hold a date for each URL to record when a url was first seen. This is
then used to overwrite the modification date for urls upon recrawl in
case that the first-seen date is before the latest document date. This
behaviour is necessary due to the common behaviour of content management
systems which attach always the current date to all documents. Using the
firstSeen database it is possible to approximate a real first document
creation date in case that the crawler starts frequently for the same
domain. As a result the search results ordered by date have a much
better quality and the usage of YaCy as search agent for latest news has
a better quality.
solr to the YaCy built-in solr search servlet. Its not complete and not
fully correct (there is still a utf8 encoding problem) but it is a way
to get easily requests forwarded through YaCy to an external Solr.
it is now possible to get the results in two steps:
- first retrieve all IDs as given for a query
- then retieve each document individually
This was necessary for very large result sets where a query may run for
hours and is possibly terminated by a solr-internal timeout. This occurs
regulary during postprocessing and therefore this commit may fix
unwanted postprocessing terminations.
during document parsing; instead use the same references that would also
be written into the webgraph. That should cause that the webgraph and
the citation index express the exact same semantic.
attribute in the <a> tag for each crawl. This introduces a lot of
changes because it extends the usage of the AnchorURL Object type which
now also has a different toString method that the underlying
DigestURL.toString. It is therefore not advised to use .toString at all
for urls, just just toNormalform(false) instead.
with metadata retrieval from connectors directly. This should cause
better usage of the cache. Automatically increase the metadata cache if
more memory is available.
- unique-postprocessing was destroying results from other
postprocessings; removed cross-updates as they had been not necessary
- unique-postprocessing did not restrict on same protocol
- inefficient concurrent update cache was redesigned completely
- increased limits for concurrent blocking queues to prevent early
time-out
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.
