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Foxtrot search server6/3/2023 Estimation of the remaining indexing time is now more reliable.More secure network encryption (TLS 1.3) for shared indices.This is slower, but may be more accurate. Added a hidden preference to use the NaturalLanguage framework for language identification on macOS 10.14 and later (defaults write UseNaturalLanguageIdentification -bool YES).Highlighting found occurrences did not work for documents created with recent versions of Pages, Numbers and Keynote.Use a modern API for language identification on macOS 10.13 and later.When indexing takes a very long time, a message window is displayed to suggest reading our FAQ in case the user is not in front of his Mac, the estimated remaining time is now updated in this message window, and it is dismissed when indexing is eventually finished.Hidden preferences have been added to further control their length. Document excerpts are now more relevant, as their length is adjusted according to the layout of the search results list.when the “My Computer” disclosure triangle is toggled off). Searching did not work correctly when the search sources list was collapsed (i.e.mkv files should now be categorized as “Video”. File types for which no Spotlight metadata importer is installed, but for which an application properly defines a Uniform Type Identifier, are no longer categorized as “other documents” for example.When the “by location” categorizer (categorize by path) was used in “view as columns” mode, there was no tooltip shown upon hovering over a truncated path and truncated paths were incorrectly displayed, with no “…” indicator.It's also likely to produce maintenance headaches further down the line. I must emphasis that this is not really an optimal technique and should only be used if the data is a good match for the approach and for some reason you do not want to use full text search (and the database performance on the like scan really is unacceptable). This would also be one of those cases where using triggers, rather than application code, would be much preferred. For example, if my data always consisted of a field containing the pattern 'AAA/BBB/CCC' and my users were searching on BBB then I'd tokenize that out at insert/update (and remove on delete). Firstly full text searching, it's really designed for this sort of problem so I'd look at that first.Īlternatively in some circumstances it might be appropriate to denormalize the data and pre-process the target fields into appropriate tokens, then add these possible search terms into a separate one to many search table. You do have a couple of alternative approaches. Like '%ABC%' will always perform a full table scan. So the possible optimizations depend a lot on the specifics of your table definition and the selectivity of your data. If you always need to return a large set of rows you would almost certainly be better off with a table scan. first determine a set of IDs (or whatever your PK is) from your highly selective index, then search your less selective columns with a filter against that small set of PKs. If you have a bunch of columns to search across only a few that are highly selective, you could create multiple indexes and use a reduction approach. ![]() Select * from t1 with (index(t1i1)) where v1 like '%456%' Or you can tell the optimizer directly to use the index, if it hadn't decide to use this plan on its own: ![]() If you look at the actual execution plan you can see the engine scanned the index and did a bookmark lookup on the matching row. create index that only contains the column(s) to search across Here is an example where the total row size is much greater than the column size to search across: create table t1 (v1 varchar(100), b1 varbinary(8000)) It will still be a scan, but your index will fit more rows per page than the source table. If you are searching for values with high selectivity/uniqueness (so few rows to return), and the predicated columns are a smallish portion of the entire row size, an index could be quite useful. How much of the total size of the row are your predicated columns? How many rows do you expect to match? Do you need to return all rows that match the predicate, or just top 1 or top n rows? You can potentially see performance improvements by adding index(es), it depends a lot on the specifics :)
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