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I have a file full of long SQL queries, one per line. I need to create a list of unique queries, but most of the queries include parameter values that make using an exact matching tool like uniq impossible. Is there a way to find unique lines "fuzzily", like agrep?

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    It would be handy if you can show some sample input data, and what result you expect. Did you try anything that came close?
    – Bernhard
    Apr 29, 2014 at 11:55
  • aaa, aab, abb, bbb have only one difference between one and the next. Should they be considered all the same? Which one to pick?
    – Stéphane Chazelas
    Apr 29, 2014 at 12:08
  • It should be more like a percentage. For example, those would all be different if the percentage was set to 5% difference.
    – Matt Alexander
    Apr 29, 2014 at 12:22
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    That would still be the same. approximative comparison is not a transitive order (a = b and b = c doesn't necessarily mean a = c if the condition to be equal is that there be less than 5% difference, so if you have a = b and b = c (but possibly a != c), which one(s) should that uniq return?). It would definitely help to have a sample input and expected output.
    – Stéphane Chazelas
    Apr 29, 2014 at 12:41
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    You might have better luck on dba.stackexchange.com for an actual DB solution to this problem. I would recast your Q asking it in DB terms and leave this one here asking for a solution using uniq, agrep, etc. since there are 2 paths to take for you, there seems to be 2 potential Q's. Just don't copy/paste this verbatim there or you'll likely get them both closed as cross-posts which isn't discouraged on SE sites. Also you might want to show some of these queries so we can better assist you.
    – slm ♦
    Apr 30, 2014 at 13:23

2 Answers 2

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If the queries are predictable enough, maybe you could simply sed out the parameter values--e.g. if many queries contain equality comparison with numbers, sed 's/=[[:digit:]]+//g' would remove all the actual numbers, leaving only the column names.

Otherwise, the only really general solutions I can think of are pattern recognition techniques like k-nearest neighbors, which can classify arbitrary lists of strings into clusters based on similarity.

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  • This is exactly what I ended up doing.
    – Matt Alexander
    Apr 30, 2014 at 14:48
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You might have luck running every query through EXPLAIN ANALYZE and finding unique results in the query plans.

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