Note how the query cache impact on RAM is thus not limited by qcache_max_bytes! If you run, say, 10 concurrent queries, each of them matching upto 1M matches (after filters), then the peak temporary RAM use will be in the 40 MB to 240 MB range, even if in the end the queries are quick enough and do not get cached. Once the query completes, we check the wall time and size thresholds, and either save that compressed result set for reuse, or discard it. Compressed matches can consume anywhere from 2 bytes to 12 bytes per match on average, mostly depending on the deltas between the subsequent docids. That happens after full-text matching, filtering, and ranking, so basically we store total_found pairs. When it's enabled, every full-text search result gets completely stored in memory. When reducing the cache size on the fly, MRU (most recently used) result sets win. These changes are applied immediately, and the cached result sets that no longer satisfy the constraints are immediately discarded. These settings can be changed on the fly using the SET GLOBAL statement: mysql> SET GLOBAL qcache_max_bytes=128000000 qcache_ttl_sec, cached entry TTL, or time to live.Queries that completed faster than this will not be cached. qcache_thresh_msec, the minimum wall query time to cache.Setting qcache_max_bytes to 0 completely disables the query cache. qcache_max_bytes, a limit on the RAM use for cached queries storage.You can configure it using the following directives: Query cache stores a compressed result set in memory, and then reuses it for subsequent queries where possible. Insert into products values (0,'Crossbody Bag with Tassel'), (0,'microfiber sheet set'), (0,'Pet Hair Remover Glove') create table products(title text) min_infix_len='2' To show how it works let's create a table and add few documents into it. If the rejected queue is filled, the engine stops looking for potential matchesĪlternate mode to display the data by returning all suggests, distances and docs each per one rowĭo not skip dictionary words with non alphabet symbols This parameter defines the size of the rejected queue (as reject*max(max_matched,limit)). They are put in a rejected queue that gets reset in case one actually can go in the match queue. Rejected words are matches that are not better than those already in the match queue. Keeps only dictionary words whose length difference is less than N Provides Levenshtein distance and document count of the found words Keeps only dictionary words which Levenshtein distance is less than or equal to N Several options are supported for customization: Option CALL SUGGEST will return suggestions only for the first word.CALL QSUGGEST will return suggestions only for the last word, ignoring the rest. If the first parameter is not a single word, but multiple, then: They return the suggested keywords, Levenshtein distance between the suggested and original keywords and the docs statistics of the suggested keyword. They work only on tables with infixing enabled and dict=keywords. These commands provide for a given word all suggestions from the dictionary. They are both available via SQL only and the general syntax is: CALL QSUGGEST(word, table ) Manticore provides commands CALL QSUGGEST and CALL SUGGEST that can be used for the purpose of automatic spell correction. The idea here is that mostly users are right with spelling, therefore we can use words from their search history as a source of truth even if we don't have the words in our documents nor we use an external dictionary. It's even more utilized in autocomplete functionality, but makes sense for autocorrect too. Another classical approach is to use previous search queries as the data set for spell correction.The context may be not just a neighbour word in your query, but your location, date of time, current sentence's grammar (to let's say change "there" to "their" or not), your search history and virtually any other things that can affect your intent. "white ber" would be corrected to "white bear" while "dark ber" - to "dark beer". Not just dictionary-based, but context-aware, e.g.The problem which may arise here is that your data and the external dictionary can be too different: some words may be missing in the dictionary, some words may be missing in your data. Or can be based on an external dictionary which has nothing to do with your data.The idea here is that mostly in the dictionary made up from your data the spelling is correct and then for each typed word the system just tries to find a word which is most similar to that (we'll speak about how exactly it can be done with Manticore shortly) A dictionary of properly spelled words.Mostly there has to be a data set the system is based on. There are few ways how spell correction can be done, but the important thing is that there is no purely programmatic way which will convert your mistyped "ipone" into "iphone" (at least with decent quality).
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