refactor: optimize fuzzy matching with lazy initialization #2523
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| ```yaml | ||
| # Global settings | ||
| verbose: true | ||
| disable-fuzzy: false |
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I just realized that, we are apparently inconsistency when it comes to _ vs - for word separation?
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I think the default is to use -, and we only use _ for experiments to mirror the environment variable. I’m open to discussing it, tho
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| if !e.DisableFuzzy { | ||
| // Lazy initialization of fuzzy model | ||
| if e.fuzzyModel == nil { | ||
| e.setupFuzzyModel() | ||
| } | ||
| if e.fuzzyModel != nil { | ||
| didYouMean = e.fuzzyModel.SpellCheck(call.Task) | ||
| } |
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We should probably wrap this into a mutex.
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Good catch! I've implemented a Once to be sure this is executed only once
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Problem
The fuzzy model was trained on every Task invocation (including shell autocompletion), causing performance issues with hundreds/thousands of tasks. The model is only needed when a task doesn't exist to show "Did you mean...?" suggestions.
Solution
Setup()) (thanks @trulede for the suggestion)--disable-fuzzyflag anddisable-fuzzytaskrc option to completely disable the featureChanges
Setup()to lazy init inGetTask()DisableFuzzyoption to taskrc, flags, and ExecutorPerformance
Compatibility
Fully backward compatible. Fuzzy matching enabled by default, users can opt-out via
.taskrc.ymlor--disable-fuzzy.Related Issues