О GraalVM не слышал только ленивый: новые оптимизации, интеграция с Python/Ruby/JS и AOT-компиляция в нативный код. На любой JVM-конференции из каждого утюга рассказывают, как изменится наша жизнь к лучшему с приходом коммунизма^W этой технологии
Operations with Flink state are a common source of performance issues for a typical stateful stream processing application. One tiny mistake can easily make your job to spend most of a precious CPU time in serialization and inflate a checkpoint size to the sky. In this talk we’ll focus on a Flink serialization framework and common problems happening around it
You type a query in a search box and get great search results back. Sounds pretty simple, right? But the same search results ranking can be great for you, but not for someone else.
With widely known Learn-to-Rank process, you need to deduce which search item was relevant for a user in the past to get the better ranking in the future
Case class is a most widely used way to model your data. But when the data is huge, you can amazingly discover that only a tiny 10% of your precious RAM used for the data itself. But where is other 90%?