REGEXP_SUBSTR SQL Server

Question

With T as
( 
select 'Cytomegalovirus Nucleoside Analog DNA Polymerase Inhibitor [EPC],DNA Polymerase Inhibitors [MoA],Nucleoside Analog [Chemical/Ingredient],Nucleoside Analog Antiviral [EPC]' CLASS 
FROM DUAL )

Need to pull strings with [EPC].

Desired Output:

Cytomegalovirus Nucleoside Analog DNA Polymerase Inhibitor [EPC], Nucleoside Analog Antiviral [EPC]

Answer 1

You do not need true regexes, since your split string is a literal. On SQL Server 2017, this should work (not tested):

SELECT STRING_AGG([value], ',')
FROM STRING_SPLIT('Cytomegalovirus Nucleoside Analog DNA Polymerase Inhibitor [EPC],DNA Polymerase Inhibitors [MoA],Nucleoside Analog [Chemical/Ingredient],Nucleoside Analog Antiviral [EPC]', ',')
WHERE [value] LIKE '%\[EPC\]%' ESCAPE '\'

On SQL Server 2016, we lack STRING_AGG , but we do have STRING_SPLIT , so this will work (tested):

SELECT STUFF((
    SELECT ',' + [value]
    FROM STRING_SPLIT('Cytomegalovirus Nucleoside Analog DNA Polymerase Inhibitor [EPC],DNA Polymerase Inhibitors [MoA],Nucleoside Analog [Chemical/Ingredient],Nucleoside Analog Antiviral [EPC]', ',')
    WHERE [value] LIKE '%\[EPC\]%' ESCAPE '\'
    FOR XML PATH('')
), 1, 1, '')

On earlier versions, lack of decently performing native string splitting makes this a giant pain in the behind. Many questions deal with that, like this one . Using any of those solutions in combination with the FOR XML PATH trick for concatenation will work.

Alternatively, a CLR function can do this cleaner and faster (and with true regexes), but implementing those is more involved. This question is a good start for that.

Last but certainly not least: if you have to perform these sorts of operations in SQL, this is usually a sign that your database design is lacking. In particular, the comma-separated values should have been separate rows with an EPC BIT NOT NULL column (or more generally, a CategoryID INT REFERENCES Categories(ID) column), and you should consider ways to have your client code store data in a way that is amenable to efficient manipulation by your database. Normalization is the keyword here.