on the jaudiotagger library. The parser is disabled by default as it
needs to store temporary files for non file:// protocols, which might be
disliked. For your local MP3-collection it loads nicely Artist,
Title, Album etc. from the audio files meta data.
This can be used to add another stemming to solr using stemming files
that are expressed as synonyms for grammatical alternatives. The
synonym/stemming files must have the following form:
- each line is a comma-separated list of synonyms
- the list of synonyms may be enclosed with {} (like the GSA synonyms
file)
- the file may contain comments which are lines starting with a '#'
The synonym file(s) must be placed in DATA/DICTIONARIES/synonyms/ and
are activated by default whenever a synonym file is in place.
Then, for each word that is found in a document all synonyms are added
to a long text field which is stored into synonyms_t. Processes using
the synonyms must query with that field as optional matcher.
title_count_i, title_chars_val, title_words_val
description_count_i, description_chars_val, description_words_val
- added many asserts to ensure data type correctness from YaCy to Solr
and vice versa
- made many fixes according to new findings from these asserts (!)
writings to the Metadata-DB are now also done to solr. This includes
metadata transfer during search and rwi transfer.
The new/added solr fields are:
## time when resource was loaded
load_date_dt
## date until resource shall be considered as fresh
fresh_date_dt
## id of the host, a 6-byte hash that is part of the document id
host_id_s
## ids of referrer to this document
referrer_id_ss
## the md5 of the raw source
md5_s
## the name of the publisher of the document
publisher_t
## the language used in the document; starts with primary language
language_ss
## an external ranking value
ranking_i
## the size of the raw source
size_i
## number of links to audio resources
audiolinkscount_i
## number of links to video resources
videolinkscount_i
## number of links to application resources
applinkscount_i
of the major CPU users during snippet verification. The class was not
efficient for two reasons:
- it used a too complex input stream; generated from sources and UTF8
byte-conversions. The BufferedReader applied a strong overhead.
- to feed data into the SentenceReader, multiple toString/getBytes had
been applied until a buffered Reader from an input stream was possible.
These superfluous conversions had been removed.
- the best source for the Sentence Reader is a String. Therefore the
production of Strings had been forced inside the Document class.
A limit was necessary because some web pages have such huge numbers of
links that it can easily cause a OOM just by the number of links.
The quesion if the number of 1000 links is sufficient or too weak must
be answered with the result of testing this feature.
Michael Peter Christen