see freshly deprecated https://developers.google.com/webmasters/ajax-crawling/
Implementation improves parsing of the homepage (ajax page) which uses metatag "fragment" in header and parses supplied html snapshot instead of mostly empty ajax/scripted page.
Implementation supports also hash-bang urls (url with anchor starting with ! like ...path#!hashfragment) but our crawler filters it
(use of hash-bang is controversly discussed and proposal is deprecated, makes no sense to adjust the crawler, but as long as it is used by some sites the minor change/improvement in htmlparser is good for some time).
Quick - how does it work
- if metatag fragment with content "!" is found
- htmlparser tries to get content of htmls snapshot (using a different url)
- htmlparser returns 2 documents (original url and snapshot content - but using same original url)
- after parsing result documents are joined (and stored to index containing content also from snapshot page... as the original ajax page contains typically no parseable html content)
Reads document level included title and description and skips the graphic content to save bandwidth.
svg metadata element is not interpreted
- remove rdfParser from init (current function identical with genericParser)
to prevent blank thumbnail display in image search because of not handled source which don't load on click.
Now the cross icon indicates the problem (inlcuding not supported format)
bayesian filters. This can be used to classify documents during
indexing-time using a pre-definied bayesian filter.
New wordings:
- a context is a class where different categories are possible. The
context name is equal to a facet name.
- a category is a facet type within a facet navigation. Each context
must have several categories, at least one custom name (things you want
to discover) and one with the exact name "negative".
To use this, you must do:
- for each context, you must create a directory within
DATA/CLASSIFICATION with the name of the context (the facet name)
- within each context directory, you must create text files with one
document each per line for every categroy. One of these categories MUST
have the name 'negative.txt'.
Then, each new document is classified to match within one of the given
categories for each context.
during surrogate reading: those attributes from the dump are removed
during the import process and replaced by new detected attributes
according to the setting of the YaCy peer.
This may cause that all such attributes are removed if the importing
peer has no synonyms and/or no vocabularies defined.
reason: experimental implementatin of RDFa parser not executed (limited to special urls) but may cause error on normal html parsing due to a inputstream.reset
keeping surrogates after processing is essential for some users. If the
space they are taking is too high, please set up an automatic deletion
process (like a cronjob).
to support the new time parser and search functions in YaCy a high
precision detection of date and time on the day is necessary. That
requires that the time zone of the document content and the time zone of
the user, doing a search, is detected. The time zone of the search
request is done automatically using the browsers time zone offset which
is delivered to the search request automatically and invisible to the
user. The time zone for the content of web pages cannot be detected
automatically and must be an attribute of crawl starts. The advanced
crawl start now provides an input field to set the time zone in minutes
as an offset number. All parsers must get a time zone offset passed, so
this required the change of the parser java api. A lot of other changes
had been made which corrects the wrong handling of dates in YaCy which
was to add a correction based on the time zone of the server. Now no
correction is added and all dates in YaCy are UTC/GMT time zone, a
normalized time zone for all peers.
- date navigation
The date is taken from the CONTENT of the documents / web pages, NOT
from a date submitted in the context of metadata (i.e. http header or
html head form). This makes it possible to search for documents in the
future, i.e. when documents contain event descriptions for future
events.
The date is written to an index field which is now enabled by default.
All documents are scanned for contained date mentions.
To visualize the dates for a specific search results, a histogram
showing the number of documents for each day is displayed. To render
these histograms the morris.js library is used. Morris.js requires also
raphael.js which is now also integrated in YaCy.
The histogram is now also displayed in the index browser by default.
To select a specific range from a search result, the following modifiers
had been introduced:
from:<date>
to:<date>
These modifiers can be used separately (i.e. only 'from' or only 'to')
to describe an open interval or combined to have a closed interval. Both
dates are inclusive. To select a specific single date only, use the
'to:' - modifier.
The histogram shows blue and green lines; the green lines denot weekend
days (saturday and sunday).
Clicking on bars in the histogram has the following reaction:
1st click: add a from:<date> modifier for the date of the bar
2nd click: add a to:<date> modifier for the date of the bar
3rd click: remove from and date modifier and set a on:<date> for the bar
When the on:<date> modifier is used, the histogram shows an unlimited
time period. This makes it possible to click again (4th click) which is
then interpreted as a 1st click again (sets a from modifier).
The display feature is NOT switched on by default; to switch it on use
the /ConfigSearchPage_p.html servlet.
given css class and extends a given vocabulary with a term consisting
with the text content of the html class tag. Additionally, the term is
included into the semantic facet of the document. This allows the
creation of faceted search to documents without the pre-creation of
vocabularies; instead, the vocabulary is created on-the-fly, possibly
for use in other crawls. If any of the term scraping for a specific
vocabulary is successful on a document, this vocabulary is excluded for
auto-annotation on the page.
To use this feature, do the following:
- create a vocabulary on /Vocabulary_p.html (if not existent)
- in /CrawlStartExpert.html you will now see the vocabularies as column
in a table. The second column provides text fields where you can name
the class of html entities where the literal of the corresponding
vocabulary shall be scraped out
- when doing a search, you will see the content of the scraped fields in
a navigation facet for the given vocabulary
trace: java.lang.OutOfMemoryError: Java heap space
at java.awt.image.DataBufferInt.<init>(DataBufferInt.java:75)
at java.awt.image.Raster.createPackedRaster(Raster.java:467)
at java.awt.image.DirectColorModel.createCompatibleWritableRaster(DirectColorModel.java:1032)
at java.awt.image.BufferedImage.<init>(BufferedImage.java:331)
at net.yacy.document.parser.images.bmpParser$IMAGEMAP.<init>(bmpParser.java:149)
at net.yacy.document.parser.images.bmpParser.parse(bmpParser.java:69)
at net.yacy.document.parser.images.genericImageParser.parse(genericImageParser.java:116)
parsing into individual pages and add them all using different URLs.
These constructed urls are generated from the source url with an
appended page=<pagenumber> attribute to the url get/post properties.
This will distinguish the different page entries. The search result list
will then replace the post parameter with a url anchor # mark which
causes that the original url is presented in the search result. These
URLs can be opened directly on the correct page using pdf.js which is
now built-in into firefox. That means: if you find a search hit on page
5 and click on the search result, firefox will open the pdf viewer and
shows page 5.
occurrences within the (web) page documents (not the document
last-modified!). This works only if the solr field dates_in_content_sxt
is enabled. A search request may then have the form "term on:<date>",
like
gift on:24.12.2014
gift on:2014/12/24
* on:2014/12/31
For the date format you may use any kind of human-readable date
representation(!yes!) - the on:<date> parser tries to identify language
and also knows event names, like:
bunny on:eastern
.. as long as the date term has no spaces inside (use a dot). Further
enhancement will be made to accept also strings encapsulated with
quotes.
notions within the fulltext of a document. This class attempts to
identify also dates given abbreviated or with missing year or described
with names for special days, like 'Halloween'. In case that a date has
no year given, the current year and following years are considered.
This process is therefore able to identify a large set of dates to a
document, either because there are several dates given in the document
or the date is ambiguous. Four new Solr fields are used to store the
parsing result:
dates_in_content_sxt:
if date expressions can be found in the content, these dates are listed
here in order of the appearances
dates_in_content_count_i:
the number of entries in dates_in_content_sxt
date_in_content_min_dt:
if dates_in_content_sxt is filled, this contains the oldest date from
the list of available dates
#date_in_content_max_dt:
if dates_in_content_sxt is filled, this contains the youngest date from
the list of available dates, that may also be possibly in the future
These fields are deactiviated by default because the evaluation of
regular expressions to detect the date is yet too CPU intensive. Maybe
future enhancements will cause that this is switched on by default.
The purpose of these fields is the creation of calendar-like search
facets, to be implemented next.
- snapshots can now also be xml files which are extracted from the solr
index and stored as individual xml files in the snapshot directory along
the pdf and jpg images
- a transaction layer was placed above of the snapshot directory to
distinguish snapshots into 'inventory' and 'archive'. This may be used
to do transactions of index fragments using archived solr search results
between peers. This is currently unfinished, we need a protocol to move
snapshots from inventory to archive
- the SNAPSHOT directory was renamed to snapshot and contains now two
snapshot subdirectories: inventory and archive
- snapshots may now be generated by everyone, not only such peers
running on a server with xkhtml2pdf installed. The expert crawl starts
provides the option for snapshots to everyone. PDF snapshots are now
optional and the option is only shown if xkhtml2pdf is installed.
- the snapshot api now provides the request for historised xml files,
i.e. call:
http://localhost:8090/api/snapshot.xml?urlhash=Q3dQopFh1hyQ
The result of such xml files is identical with solr search results with
only one hit.
The pdf generation has been moved from the http loading process to the
solr document storage process. This may slow down the process a lot and
a different version of the process may be needed.
thread pools will flush their cached (dead) threads after 60 seconds.
This will cause that YaCy now runs constantly withl about 50 threads,
about 100 at peak times. Previously, about 400 threads had been cached
and kept in a hibernation state, which caused that the numproc counter
in /proc/user_beancounters (exists only in VM-hosted linux) was as high
as the cached number of threads. This caused that VM supervisors
terminated whole VM sessions if a limit was reached. Many VM providers
have limits of numproc=96 which made it virtually impossible to run YaCy
on such machines. With this change, it will be possible to run many YaCy
instances even on VM hosts.
this extracts clickable links in pdf and adds it to the list of links
include a test case for this function
this is the corrected comment for commit:
aa2e15d846
tested with IE11 and Firefox 32 (change worked for both to show 2nd line without cutting off height)
+fix charset parameter in metadataImageParser
+update start errMsgTxt to "java 1.7"
This is a modified genericImageParser adding tif (and psd) support even if java ImageIO plugin for tif is not installed in JDK.
Adds just tif and psd to the available parsers.
Uses the same library to extract metadata, so could eventually be merged with genericImageParser.
All detected metadata are added to the parsed document (potentially some more as with genericImageParser)
genericImageParser uses javax ImageIO, supported images depend on available plugins for ImageIO package (this is JDK installation specific). Jpeg, png and gif are availabel by default. Tif and others only on avalable plugin (in classpath).
Add supported image type dynamically on startup.
the parser initialization. To make the apk parser usable, the handling
of application type links had to be modified. Now all documents which
have not a parser attached are placed to the noload-queue while all
other documents are parsed using the associated parser class. This may
have side-Effects on other parsers and the display of different file
classes (images, apps, videos).
- type detection (rss/atom)
- init type parameter overwritten during parse, parameter obsolete
- detection by endtag changed to simpler first-tag evaluation
- channel image not used, removed related extra parser handling
- remove unused code (set/getImage) in rssfeed
- atom link extraction to account for possible multipe link tags
- spec limits link to one with rel="alternate" or one without rel attribute
not accounting for the follwing type & hreflang exception yet:
o atom:entry elements MUST NOT contain more than one atom:link
element with a rel attribute value of "alternate" that has the
same combination of type and hreflang attribute values.
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.
generic parser but extracts links like the htmlParser. This should be
used for ASCII documents without known text format annotation like
source code files or json documents. Probably also good for xml files
without known schema.
