November 2024
Intermediate to advanced
438 pages
10h 40m
French
Content preview from Algorithmes de données avec Spark
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Chapitre 11. Modèles de conception de la jonction
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Dans ce chapitre, nous allons examiner des modèles de conception pratiques pour joindre des ensembles de données. Comme dans les chapitres précédents, je me concentrerai sur les modèles qui sont utiles dans les environnements du monde réel. PySpark prend en charge une opération de jointure de base pour les RDD (pyspark.RDD.join()) et les DataFrames (pyspark.sql.DataFrame.join()) qui sera suffisante pour la plupart des cas d'utilisation. Cependant, dans certaines circonstances, cette jointure peut s'avérer coûteuse, c'est pourquoi je te montrerai également quelques algorithmes de jointure spéciaux qui peuvent s'avérer utiles.
Ce chapitre présente le concept de base de la jointure de deux ensembles de données, et fournit des exemples de certains modèles de conception de jointure utiles et pratiques. Je te montrerai comment l'opération de jointure est mise en œuvre dans le paradigme MapReduce et comment utiliser les transformations de Spark pour effectuer une jointure. Tu verras comment réaliser des jointures côté carte avec des RDD et des DataFrames, et comment réaliser une jointure efficace à l'aide d'un filtre Bloom.
Introduction à l'opération Join
Dans le monde des bases de données relationnelles, joindre ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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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.