Event#300 Streaming the result of a Query execution by Jaroslav Mazurak
https://www.javaclub.lviv.ua/2023/11/28/Event-300-Streaming-the-result-of-a-Query-execution-by-Jaroslav-Mazurak.html
This week on Java Club Yaroslav Mazurak will share hos experience on how to stream the result of a Query execution. Don’t miss this opportunity to learn and connect with fellow Java enthusiasts. Join us next Thursday, at 10:00 Online Media #Stream #Query #SQL #monitoring
https://www.javaclub.lviv.ua/2023/11/28/Event-300-Streaming-the-result-of-a-Query-execution-by-Jaroslav-Mazurak.html
This week on Java Club Yaroslav Mazurak will share hos experience on how to stream the result of a Query execution. Don’t miss this opportunity to learn and connect with fellow Java enthusiasts. Join us next Thursday, at 10:00 Online Media #Stream #Query #SQL #monitoring
Lviv JavaClub
Event#300 Streaming the result of a Query execution by Jaroslav Mazurak
This week on Java Club Yaroslav Mazurak will share hos experience on how to stream the result of a Query execution. Don’t miss this opportunity to learn and connect with fellow Java enthusiasts. Join us next Thursday, at 10:00 Online
🎬 CoffeeJUG: Hunting with Stream Gatherers by Piotr Przybyl
The talk: “Hunting with Stream Gatherers”The speaker: Piotr Przybyl, Senior Developer Advocate @ ElasticStreams were a very nice addition to Java 8, based on lambdas. They allow streamlined data processing without side effects, taking us gently towards functional style. With newer additions to Java, like records and pattern matching, they shine even more in data-driven flows. However, they don’t come without flaws. For starters, the only available extension point was collectors: if your needs for gathering data weren’t satisfied by the whole Collectors ZOO, you could always fall back to creating your own Collector. However, if map, filter or flatMap weren’t enough, you couldn’t add your own intermediate operation. Secondly, parallel streams were limited to ForkJoin pool, effectively rendering them unusable for scenarios involving any IO. Since Java 24, Stream Gatherers are our extension point for intermediate operations in streams. If you’d like to comprehend how they work, find nice use cases and hunt for more performance, this talk is for you.#CoffeeJUG #JUG #java #Stream #Przybyl #Elastic
via YouTube https://www.youtube.com/watch?v=Up3O7UWBl8M
The talk: “Hunting with Stream Gatherers”The speaker: Piotr Przybyl, Senior Developer Advocate @ ElasticStreams were a very nice addition to Java 8, based on lambdas. They allow streamlined data processing without side effects, taking us gently towards functional style. With newer additions to Java, like records and pattern matching, they shine even more in data-driven flows. However, they don’t come without flaws. For starters, the only available extension point was collectors: if your needs for gathering data weren’t satisfied by the whole Collectors ZOO, you could always fall back to creating your own Collector. However, if map, filter or flatMap weren’t enough, you couldn’t add your own intermediate operation. Secondly, parallel streams were limited to ForkJoin pool, effectively rendering them unusable for scenarios involving any IO. Since Java 24, Stream Gatherers are our extension point for intermediate operations in streams. If you’d like to comprehend how they work, find nice use cases and hunt for more performance, this talk is for you.#CoffeeJUG #JUG #java #Stream #Przybyl #Elastic
via YouTube https://www.youtube.com/watch?v=Up3O7UWBl8M
YouTube
CoffeeJUG: Hunting with Stream Gatherers by Piotr Przybyl
The talk: “Hunting with Stream Gatherers”
The speaker: Piotr Przybyl, Senior Developer Advocate @ Elastic
Streams were a very nice addition to Java 8, based on lambdas. They allow streamlined data processing without side effects, taking us gently towards…
The speaker: Piotr Przybyl, Senior Developer Advocate @ Elastic
Streams were a very nice addition to Java 8, based on lambdas. They allow streamlined data processing without side effects, taking us gently towards…