This organizes all urls to be loaded in separate queues for each host.
Each host separates the crawl depth into it's own queue. The primary
rule for urls taken from any queue is, that the crawl depth is minimal.
This produces a crawl depth which is identical to the clickdepth.
Furthermorem the crawl is able to create a much better balancing over
all hosts which is fair to all hosts that are in the queue.
This process will create a very large number of files for wide crawls in
the QUEUES folder: for each host a directory, for each crawl depth a
file inside the directory. A crawl with maxdepth = 4 will be able to
create 10.000s of files. To be able to use that many file readers, it
was necessary to implement a new index data structure which opens the
file only if an access is wanted (OnDemandOpenFileIndex). The usage of
such on-demand file reader shall prevent that the number of file
pointers is over the system limit, which is usually about 10.000 open
files. Some parts of YaCy had to be adopted to handle the crawl depth
number correctly. The logging and the IndexCreateQueues servlet had to
be adopted to show the crawl queues differently, because the host name
is attached to the port on the host to differentiate between http,
https, and ftp services.
- added order option to solr queries to be able to retrieve document
lists in specific order, here: link length
- added HyperlinkEdge class which manages the link structure
- integrated the HyperlinkEdge class into clickdepth computation
- extended the linkstructure.json servlet to show also the clickdepth
and other statistic information
instead of TreeMaps)
- enhanced memory footprint of database indexes (by introduction of
optimize calls)
- optimize calls shrink the amount of used memory for index sets if they
are not changed afterwards any more
different from normal requests. This happens if the remote solr is
actually a solrCloud; in such cases the luke request returns only the
result of the single solr peer, not the whole cloud.
also done: some refactoring.
url along with the load date. While this takes much more memory, it
eliminates database lookups for getURL() requests, which happen equally
often. This speeds up remote solr configurations.
this hack is the occurrence of Exceptions like:
W 2014/02/11 18:51:33 ConcurrentLog GC overhead limit exceeded
java.io.IOException: GC overhead limit exceeded
at
net.yacy.cora.federate.solr.connector.AbstractSolrConnector.getDocumentById(AbstractSolrConnector.java:334)
at
net.yacy.cora.federate.solr.connector.MirrorSolrConnector.getDocumentById(MirrorSolrConnector.java:173)
at
net.yacy.cora.federate.solr.connector.ConcurrentUpdateSolrConnector.getDocumentById(ConcurrentUpdateSolrConnector.java:415)
at net.yacy.search.index.Fulltext.getMetadata(Fulltext.java:331)
at net.yacy.search.index.Fulltext.getMetadata(Fulltext.java:317)
at
net.yacy.search.query.SearchEvent.pullOneRWI(SearchEvent.java:1024)
at
net.yacy.search.query.SearchEvent.pullOneFilteredFromRWI(SearchEvent.java:1047)
at
net.yacy.search.query.SearchEvent$3.run(SearchEvent.java:1263)
Caused by: java.lang.OutOfMemoryError: GC overhead limit exceeded
at java.util.Arrays.copyOfRange(Arrays.java:3077)
at java.lang.StringCoding.decode(StringCoding.java:196)
at java.lang.String.<init>(String.java:491)
at java.lang.String.<init>(String.java:547)
at
org.apache.lucene.codecs.compressing.CompressingStoredFieldsReader.readField(CompressingStoredFieldsReader.java:187)
at
org.apache.lucene.codecs.compressing.CompressingStoredFieldsReader.visitDocument(CompressingStoredFieldsReader.java:351)
at
org.apache.lucene.index.SegmentReader.document(SegmentReader.java:276)
at
org.apache.lucene.index.BaseCompositeReader.document(BaseCompositeReader.java:110)
at
org.apache.lucene.index.IndexReader.document(IndexReader.java:436)
at
org.apache.solr.search.SolrIndexSearcher.doc(SolrIndexSearcher.java:657)
at
net.yacy.cora.federate.solr.connector.EmbeddedSolrConnector.SolrQueryResponse2SolrDocumentList(EmbeddedSolrConnector.java:230)
at
net.yacy.cora.federate.solr.connector.EmbeddedSolrConnector.getDocumentListByParams(EmbeddedSolrConnector.java:320)
at
net.yacy.cora.federate.solr.connector.AbstractSolrConnector.getDocumentById(AbstractSolrConnector.java:330)
... 7 more
This problem was analysed with the Eclipse Memory Analyser after a heap
dump, where the following problem was reported as the main Problem
Suspect:
One instance of "org.apache.solr.util.ConcurrentLRUCache" loaded by
"sun.misc.Launcher$AppClassLoader @ 0x42e940a0" occupies 902.898.256
(61,80%) bytes. The memory is accumulated in one instance of
"java.util.concurrent.ConcurrentHashMap$Segment[]" loaded by "<system
class loader>".
This memory is part of the result cache of Solr. Flushing this cache
appears the most appropriate solution to that problem.
introduced, it was also used for search facets. The generic search
facets are now deduced from generic solr fields which makes jena as tool
for facet semantics superfluous.
Michael Peter Christen