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Martin

4:02 AM

In fact, I have used lower on bodies in the queries which I linked here: List of commonly misspelled words.

For example this one: data.stackexchange.com/math/query/1157664/…

WHERE (CONCAT(LOWER(Title),' ',LOWER(Body)) LIKE '%[^a-zA-Z]##text?matrice##[^a-zA-Z]%'

Here is a post about the issue on SO: How can I search (case-insensitive) in a column using LIKE wildcard?

The top voted answer uses COLLATE UTF8_GENERAL_CI.

There is also an answer with lower, some comment mentions lower, it has a few comments related to that.

@Col. -- Admittedly, this is less than ideal for indexed columns, but it will work for a structure which is already in place. I've also found that case-insensitive searches are more often on columns which are not indexed anyway. — cwallenpoole May 20 '10 at 18:48

1 hour later…

Martin

5:14 AM

So I suppose this should be faster:

(Body LIKE '%[^a-zA-Z]##text?matrice##[^a-zA-Z]%' COLLATE Latin1_General_CI_AI)
OR (Title LIKE '%[^a-zA-Z]##text##[^a-zA-Z]%' COLLATE Latin1_General_CI_AI)

data.stackexchange.com/math/query/1157664/…

1 hour later…

rene

6:41 AM

@Martin I would expect that to be faster yes.

@Martin yeah, I think mathematics has the benefit of having way less rows then SO main. Combined with the awesome hardware SQL Server runs on a lot of things that might go south on SO does work for all other sites.

Optimized or not.

Martin

7:41 AM

Well, it is the second largest site, but still much smaller than Stack Overflow.

2 hours later…

rene

9:28 AM

in Tavern on the Meta, 12 mins ago, by Sonic the Stay-Home Hedgehog

@rene Can you please update the database schema documentation with the changes listed in the above post under point 5?

remind to self on Sunday.

3 hours later…

Feeds

12:44 PM

Q: Query the Tags that appear together frequently

I am trying to find out the tags that appear frequently together for a given tag. for e.g pandas will appear frequently with python3 and nltk will appear together with nlp and so on. So by running a query for pandas, I want to get the more frequently used tags with pandas.