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 2. Comment fonctionne Spark
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Ce chapitre présente la conception générale de Spark ainsi que sa place dans l'écosystème du big data. Spark était autrefois comparé à ASF MapReduce, car Spark peut également être utilisé pour le traitement distribué des données avec Hadoop.1 Aujourd’hui, ceux qui ne connaissent pas bien MapReduce le considèrent souvent comme une version « à plus grande échelle » de pandas.
Comme on va le voir dans ce chapitre, les principes de conception de Spark sont assez différents de ceux de MapReduce. Contrairement à Hadoop MapReduce, Spark n’a pas besoin d’être exécuté en tandem avec ASF Hadoop — même si c’est souvent le cas. De plus en plus, Spark est exécuté sans Hadoop chez les fournisseurs de cloud ou en utilisant Kubernetes avec un stockage désagrégé.
Spark a hérité d’une partie de son API, de sa conception et des formats pris en charge d’autres frameworks de calcul existants, en particulier DryadLINQ.2 Le fonctionnement interne de Spark, en particulier la façon dont il gère les pannes, diffère de celui de nombreux systèmes distribués traditionnels.
La capacité de Spark à exploiter l'évaluation paresseuse dans les calculs en mémoire le rend particulièrement unique. Les créateurs de Spark considèrent qu'il s'agit du premier langage de programmation de haut niveau pour le traitement rapide et distribué des données.3
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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.