Is it worth to save keyword <-> link relation into "hastable" like structure in mysql?

Question

im working on PHP + MySQL application, which will crawl HDD/shared drive and index all files and directories into database, to provide "fulltext" search on it. So far im doing well, but im stuck on question, if i chosed good way how to store data into database.

On picture below, you can see part schema of my database. Thought is, that i'm saving domain (which represents part of disk which i wana to index) then there are some link(s) (which represents files and folder (with content, filepath, etc) then i have table to store sole (uniq) keywords, which i find in file/folder name or content.

And finaly, i have 16 tables linkkeyword to store relations between links and keywords. I have 16 of them because i thought it might be good to make something like hashtable, because im expecting high number of relations between link <-> keyword. (so far for 15k links and 400k keywords i have about 2.5milion of linkkeyword records). So to avoid storing so much data into one table (and later search above them) i thought that this hastable can be faster. It works like i wana to search for word, i compute it md5 and look at first character of md5 and then i know to which linkkeyword table i should use. So there is only about 150~200k records in each linkkeyword table (against 2.5milions)

So there im curious, if this approach can be of any use, or if will be better to store all linkkeyword information to single table and mysql will take care of it (and to how much link<->keyword it can work?)

So far this was great solution to me, but i crushed hard when i tried to implement regular-expression search. So user can use e.g. "tem*" which can result in temp, temporary, temple etc... In normal way when searching for word, i will conpute in md5 hash and then i know to which linkkeyword table i need to look. But for regular expression i need to get all keywords from keywords table (which matches regular expression) and then process them one by one.

Im also attaching part of code for normal keyword search

 private function searchKeywords($selectedDomains) {
    $searchValues = $this->searchValue;

    $this->resultData = array();
    foreach (explode(" ", $searchValues) as $keywordName) {
        $keywordName = strtolower($keywordName);
        $keywordMd5 = md5($keywordName);
        $selection = $this->database->table('link');
        $results = $selection->where('domain.id', $selectedDomains)->where('domain.searchable = ?', '1')->where(':linkkeyword' . $keywordMd5[0] . '.keyword.keyword LIKE ?', $keywordName)
            ->select('link.*,:linkkeyword' . $keywordMd5[0] . '.weight,:linkkeyword' . $keywordMd5[0] . '.keyword.keyword');

        foreach ($results as $result) {
        $keyExists = array_key_exists($result->linkId, $this->resultData);

        if ($keyExists) {
            $this->resultData[$result->linkId]->updateWeight($result->weight);
            $this->resultData[$result->linkId]->addKeyword($result->keyword);
        } else {
            $domain = $result->ref('domain');
            $linkClass = new search\linkClass($result, $domain);
            $linkClass->updateWeight($result->weight);
            $linkClass->addKeyword($result->keyword);
            $this->resultData[$result->linkId] = $linkClass;
        }
        }
    }
    }

and regular expression search function

private function searchRegexp($selectedDomains) {

//get stored search value
$searchValues = $this->searchValue;
//replace astering and exclamation mark (counted as characters for regular expression) and replace them by their mysql equivalent
$searchValues = str_replace("*", "%", $searchValues);
$searchValues = str_replace("!", "_", $searchValues);

// empty result array to prevent previous results to interfere
$this->resultData = array();
//searched phrase can be multiple keywords, so split it by space and get results for each keyword
foreach (explode(" ", $searchValues) as $keywordName) {
    //set default link result weight to -1 (default value)
    $weight = -1;
    //select all keywords, which match searched keyword (or its regular expression)
    $keywords = $this->database->table('keyword')->where('keyword LIKE ?', $keywordName);

    foreach ($keywords as $keyword) {
    //count keyword md5 sum to determine which table should be use to match it links
    $md5 = md5($keyword->keyword);
    //get all link ids from linkkeyword relation table
    $keywordJoinLink = $keyword->related('linkkeyword' . $md5[0])->where('link.domain.searchable','1');
    //loop found links
    foreach ($keywordJoinLink as $link) {
        //store link weight, for later result sort
        $weight = $link->weight;
        //get link ID
        $linkId = $link->linkId;
        //check if link already exists in results, to prevent duplicity
        $keyExists = array_key_exists($linkId, $this->resultData);
        //if link already exists in result set, just update its weight and insert matching keyword for later keyword tag specification
        if ($keyExists) {
        $this->resultData[$linkId]->updateWeight($weight);
        $this->resultData[$linkId]->addKeyword($keyword->keyword);
        //if link isnt in result yet, insert it
        } else {
        //get link reference
        $linkData = $link->ref('link', 'linkId');
        //get information about domain, to which link belongs (location, flagPath,...)
        $domainData = $linkData->ref('domain', 'domainId');
        //if is domain searchable and was selected before search, add link to result set. Otherwise ignore it
        if ($domainData->searchable == 1 && in_array($domainData->id, $selectedDomains)) {
            //create new link instance
            $linkClass = new search\linkClass($linkData, $domainData);
            //insert matching keyword to links keyword set
            $linkClass->addKeyword($keyword->keyword);
            //set links weight
            $linkClass->updateWeight($weight);
            //insert link into result set
            $this->resultData[$linkId] = $linkClass;
        }
        }
    }
    }
}
}

Answer 1

Your question is mostly one of opinion, so you may want to include the criteria that allow us to answer "worth it' more objectively.

It appears you've re-invented the concept of database sharding (though without distributing your data across multiple servers).

I assume you are trying to optimize search time; if that's the case, I'd suggest that 2.5 million records on a modern hardware is not a particularly big performance challenge, as long as your queries can use an index. If you can't use an index (e.g. because you're doing a regular expression search), sharding will probably not help at all.

My general recommendation with database performance tuning is to start with the simplest possible relational solution, keep tuning that until it breaks your performance goals, then add more hardware, and only once you've done that should you go for "exotic" solutions like sharding.

This doesn't mean using prayer as a strategy. For performance-critical application, I typically build a test database, where I can experiment with solutions. In your case, I'd build a database with your schema without the "sharding" tables, and then populate it with test data (either write your own population routines, or use a tool like DBMonster). Typically, I'd go for at least double the size I expect in production. You can then run and tune queries to prove, one way or another, whether your schema is good enough. It sounds like a lot of work, but it's much less work than your sharding solution is likely to bring along.

There are (as @danFromGermany comments) solutions that are optimized for text serach, and you could use MySQL fulltext search features rather than regular expressions.