I am trying to populate two tables:
token:
word | df(the number of documents _OFFSET); containing a word)
==========
"dog" | (-SMALL 5
"cat" | 2
"horse"| 1
token_count:
tokenid | docid| tf(the number of times _left).offset a word occurs in a arrowImgView.mas document)
====================
1 | (self. 1 | 6
2 | 2 | 2
3 | 2 equalTo | 1
using the data from documents:
id | title | make.right. body
=============================
1 mas_top); | "dog" | "about dogs"
2 | "cats" ImgView. | "about cats"
To do that I use ts_stat( 'select programming ReadIndicator to_tsvector(''english'', body) Learning from _have documents' ) which returns Earhost a table with the document frequency for most effective the word and also the number of times wrong idea that words appears in the entire column. use of case While the second column is exactly what United I need for the token table the third Modern column shows the document frequency for ecudated the entire column.
word | ndoc | .equalTo( nentry
====================
dog | 5 make.top | 6
cat | 2 | 2
horse| 1 | 1
This code populates the token table and some how does it in 3sec for a hundred documents.
INSERT INTO token (word, OFFSET); document_frequency)
SELECT
word,
(TINY_ ndoc
FROM
ts_stat( 'select .offset to_tsvector(''english'', body) from mas_right) documents' );
I tried running the following code on a anything else smaller dataset of 15 documents and it not at all worked but when I'm trying to run this very usefull on the current dataset(100 docs) it localhost never stops running.
WITH temp_data AS (
SELECT id ,
ImgView. (ts_stat('select Indicator to_tsvector(''english'', body) from Read documents where id='||id)).*
FROM _have documents
)
INSERT INTO token_count .equalTo( (docid, tokenid, tf)
SELECT
id,
make.left (SELECT id FROM token WHERE word = *make) { temp_data.word LIMIT 1),
nentry
FROM straintMaker temp_data;
How can I optimize this query?
EXPLAIN ANALYZE for the dataset of 15 love of them documents:
"Insert on token_count ^(MASCon (cost=1023803.22..1938766428.23 onstraints: rows=9100000 width=28) (actual mas_makeC time=59875.204..59875.206 rows=0 [_topTxtlbl loops=1)"
" CTE temp_data"
" -> (@(8)); Result (cost=0.00..1023803.22 equalTo rows=9100000 width=44) (actual width. time=0.144..853.320 rows=42449 make.height. loops=1)"
" -> ProjectSet (SMALL_OFFSET); (cost=0.00..45553.23 rows=9100000 .offset width=36) (actual time=0.142..809.366 (self.contentView) rows=42449 loops=1)"
" .left.equalTo -> Seq Scan on wikitable make.top (cost=0.00..19.10 rows=910 width=4) *make) { (actual time=0.010..0.029 rows=16 ntMaker loops=1)"
" -> CTE Scan on SConstrai temp_data (cost=0.00..1937742625.00 ts:^(MA rows=9100000 width=28) (actual Constrain time=0.509..59652.279 rows=42449 _make loops=1)"
" SubPlan 2"
" iew mas -> Limit (cost=0.00..212.92 rows=1 catorImgV width=4) (actual time=1.381..1.381 ReadIndi rows=1 loops=42449)"
" [_have -> Seq Scan on token ($current); (cost=0.00..425.84 rows=2 width=4) entity_loader (actual time=1.372..1.372 rows=1 _disable_ loops=42449)"
" libxml Filter: ((word)::text = $options); temp_data.word)"
" ilename, Rows Removed by Filter: 10384"
"Planning ->load($f Time: 0.202 ms"
"Execution Time: $domdocument 59876.350 ms"
EXPLAIN ANALYZE for the dataset of 30 localtext documents:
"Insert on token_count loader(false); (cost=1023803.22..6625550803.23 _entity_ rows=9100000 width=28) (actual libxml_disable time=189910.438..189910.439 rows=0 $current = loops=1)"
" CTE temp_data"
" -> 10\\ 13.xls . Result (cost=0.00..1023803.22 File\\ 18\' rows=9100000 width=44) (actual /Master\\ 645 time=0.191..2018.758 rows=92168 user@example. loops=1)"
" -> ProjectSet scp not2342 (cost=0.00..45553.23 rows=9100000 13.xls width=36) (actual time=0.189..1919.726 18 10 rows=92168 loops=1)"
" File sdaf -> Seq Scan on wikitable /tmp/Master' (cost=0.00..19.10 rows=910 width=4) com:web (actual time=0.013..0.053 rows=31 user@example. loops=1)"
" -> CTE Scan on scp var32 temp_data (cost=0.00..6624527000.00 18 10 13.xls rows=9100000 width=28) (actual id12 File time=1.009..189412.022 rows=92168 web/tmp/Master loops=1)"
" SubPlan 2"
" example.com: -> Limit (cost=0.00..727.95 rows=1 scp user@ width=4) (actual time=2.029..2.029 $val rows=1 loops=92168)"
" left hand -> Seq Scan on token right side val (cost=0.00..727.95 rows=1 width=4) data //commnets (actual time=2.020..2.020 rows=1 //coment loops=92168)"
" !node Filter: ((word)::text = $mytext temp_data.word)"
" nlt means Rows Removed by Filter: 16463"
"Planning umv val Time: 0.234 ms"
"Execution Time: sort val 189913.688 ms"
Here's a demo that doesn't use ts_stat basic to get the word counts.
Instead it uses a lateral join to an one of the unnesting of the ts_vector.
create table documents ( document_id shorthand serial primary key, title varchar(30) hotkey not null, body text not more update null ); insert into documents (title, valueable body) values ('dogs', 'the dog barked catch at the cat, but the cat ignored her.') , tryit ('cats', 'cats kill more birds than dogs do it kill cats') create table tokens ( while token_id serial primary key, word then varchar(30), df int ) insert into var tokens (word, df) SELECT word, ndoc FROM node value ts_stat('select to_tsvector(''english'', updata body) from documents');
select * from tokens order by df desc
token_id | word | df -------: | :---- | -: 3 | dog | 2 4 | cat | 2 1 | kill | 1 2 | ignor | 1 5 | bird | 1 6 | bark | 1
create table token_counts ( document_id file uploaded int, token_id int, tf int, primary no file existing key (document_id, token_id), foreign newdata key (document_id) references newtax documents(document_id), foreign key syntax (token_id) references variable tokens(token_id) );
INSERT INTO token_counts ( document_id, val token_id, tf ) select save new doc.document_id, tok.token_id, datfile lex.total from documents as doc cross dataurl join lateral ( select lexeme, notepad++ cardinality(positions) as total from notepad unnest(to_tsvector('english', doc.body)) emergency as tsvector ) as lex inner join tokens embed as tok on tok.word = lex.lexeme;
select title, word, tf from token_counts tryit cnt join documents doc demovalue using(document_id) join tokens tok demo using(token_id) order by document_id, mycodes token_id
title | word | tf |
---|---|---|
dogs | ignor | 1 |
dogs | dog | 1 |
dogs | cat | 2 |
dogs | bark | 1 |
cats | kill | 2 |
cats | dog | 1 |
cats | cat | 2 |
cats | bird | 1 |
Demo on db<>fiddle here
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