + batchId);
// 初始化Map,这里的Map的输出类型为<SelectorEntry,WebPage>, 使用 SelectorEntryPartitioner来进行切分
StorageUtils.initMapperJob(currentJob, FIELDS, SelectorEntry.class,
WebPage.class, GeneratorMapper.class, SelectorEntryPartitioner.class, true);
// 初始化Reducer, 使用了generatorReducer来进行聚合处理
StorageUtils.initReducerJob(currentJob, GeneratorReducer.class);
currentJob.waitForCompletion(true);
ToolUtil.recordJobStatus(null, currentJob, results);
results.put(BATCH_ID, batchId);
return results;
}
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好像比原来的Generate简单很多，这里的GeneratorMapper完成的工作与之前的版本是一样的，如url的正规化，过滤，分数的设置，而GeneratorReducer完成的工作也和之前差不多，只是输出变成了DataStore，如HBase，完成以后会每个WebPage进行打标记，表示当前WebPage所完成的一个状态。

4. FetcherJob 源代码分析   使用了Gora的 fetcher比原来简单了很多，下面是其run的源代码

public Map<String,Object> run(Map<String,Object> args) throws Exception {
checkConfiguration();
String batchId = (String)args.get(Nutch.ARG_BATCH);
Integer threads = (Integer)args.get(Nutch.ARG_THREADS);
Boolean shouldResume = (Boolean)args.get(Nutch.ARG_RESUME);
Integer numTasks = (Integer)args.get(Nutch.ARG_NUMTASKS);

if (threads != null && threads > 0) {
getConf().setInt(THREADS_KEY, threads);
}
if (batchId == null) {
batchId = Nutch.ALL_BATCH_ID_STR;
}
getConf().set(GeneratorJob.BATCH_ID, batchId);
if (shouldResume != null) {
getConf().setBoolean(RESUME_KEY, shouldResume);
}

LOG.info("FetcherJob : timelimit set for : " + getConf().getLong("fetcher.timelimit", -1));
LOG.info("FetcherJob: threads: " + getConf().getInt(THREADS_KEY, 10));
LOG.info("FetcherJob: parsing: " + getConf().getBoolean(PARSE_KEY, false));
LOG.info("FetcherJob: resuming: " + getConf().getBoolean(RESUME_KEY, false));


// set the actual time for the timelimit relative
// to the beginning of the whole job and not of a specific task
// otherwise it keeps trying again if a task fails
long timelimit = getConf().getLong("fetcher.timelimit.mins", -1);
if (timelimit != -1) {
timelimit = System.currentTimeMillis() + (timelimit * 60 * 1000);
getConf().setLong("fetcher.timelimit", timelimit);
}
numJobs = 1;
currentJob = new NutchJob(getConf(), "fetch");
// 得到它过滤的字段
Collection<WebPage.Field> fields = getFields(currentJob);
// 初始化mapper, 其输出为<IntWritable,FetchEntry>
// 在mapper中输入数据进行过滤，主要是对不是同一个batch与已经fetch的数据进行过滤
StorageUtils.initMapperJob(currentJob, fields, IntWritable.class,
FetchEntry.class, FetcherMapper.class, FetchEntryPartitioner.class, false);
// 初始化reducer
StorageUtils.initReducerJob(currentJob, FetcherReducer.class);
if (numTasks == null || numTasks < 1) {
currentJob.setNumReduceTasks(currentJob.getConfiguration().getInt("mapred.map.tasks",
currentJob.getNumReduceTasks()));
} else {
currentJob.setNumReduceTasks(numTasks);
}
currentJob.waitForCompletion(true);
ToolUtil.recordJobStatus(null, currentJob, results);
return results;
}
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这里把原来在Mapper中使用到的生产者与消费者模型用到了reducer中，重写了reducer的run方法，在其中打开多个抓取线程，对url进行多线程抓取，有兴趣可以看一下FetcherReducer这个类。

5. ParserJob 代码分析   下面是ParserJob.java中的run代码

@Override
public Map<String,Object> run(Map<String,Object> args) throws Exception {
String batchId = (String)args.get(Nutch.ARG_BATCH);
Boolean shouldResume = (Boolean)args.get(Nutch.ARG_RESUME);
Boolean force = (Boolean)args.get(Nutch.ARG_FORCE);

if (batchId != null) {
getConf().set(GeneratorJob.BATCH_ID, batchId);
}
if (shouldResume != null) {
getConf().setBoolean(RESUME_KEY, shouldResume);
}
if (force != null) {
getConf().setBoolean(FORCE_KEY, force);
}
LOG.info("ParserJob: resuming:\t" + getConf().getBoolean(RESUME_KEY, false));
LOG.info("ParserJob: forced reparse:\t" + getConf().getBoolean(FORCE_KEY, false));
if (batchId == null || batchId.equals(Nutch.ALL_BATCH_ID_STR)) {
LOG.info("ParserJob: parsing all");
} else {
LOG.info("ParserJob: batchId:\t" + batchId);
}
currentJob = new NutchJob(getConf(), "parse");

Collection<WebPage.Field> fields = getFields(currentJob);
// 初始化mapper，输出类型为<String,WebPage>, 解析全部在maper完成
StorageUtils.initMapperJob(currentJob, fields, String.class, WebPage.class,
ParserMapper.class);
// 初始化reducer,这里是支持把<key,values>写到数据库中
StorageUtils.initReducerJob(currentJob, IdentityPageReducer.class);
currentJob.setNumReduceTasks(0);


currentJob.waitForCompletion(true);
ToolUtil.recordJobStatus(null, currentJob, results);
return results;
}
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6. DbUpdaterJob 代码分析
下面是DbUpdaterjob的run方法代码

public Map<String,Object> run(Map<String,Object> args) throws Exception {
String crawlId = (String)args.get(Nutch.ARG_CRAWL);
numJobs = 1;
currentJobNum = 0;
currentJob = new NutchJob(getConf(), "update-table");
if (crawlId != null) {
currentJob.getConfiguration().set(Nutch.CRAWL_ID_KEY, crawlId);
}
//job.setBoolean(ALL, updateAll);
ScoringFilters scoringFilters = new ScoringFilters(getConf());
HashSet<WebPage.Field> fields = new HashSet<WebPage.Field>(FIELDS);
fields.addAll(scoringFilters.getFields());

// Partition by {url}, sort by {url,score} and group by {url}.
// This ensures that the inlinks are sorted by score when they enter
// the reducer.

currentJob.setPartitionerClass(UrlOnlyPartitioner.class);
currentJob.setSortComparatorClass(UrlScoreComparator.class);
currentJob.setGroupingComparatorClass(UrlOnlyComparator.class);

// 这里的maper读取webpage中的outlinks字段值，对每个外链接计算分数
StorageUtils.initMapperJob(currentJob, fields, UrlWithScore.class,
NutchWritable.class, DbUpdateMapper.class);
// 对新生成的外链接设置一些分数，状态等信息，再把新的WebPage写回数据库
StorageUtils.initReducerJob(currentJob, DbUpdateReducer.class);
currentJob.waitForCompletion(true);
ToolUtil.recordJobStatus(null, currentJob, results);
return results;
}
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7. SolrIndexerJob 代码分析
下面是其run方法的源代码

@Override
public Map<String,Object> run(Map<String,Object> args) throws Exception {
String solrUrl = (String)args.get(Nutch.ARG_SOLR);
String batchId = (String)args.get(Nutch.ARG_BATCH);
NutchIndexWriterFactory.addClassToConf(getConf(), SolrWriter.class);
getConf().set(SolrConstants.SERVER_URL, solrUrl);


// 初始化 job
currentJob = createIndexJob(getConf(), "solr-index", batchId);
Path tmp = new Path("tmp_" + System.currentTimeMillis() + "-"
+ new Random().nextInt());
// 设置输出索引到文件，输出格式使用IndexeroutputFormat, 其默认调用Solr的API把数据传给Solr建立索引
FileOutputFormat.setOutputPath(currentJob, tmp);
currentJob.waitForCompletion(true);
ToolUtil.recordJobStatus(null, currentJob, results);
return results;
}
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有兴趣可以看一下SolrWriter，它实现了NutchIndexerWriter这个接口，来把数据写到不同的后台搜索引擎中，这里默认使用了Solr，当然你也可以通过实现它来扩展你自己的搜索引擎，当然nutch还提供了插件来自定义索引的字段值,也就是IndexingFilter.java这个接口。

8. 总结    Nutch 2.0个人感觉现在还是不成熟的，有很多功能还没有完成，主要的改变还是在它的数据存储层，把原来的数据存储进行了抽象，使其可以更好的运行在大规模数据抓取中，而且可以让用户来扩展具体的数据存储。当然数据存储层的变化带来了一些流程上的变化，有一些操作可以支持使用数据库操作来完成，这也大大减少了一些原来要MR任务来完成的代码。总之nutch 2.0  还是让我们看到了nutch的一个发展方向。希望它发现的越来越好吧。

商用一般使用的1版本

SOS~~~求问LZ,,,
在使用nutch后，爬取的都是一个一个的网页内容。。。
想问：后续的处理一般是怎么样的。
比如，想爬取一个网站的视频资源，如何下载下来呢。。。
谢谢啦！！