[英]StormCrawler: best topology for cluster
我正在使用stormcrawler来抓取40k网站,max_depth = 2,并且我想尽快完成。 我有5个风暴节点(具有不同的静态ips)和3个弹性节点。 现在,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
和搜寻器配置:
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,有什么区别? 我尝试了AggregationSpout,但是性能等于默认配置下1台计算机的性能。
2)这种并行配置正确吗?
3)当我从1节点跳到5节点配置时,我发现“ FETCH ERROR”增加了约20%,并且很多站点无法正确获取。 可能是什么原因?
更新:
ES-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.*"
1)我应该使用AggreationsSpout还是CollapsingSpout,有什么区别? 我尝试了AggregationSpout,但是性能等于默认配置下1台计算机的性能。
顾名思义,AggregationSpout使用聚合作为一种按主机(或域或IP或其他)对URL进行分组的机制,而CollapsingSpout使用collapsing 。 如果将其配置为每个存储桶具有多个URL( es.status.max.urls.per.bucket ),则后者可能会变慢,因为它为每个存储桶发出子查询。 AggregationSpout应该具有良好的性能,尤其是在es.status.sample设置为true的情况下。 CollapsingSpouts在此阶段处于实验阶段。
2)这种并行配置正确吗?
这可能比需要的更多JSoupParserBolts。 实际上,与Fetcherbolts相比,比例为1:4很好,即使有500条取纱螺纹也是如此。 Storm UI对于发现瓶颈以及需要扩展的组件很有用。 其他所有内容看起来都不错,但是实际上,您应该查看Storm UI和指标以将拓扑调整为适合您的爬网的最佳设置。
3)当我从1节点跳到5节点配置时,我发现“ FETCH ERROR”增加了约20%,并且很多站点无法正确获取。 可能是什么原因?
这可能表明您已经饱和了网络连接,但相反,当使用更多节点时则不应该如此。 也许使用Storm UI检查FetcherBolts如何在节点上分布。 是一名工人在运行所有实例,还是所有实例都获得相等的数量? 查看日志以查看会发生什么,例如是否有大量超时异常?
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