Given a table name table and a string column named column, I want to search for the word word in that column in the following way: exact matches be on top, followed by prefix matches and finally postfix matches.
Currently I got the following solutions:
Solution 1:
select column
from (select column,
case
when column like 'word' then 1
when column like 'word%' then 2
when column like '%word' then 3
end as rank
from table) as ranked
where rank is not null
order by rank;
Solution 2:
select column
from table
where column like 'word'
or column like 'word%'
or column like '%word'
order by case
when column like 'word' then 1
when column like 'word%' then 2
when column like '%word' then 3
end;
Now my question is which one of the two solutions are more efficient or better yet, is there a solution better than both of them?
Your 2nd solution looks simpler for the planner to optimize, but it is possible that the first one gets the same plan as well.
For the Where, is not needed as it is covered by ; it might confuse the DB to do 2 checks instead of one.
But the biggest problem is the third one as this has no way to be optimized by an index.
So either way, PostgreSQL is going to scan your full table and manually extract the matches. This is going to be slow for 20,000 rows or more.
I recommend you to explore fuzzy string matching and full text search; looks like that is what you're trying to emulate.
Even if you don't want the full power of FTS or fuzzy string matching, you definitely should add the extension "pgtrgm", as it will enable you to add a GIN index on the column that will speedup LIKE '%word' searches.
https://www.postgresql.org/docs/current/pgtrgm.html
And seriously, have a look to FTS. It does provide ranking. If your requirements are strict to what you described, you can still perform the FTS query to "prefilter" and then apply this logic afterwards.
There are tons of introduction articles to PostgreSQL FTS, here's one:
https://www.compose.com/articles/mastering-postgresql-tools-full-text-search-and-phrase-search/
And even I wrote a post recently when I added FTS search to my site:
https://deavid.wordpress.com/2019/05/28/sedice-adding-fts-with-postgresql-was-really-easy/
Related
I have a big int mobile_number column in my users table, I want to find users for a specific operator
My current query is so slow and I have no idea how can I add an index to make it faster, I am looking for an index or alternative faster solution
SELECT mobile_number
FROM users
WHERE mobile_number::TEXT~'^98(?:901|902|903|904|905|930|933|935|936|937|938|939)'
How can I make a start with regex faster? I tried gin_trgm_ops but not works
Please ignore the following solutions:
Adding a pre calculated operator column
Comparing numbers mobile_number>= 989010000000 AND mobile_number<= 989019999999
I am trying to figure out what would be the best way to go ahead and locate duplicates in a 5 column csv data. The real data has more than million rows in it.
Following is the content of mentioned 6 columns.
Name, address, city, post-code, phone number, machine number
Data does not have fixed length, data might in certain columns might be missing in certain instances.
I am thinking of using perl to first normalize all the short forms used in names, city and address. Fellow perl enthusiasts from stackoverflow have helped me a lot.
But there would still be a lot of data which would be difficult to match.
So I am wondering is it possible to match content based on "LIKELINESS / SIMILARITY" (eg. google similar to gugl) the likeliness would be required to overcome errors that creeped in while collecting data.
I have 2 tasks in hand w.r.t. the data.
Flag duplicate rows with certain identifier
Mention the percentage match between similar rows.
I would really appreciate if I could get suggestions as to what all possible methods could be employed and which would propbably be best because of their certain merits.
You could write a Perl program to do this, but it will be easier and faster to put it into a SQL database and use that.
Most SQL databases have a way to import CSV. For this answer, I suggest PostgreSQL because it has very powerful string functions which you will need to find your fuzzy duplicates. Create your table with an auto incremented ID column if your CSV data doesn't already have unique IDs.
Once the import is done, add indexes on the columns you want to check for duplicates.
CREATE INDEX name ON whatever (name);
You can do a self-join to look for duplicates in whatever way you like. Here's an example that finds duplicate names.
SELECT id
FROM whatever t1
JOIN whatever t2 ON t1.id < t2.id
WHERE t1.name = t2.name
PostgreSQL has powerful string functions including regexes to do the comparisons.
Indexes will have a hard time working on things like lower(t1.name). Depending on the sorts of duplicates you want to work with, you can add indexes for these transforms (this is a feature of PostgreSQL). For example, if you wanted to search case insensitively you can add an index on the lower-case name. (Thanks #asjo for pointing that out)
CREATE INDEX ON whatever ((lower(name)));
// This will be muuuuuch faster
SELECT id
FROM whatever t1
JOIN whatever t2 ON t1.id < t2.id
WHERE lower(t1.name) = lower(t2.name)
A "likeness" match can be achieved in several ways, a simple one would be to use the fuzzystrmatch functions like metaphone(). Same trick as before, add a column with the transformed row and index it.
Other simple things like data normalization are better done on the data itself before adding indexes and looking for duplicates. For example, trim out and squish extra whitespace.
UPDATE whatever SET name = trim(both from name);
UPDATE whatever SET name = regexp_replace(name, '[[:space:]]+', ' ');
Finally, you can use the Postgres Trigram module to add fuzzy indexing to your table (thanks again to #asjo).
I have the following situation. In our database, our user has the ability to search part numbers as 'keywords'. Part numbers are attached as 'footnotes' which get attached to certain items. An example of a footnote of this nature would have a description of:
Part Number: 09C888
Our keyword search searches multiple tables through an incredibly fun set of LEFT JOINs eventually forming a ts_vector which then is used against a tsquery. Our current issue is that this methodology seems to only accept exact matches. Example:
select to_tsvector('Part Number: 09C888') ## to_tsquery('09C888:*');
?column?
---------
t
Using the full version of the part number as the search criteria works fine. However...
select to_tsvector('Part Number: 09C888') ## to_tsquery('9C888:*');
?column?
----------
f
Is there a way to modify the above tsquery item to match against 09C888 with values of 09C888 AND 9C888? Normally, I could do something similar with the LIKE construct, but we're currently using full text search for efficiency on large amounts of data. From perusing the postgresql documentation, I cannot figure out an easy way to do this. I am also hesitant to change the overall query since it's doing... well, its doing a lot of stuff of which the text matching is only one part of. (Obviously a potential place for improvement.)
EDIT:
I've actually figured out how to do this using a modified query
select to_tsvector('Part Number: 09C888') ## to_tsquery('09C888|9C888:*');
Is there a better way to determine match than what I've listed above? Mostly because the solution in incredibly specific, but essentially these part numbers may or may not have leading 0s.
Have you considered storing the part number with leading zeroes removed in a separate column and search against that?
+---------------------+-------+
| Part Number: 09C888 | 9C888 |
+---------------------+-------+
CREATE INDEX footnote_part_number_txt_idx
ON footnotes (stripped_part_number text_pattern_ops);
then you can query (using the index)
SELECT footnote_str
FROM footnotes
WHERE stripped_part_number LIKE '9C88%'
See: http://petereisentraut.blogspot.se/2009/10/rethink-your-text-column-indexing-with.html
I have a large postgres table of locations (shops, landmarks, etc.) which the user can search in various ways. When the user wants to do a search for the name of a place, the system currently does (assuming the search is on cafe):
lower(location_name) LIKE '%cafe%'
as part of the query. This is hugely inefficient. Prohibitively so. It is essential I make this faster. I've tried indexing the table on
gin(to_tsvector('simple', location_name))
and searching with
(to_tsvector('simple',location_name) ## to_tsquery('simple','cafe'))
which works beautifully, and cuts down the search time by a couple of orders of magnitude.
However, the location names can be in any language, including languages like Chinese, which aren't whitespace delimited. This new system is unable to find any Chinese locations, unless I search for the exact name, whereas the old system could find matches to partial names just fine.
So, my question is: Can I get this to work for all languages at once, or am I on the wrong track?
If you want to optimize arbitrary substring matches, one option is to use the pg_tgrm module. Add an index:
CREATE INDEX table_location_name_trigrams_key ON table
USING gin (location_name gin_trgm_ops);
This will break "Simple Cafe" into "sim", "imp", "mpl", etc., and add an entry to the index for each trigam in each row. The query planner can then automatically use this index for substring pattern matches, including:
SELECT * FROM table WHERE location_name ILIKE '%cafe%';
This query will look up "caf" and "afe" in the index, find the intersection, fetch those rows, then check each row against your pattern. (That last check is necessary since the intersection of "caf" and "afe" matches both "simple cafe" and "unsafe scaffolding", while "%cafe%" should only match one). The index becomes more effective as the input pattern gets longer since it can exclude more rows, but it's still not as efficient as indexing whole words, so don't expect a performance improvement over to_tsvector.
Catch is, trigrams don't work at all for patterns that under three characters. That may or may not be a deal-breaker for your application.
Edit: I initially added this as a comment.
I had another thought last night when I was mostly asleep. Make a cjk_chars function that takes an input string, regexp_matches the entire CJK Unicode ranges, and returns an array of any such characters or NULL if none. Add a GIN index on cjk_chars(location_name). Then query for:
WHERE CASE
WHEN cjk_chars('query') IS NOT NULL THEN
cjk_chars(location_name) #> cjk_chars('query')
AND location_name LIKE '%query%'
ELSE
<tsvector/trigrams>
END
Ta-da, unigrams!
For full text search in a multi-language environment you need to store the language each datum is in along side the text its self. You can then use the language-specified flavours of the tsearch functions to get proper stemming, etc.
eg given:
CREATE TABLE location(
location_name text,
location_name_language text
);
... plus any appropriate constraints, you might write:
CREATE INDEX location_name_ts_idx
USING gin(to_tsvector(location_name_language, location_name));
and for search:
SELECT to_tsvector(location_name_language,location_name) ## to_tsquery('english','cafe');
Cross-language searches will be problematic no matter what you do. In practice I'd use multiple matching strategies: I'd compare the search term to the tsvector of location_name in the simple configuration and the stored language of the text. I'd possibly also use a trigram based approach like willglynn suggests, then I'd unify the results for display, looking for common terms.
It's possible you may find Pg's fulltext search too limited, in which case you might want to check out something like Lucerne / Solr.
See:
* controlling full text search.
* tsearch dictionaries
Similar to what #willglynn already posted, I would consider the pg_trgm module. But preferably with a GiST index:
CREATE INDEX tbl_location_name_trgm_idx
USING gist(location_name gist_trgm_ops);
The gist_trgm_ops operator class ignore case generally, and ILIKE is just as fast as LIKE. Quoting the source code:
Caution: IGNORECASE macro means that trigrams are case-insensitive.
I use COLLATE "C" here - which is effectively no special collation (byte order instead), because you obviously have a mix of various collations in your column. Collation is relevant for ordering or ranges, for a basic similarity search, you can do without it. I would consider setting COLLATE "C" for your column to begin with.
This index would lend support to your first, simple form of the query:
SELECT * FROM tbl WHERE location_name ILIKE '%cafe%';
Very fast.
Retains capability to find partial matches.
Adds capability for fuzzy search.
Check out the % operator and set_limit().
GiST index is also very fast for queries with LIMIT n to select n "best" matches. You could add to the above query:
ORDER BY location_name <-> 'cafe'
LIMIT 20
Read more about the "distance" operator <-> in the manual here.
Or even:
SELECT *
FROM tbl
WHERE location_name ILIKE '%cafe%' -- exact partial match
OR location_name % 'cafe' -- fuzzy match
ORDER BY
(location_name ILIKE 'cafe%') DESC -- exact beginning first
,(location_name ILIKE '%cafe%') DESC -- exact partial match next
,(location_name <-> 'cafe') -- then "best" matches
,location_name -- break remaining ties (collation!)
LIMIT 20;
I use something like that in several applications for (to me) satisfactory results. Of course, it gets a bit slower with multiple features applied in combination. Find your sweet spot ...
You could go one step further and create a separate partial index for every language and use a matching collation for each:
CREATE INDEX location_name_trgm_idx
USING gist(location_name COLLATE "de_DE" gist_trgm_ops)
WHERE location_name_language = 'German';
-- repeat for each language
That would only be useful, if you only want results of a specific language per query and would be very fast in this case.
I've come across full text search in postgres in the last few days, and I am a little confused about indexing when searching across multiple columns.
The postgres docs talk about creating a ts_vector index on concatenated columns, like so:
CREATE INDEX pgweb_idx ON pgweb
USING gin(to_tsvector('english', title || ' ' || body));
which I can search like so:
... WHERE
(to_tsvector('english', title||' '||body) ## to_tsquery('english', 'foo'))
However, if I wanted to sometimes search just the title, sometimes just the body, and sometimes both, I would need 3 separate indexes. And if I added in a third column, that could potentially be 6 indexes, and so on.
An alternative which I haven't seen in the docs is just to index the two columns seperately, and then just use a normal WHERE...AND query:
... WHERE
(to_tsvector('english', title) ## to_tsquery('english','foo'))
AND
(to_tsvector('english', body) ## to_tsquery('english','foo'))
Benchmarking the two on ~1million rows seems to have basically no difference in performance.
So my question is:
Why would I want to concatenate indexes like this, rather than just indexing columns individually? What are the advantages/disadvantages of both?
My best guess is that if I knew in advance I would only want to ever search both columns (never one at a time) I would only ever need one index by concatenating which use less memory.
Edit
moved to: https://dba.stackexchange.com/questions/15412/postgres-full-text-search-with-multiple-columns-why-concat-in-index-and-not-at
Using one index is easier / faster for a DB;
It will be quite difficult to properly rank results when using two indexes;
You can assign relative weights to columns when creating a single index, so that match in title will be worth more than a match in body;
You are searching for a single word here, what happens if you search for several and they appear separately in different columns?
To answer the question of the implementation of #3, please see https://www.postgresql.org/docs/9.1/textsearch-controls.html:
a weight is one of the letters A, B, C, or D
UPDATE tt SET ti =
setweight(to_tsvector(coalesce(title,'')), 'A') ||
setweight(to_tsvector(coalesce(keyword,'')), 'B') ||
setweight(to_tsvector(coalesce(abstract,'')), 'C') ||
setweight(to_tsvector(coalesce(body,'')), 'D');