I apologize if this is a newbie question but I'm feeling overwhelmed.
I have 100+ million 2-grams(string with two words), 100+ million trigrams, 100+ 4-grams.
I was thinking of separating 2-grams into a table for them, 3-grams into another table, etc., since there's quite a lot of data and I'd like to break it up at least a bit, and don't know how else I could partition it. Plus, I'd like to avoid using enterprise features of SQL Server such as actual partitioning.
Apart from the text column, I'd have Id(bigint), AppearanceCount(int) and Hash(this'd be the hashed text column, it doesn't even need to be a computed column as I don't predict many, or any, edits of the text column).
Typical usage scenarios for the 2-gram table(the others as well) would be:
- Search the table with a regex matching the text column.
- Delete a row with a specified hash
- Insert a row
In an average case, I believe searching the table with a regex should return around 5-10 results, but outliers are possible.
So, what could I do to make sure all of this works fast? Fast being a cycle of the following working in a few seconds for n-grams and not more than a few minutes for 4-grams and 5-grams(the current non-database system for 4-grams takes an hour).
I know about full text search, but I haven't been able to find how to use regexes with it. I would really, really prefer to have work here. If that is not at all possible, I can of course give more details, but I didn't want to overwhelm everyone with an even bigger wall of text.
I'm sorry if this is too general, but I'm in the planning phase of this and would like to know if SQL Server is viable for this. I could perhaps use something else, but I'm most familiar and comfortable with SQL Server.
They're all basically syntagms, aka parts of a sentence that might or might not be gramatically wrong/have a misspelling. The words in those syntagms also have variations because the language I'm working with has cases and genders. https://en.wikipedia.org/wiki/Grammatical_case#Indo-European_languages https://en.wikipedia.org/wiki/Grammatical_gender
These words are not in English in case that matters, but I'll put in some English examples so that it's understandable. This is a stupid example, but if English had cases and wanted to use the word House in various situations, we could have the following situation where we're answering some questions: What is this? "A house." Where are you going? "To houise." What are you buying? "Housua"
Basically, you have a root of a word that can change. So, if people are misspelling the word house or using it in an improper way, we want to delete entries from our database of correct expressions that match a regular expression that signifies how the word house is used incorrectly.
2-grams and 3-grams aren't different at all, it's literally just that 3-grams have one word more.
Let's imagine that the word "home" has different forms based on the type of situation the sentence it's in describes. Also let's imagine English has a word called "foohume" that has nothing to do with the word "home".
Examples of a 2-gram where for some reason people spell home as hume: Went hume Gone hume Going hume Nice humes To huma About humas About foohume
Now, we want to delete all 2-grams which contain an incorrect use of the word home. So basically all of these except the last one. So, someone somewhere would tell the database to delete every 2-gram that matches the following conditions that would be described with a regex: - Is at the beginning of a 2-gram or has a space in front of it - Contains the root "hum" followed by a suffix of "e, es, a, as" So basically it'd be something like "[$ ]hum[e, es, a, as]". I know it's not really a valid regex but you get the idea.
What my fear is is that full text search can't be used properly with this, and that we'll have a situation where we have to sequentially scan the entire table of 2-grams, see if there's a regex match, and return that row's Id(or the entire row, but Id only might be faster). If it was more straightforward than a regex, I suppose a full text search would return the results of this fairly quickly, since as I said there should be about 10 of them on average.