November 2024
Intermediate to advanced
438 pages
10h 40m
French
Content preview from Algorithmes de données avec Spark
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Chapitre 3. Transformations du mappeur
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Ce chapitre présentera les transformations Spark mapper les plus courantes à l'aide d'exemples de travail simples. Sans une compréhension claire des transformations, il est difficile de les utiliser de manière appropriée et significative pour résoudre n'importe quel problème de données. Nous examinerons les transformations de mappeur dans le contexte des abstractions de données RDD. Un mappeur est une fonction utilisée pour traiter tous les éléments d'un RDD source et générer un RDD cible. Par exemple, un mappeur peut transformer un enregistrement String en tuples, en paires (clé, valeur) ou tout autre résultat souhaité. De manière informelle, nous pouvons dire qu'un mappeur transforme une sourceRDD[V] en une cible RDD[T], oùV et T sont les types de données des RDD source et cible, respectivement. Tu peux également appliquer des transformations de mappeur à des DataFrames, soit en appliquant des fonctions DataFrame (à l'aide de select() et d'UDF) à toutes les lignes, soit en convertissant ton DataFrame (un tableau de lignes et de colonnes) en RDD, puis en utilisant lestransformations de mappeur de Spark.
Abstractions de données et mappeurs
Spark dispose de nombreuses transformations et actions, mais ce chapitre ...
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