Оптимизация методов equals() с помощью метода Pattern Matching

0:00 Intro: designing equals() methods
1:13 Generating equals() and hashCode() methods
1:56 What could go wrong with a bad hashCode() method
4:31 Taking a look at the generated equals() methods
7:11 Checking the record and pattern matching implementations
9:33 What is predictive branching, what is the cost of a failing prediction?
12:41 Evaluating the cost of two failing predictions.
14:53 Evaluating the performance of equals() methods on simple data sets
16:07 First data set: all the objects are the same instance
16:46 Second data set: different instances carrying the same state
18:43 Third data set: different objects of the same type
20:41 Fourth data set: objects of different types
22:00 First conclusions on what patterns are the best
22:39 Adding glitches to the data sets
24:41 Results for first data set (same instances) with glitches
25:20 Results for second data set (equal instances) with glitches
26:07 Results for third data set (different instances) with glitches
28:30 Results for fourth data set (different types) with glitches
30:05 What conclusions can you draw from all these benches?
32:33 That's it for today, talk to you soon!

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