I have a query that uses phrase search to match whole phrases.
SELECT ts_headline(
'simple',
'This is my test text. My test text has many words. Well, not THAT many words.',
phraseto_tsquery('simple', 'text has many words')
);
Which results in:
This is my test <b>text</b>. My test <b>text</b> <b>has</b> <b>many</b> <b>words</b>. Well, not THAT <b>many</b> <b>words</b>.
But I would have expected this:
This is my test text. My test <b>text</b> <b>has</b> <b>many</b> <b>words</b>. Well, not THAT many words.
Or ideally even this:
This is my test text. My test <b>text has many words</b>. Well, not THAT many words.
Sidenote:
phraseto_tsquery('simple', 'text has many words')
is equivalent to
to_tsquery('simple', 'text <-> has <-> many <-> words')
I'm not sure if I'm doing something wrong, or if ts_headline simply does not support this kind of highlighting.
phraseto_tsquery('simple', 'text has many words') generates correct query but it seems the problem is in ts_headline function. Seems like an already reported BUG #155172.
Related
The Full text search of postgres includes some of these functions to search: plainto_tsquery, to_tsquery and to_tsvector .
I don't get the difference between it, the results contain the same words always, but in tsvector it is detached with the number of position of that word.
SELECT plainto_tsquery('simple', 'The & Fat & Rats');
result will be like this:
plainto_tsquery: 'fat' & 'rat'
to_tsquery: 'fat' & 'rat'
to_tsvector: 'fat':2 'rat':3
I have tried longer queries, but i haven't found a bigger difference than that.
I already read the documentation, but I didnt get the difference there either.
I am happy for any help.
"plainto_tsquery" takes a phrase in plain English (or in this case plain "simple"--although your question is not consistent. "simple" does not strip out the word 'the', the way you show, unless you made nonstandard modifications to it) and converts it to a tsquery. Since "&" is punctuation, it gets ignored. But then it adds '&' in between the words, because that is what "plainto_tsquery" does. So those changes are not visible, because you chose a poor example to feed to plainto_tsquery.
"to_tsquery" compiles the query you gave it into the structure used for searching. But then, because you are selecting it rather than using it with a ts query operator, it converts it back to text again so it can display it. It requires that what you feed it already looks mostly like a tsquery (for example, has boolean operators between each word), otherwise it throws an error. Surely you noticed that when you tried longer queries?
"to_tsvector" creates a tsvector. This is not a tsquery, rather it is what the tsquery gets applied to.
I was experimenting with PostgreSQL's text search feature - particularly with the normalization function to_tsquery.
I was using english dictionary(config) and for some reason s and t won't normalize. I understand why i and a would not, but s and t? Interesting.
Are they matched to single space and tab?
Here is the query:
select
to_tsquery('english', 'a:*') as for_a,
to_tsquery('english', 's:*') as for_s,
to_tsquery('english', 't:*') as for_t,
to_tsquery('english', 'u:*') as for_u
fiddle just in case.
You would see 'u:*' is returning as 'u:*' and 'a:*' is not returning anything.
The letters s and t are considered stop words in the english text search dictionary, therefore they get discarded. You can read the stop word list under tsearch_data/english.stop in the postgres shared folder, which you can locate by typing pg_config --sharedir
With pg 11 on ubuntu/debian/mint, that would be
cat /usr/share/postgresql/11/tsearch_data/english.stop
Quoting from the docs,
Stop words are words that are very common, appear in almost every document, and have no discrimination value. Therefore, they can be ignored in the context of full text searching.
It is best to discard english grammar and think of words in a programmatic and logical way as described above. Full text search does not try to infer context based on sentence structuring so it has no use for these words. After all, it's called full text search and not natural language search.
As to how they arrived on the conclusion to add s and t to the stop word list, statistical analysis must have revealed these characters to be noise.
I'm trying to use Sphinx to find rows having words in their title column.
The query looks like this:
SELECT * FROM my_table WHERE MATCH ('#title "words"')
But it also returns rows having word (without the s) instead of words in the title.
What am I doing wrong?
Sounds like you have morphology (specifically stemming?) enabled on the index.
Should consider enabling index_exact_words
http://sphinxsearch.com/docs/current.html#conf-index-exact-words
which gives you exact form operator.
MATCH('#title =words')
Also gives you the possibility of the interesting expand_keywords option :)
http://sphinxsearch.com/docs/current.html#conf-expand-keywords
... or if dont ever want these matches, could disable stemming :) Alas there isn't a 'stemming optional' mode. (eg a ~ fuzzy operator to specifically stem)
I call my statement with CONTAINS function, but sometimes it does not return correct records, e.g. I want to return row which contain in one field word 'Your':
SELECT [Email]
,[Comment]
FROM [USERS]
WHERE CONTAINS(Comment, 'Your')
It gives mi 0 result despite that this field contains this word (the same with 'as', 'to', 'was', 'me'). When I use 'given' instead of 'Your' then I receive a result. Is there maybe a list of words which cannot be used with CONTAINS? Or maybe this words are to short (when i use 'name' then i receive the results)? The work 'Your' is at the beginning in field Comment.
The field is of type 'text' and has enabled full-text index.
Words such as those you mention are "stop words"; they are expressly excluded from being indexed and searched in Full Text Search due to how common (and thereby meaningless for searches) they are. You'll notice the same thing when searching Google, for instance.
It is possible to edit the list, but I would avoid doing so except perhaps to add words to it; the words in the list are chosen very well, IMHO, for their lack of utility in searches.
I have an app that utilizes hashtags to help tag posts. I am trying to have a more detailed search.
Lets say one of the records I'm searching is:
The #bird flew very far.
When I search for "flew", "fle", or "#bird", it should return the record.
However, when I search "#bir", it should NOT return the sentence because the whole the tag being searched for doesn't match.
I'm also not sure if "bird" should even return the sentence. I'd be interested how to do that though as well.
Right now, I have a very basic search:
SELECT "posts".* FROM "posts" WHERE (body LIKE '%search%')
Any ideas?
You could do this with LIKE but it would be rather hideous, regexes will serve you better here. If you want to ignore the hashes then a simple search like this will do the trick:
WHERE body ~ E'\\mbird\M''
That would find 'The bird flew very far.' and 'The #bird flew very far.'. You'd want to strip off any #s before search though as this:
WHERE body ~ E'\\m#bird\M''
wouldn't find either of those results due to the nature of \m and \M.
If you don't want to ignore #s in body then you'd have to expand and modify the \m and \M shortcuts yourself with something like this:
WHERE body ~ E'(^|[^\\w#])#bird($|[^\\w#])'
-- search term goes here^^^^^
Using E'(^|[^\\w#])#bird($|[^\\w#])' would find 'The #bird flew very far.' but not 'The bird flew very far.' whereas E'(^|[^\\w#])bird($|[^\\w#])' would find 'The bird flew very far.' but not 'The #bird flew very far.'. You might also want to look at \A instead of ^ and \Z instead of $ as there are subtle differences but I think $ and ^ would be what you want.
You should keep in mind that none of these regex searches (or your LIKE search for that matter) will uses indexes so you're setting yourself up for lots of table scans and performance problems unless you can restrict the searches using something that will use an index. You might want to look at a full-text search solution instead.
It might help to parse the hash tags out of the text and store them in an array in a separate column called say hashtags when the articles are inserted/updated. Remove them from the article body before feeding it into to_tsvector and store the tsvector in a column of the table. Then use:
WHERE body_tsvector ## to_tsquery('search') OR 'search' IN hashtags
You could use a trigger on the table to maintain the hashtags column and the body_tsvector stripped of hash tags, so that the application doesn't have to do the work. Parse them out of the text when entries are INSERTed or UPDATEd.