May 2026
Intermediate to advanced
412 pages
12h 29m
French
Content preview from Spark haute performance (French Edition), 2nd Edition
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Chapitre 9. Au-delàde Scala
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Travailler avec Spark ne signifie pas se limiter à Scala, ni même se limiter à la JVM ou aux langages explicitement pris en charge par Spark. Il existe de nombreuses façons d’utiliser Spark avec différents langages, notamment des systèmes d’appel de procédure à distance comme Spark Connect, des options d’interopérabilité JVM telles que Java Native Interface (JNI), Java Native Access (JNA) ou Java Native Runtime (JNR), ainsi que des wrappers précompilés pour ces derniers (comme PySpark ou SparklyR). Ce chapitre abordera les considérations de performance liées à l'utilisation d'autres langages dans Spark et la manière de travailler efficacement avec les bibliothèques existantes. Tu apprendras les compromis en matière de performance liés à l'utilisation de différents langages et comment tu peux utiliser les accélérateurs Spark courants pour améliorer tes pipelines existants avec un minimum de modifications.
L'interopérabilité linguistique de Spark peut être divisée en deux groupes : le premier concerne le code des workers au sein de tes transformations (par exemple, les Lambda dans tes maps), et le second concerne la possibilité de spécifier les transformations sur les RDD/Datasets (par exemple, le programme driver).
Spark dispose d’API natives, ou d’une prise en charge intégrée des API, pour écrire des programmes pilotes et ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.