StormCrawler:集群的最佳拓扑结构

Question

我使用风暴爬虫来爬行40k站点，使用max_depth=2，我想尽可能快地完成它。我有5个风暴节点(具有不同的静态ips)和3个弹性节点。就目前而言，我最好的拓扑是：

spouts:
  - id: "spout"
    className: "com.digitalpebble.stormcrawler.elasticsearch.persistence.CollapsingSpout"
    parallelism: 10

bolts:
  - id: "partitioner"
    className: "com.digitalpebble.stormcrawler.bolt.URLPartitionerBolt"
    parallelism: 1
  - id: "fetcher"
    className: "com.digitalpebble.stormcrawler.bolt.FetcherBolt"
    parallelism: 5
  - id: "sitemap"
    className: "com.digitalpebble.stormcrawler.bolt.SiteMapParserBolt"
    parallelism: 5
  - id: "parse"
    className: "com.digitalpebble.stormcrawler.bolt.JSoupParserBolt"
    parallelism: 100
  - id: "index"
    className: "com.digitalpebble.stormcrawler.elasticsearch.bolt.IndexerBolt"
    parallelism: 25
  - id: "status"
    className: "com.digitalpebble.stormcrawler.elasticsearch.persistence.StatusUpdaterBolt"
    parallelism: 25
  - id: "status_metrics"
    className: "com.digitalpebble.stormcrawler.elasticsearch.metrics.StatusMetricsBolt"
    parallelism: 5

和爬虫配置：

config: 
  topology.workers: 5
  topology.message.timeout.secs: 300
  topology.max.spout.pending: 250
  topology.debug: false
  fetcher.threads.number: 500
  worker.heap.memory.mb: 4096

问题: 1)我应该使用AggreationsSpout还是CollapsingSpout，区别是什么？我尝试了AggregationSpout，但性能与默认配置的1台计算机的性能相同。

( 2)这种并行性的说法正确吗？

3)当我从1个节点跳到5个节点时，“获取错误”增加了约20%，许多站点没有被正确提取。可能是什么原因？

更新：

S-conf.yaml.

# configuration for Elasticsearch resources

config:
  # ES indexer bolt
  # adresses can be specified as a full URL
  # if not we assume that the protocol is http and the port 9200
  es.indexer.addresses: "1.1.1.1"
  es.indexer.index.name: "index"
  es.indexer.doc.type: "doc"
  es.indexer.create: false
  es.indexer.settings:
    cluster.name: "webcrawler-cluster"

  # ES metricsConsumer
  es.metrics.addresses: "http://1.1.1.1:9200"
  es.metrics.index.name: "metrics"
  es.metrics.doc.type: "datapoint"
  es.metrics.settings:
    cluster.name: "webcrawler-cluster"

  # ES spout and persistence bolt
  es.status.addresses: "http://1.1.1.1:9200"
  es.status.index.name: "status"
  es.status.doc.type: "status"
  #es.status.user: "USERNAME"
  #es.status.password: "PASSWORD"
  # the routing is done on the value of 'partition.url.mode'
  es.status.routing: true
  # stores the value used for the routing as a separate field
  # needed by the spout implementations
  es.status.routing.fieldname: "metadata.hostname"
  es.status.bulkActions: 500
  es.status.flushInterval: "5s"
  es.status.concurrentRequests: 1
  es.status.settings:
    cluster.name: "webcrawler-cluster"

  ################
  # spout config #
  ################

  # positive or negative filter parsable by the Lucene Query Parser
  # es.status.filterQuery: "-(metadata.hostname:stormcrawler.net)"

  # time in secs for which the URLs will be considered for fetching after a ack of fail
  es.status.ttl.purgatory: 30

  # Min time (in msecs) to allow between 2 successive queries to ES
  es.status.min.delay.queries: 2000

  es.status.max.buckets: 50
  es.status.max.urls.per.bucket: 2
  # field to group the URLs into buckets
  es.status.bucket.field: "metadata.hostname"
  # field to sort the URLs within a bucket
  es.status.bucket.sort.field: "nextFetchDate"
  # field to sort the buckets
  es.status.global.sort.field: "nextFetchDate"

  # Delay since previous query date (in secs) after which the nextFetchDate value will be reset
  es.status.reset.fetchdate.after: -1

  # CollapsingSpout : limits the deep paging by resetting the start offset for the ES query 
  es.status.max.start.offset: 500

  # AggregationSpout : sampling improves the performance on large crawls
  es.status.sample: false

  # AggregationSpout (expert): adds this value in mins to the latest date returned in the results and
  # use it as nextFetchDate
  es.status.recentDate.increase: -1
  es.status.recentDate.min.gap: -1

  topology.metrics.consumer.register:
       - class: "com.digitalpebble.stormcrawler.elasticsearch.metrics.MetricsConsumer"
         parallelism.hint: 1
         #whitelist:
         #  - "fetcher_counter"
         #  - "fetcher_average.bytes_fetched"
         #blacklist:
         #  - "__receive.*"

Answer 1

1)我应该使用AggreationsSpout还是CollapsingSpout，区别是什么？我尝试了AggregationSpout，但性能与默认配置的1台计算机的性能相同。

顾名思义，AggregationSpout使用聚合作为按主机(或域或IP )分组URL的机制，而CollapsingSpout使用塌陷。如果您将后者配置为每个桶(es.status.max.urls.per.bucket)有超过一个URL，则后者可能会慢一些，因为它为每个桶发出子查询。AggregationSpout应该具有良好的性能，特别是当es.status.sample设置为true时。CollapsingSpouts在这个阶段是实验性的。

( 2)这种并行结构正确吗？

这可能比需要的JSoupParserBolts更多。实际上，即使有500个抓取线程，与fetching螺栓相比，1:4的比例也不错。Storm对于发现瓶颈以及哪些组件需要扩展非常有用。其他一切看起来都没问题，但实际上，您应该查看Storm和度量标准，以便将拓扑调整到爬行的最佳设置。

3)当我从1个节点跳到5个节点时，“获取错误”增加了约20%，许多站点没有被正确提取。可能是什么原因？

这可能意味着您正在饱和您的网络连接，但当使用更多的节点时，情况不应该是这样的，相反。也许可以向Storm检查FetcherBolts是如何跨节点分布的。是一个工作人员运行所有实例，还是它们都得到一个相同的数字？看看日志，看看会发生什么，例如，是否有大量超时异常？