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 8. Travailler avec des données clé/valeur
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Comme de nombreux outils de calcul distribué en parallèle, Spark s'appuie fortement sur le paradigme des paires clé/valeur pour définir et paralléliser les opérations, en particulier les transformations de grande envergure qui nécessitent une redistribution des données entre les machines. Chaque fois que tu souhaites effectuer des opérations groupées en parallèle ou modifier l'ordre des enregistrements entre les machines — qu'il s'agisse de calculer une statistique d'agrégation ou de fusionner des enregistrements clients —, la fonctionnalité clé/valeur de Spark est utile car elle te permet de paralléliser facilement ton travail.
En Scala, Spark dispose de sa propre classePairRDDFunctions d’ s contenant des opérations définies sur des RDD de tuples. La classe PairRDDFunctions, accessible via une conversion implicite, contient la plupart des méthodes de Spark pour les jointures et les agrégations personnalisées. La classeOrderedRDDFunctions d’ s contient les méthodes de tri. Les OrderedRDDFunctions sont disponibles pour les RDD de tuples dans lesquels le premier élément (la clé) a un ordre implicite. En Python, celles-ci sont plutôt définies directement sur la classe de base RDD.
Remarque
Des opérations similaires sont disponibles sur les ensembles de données, comme expliqué dans « Opérations groupées ...
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