November 2024
Intermediate to advanced
606 pages
17h 8m
French
Content preview from Spark : Le guide définitif
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Chapitre 20. Principes de base du traitement des flux
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Le traitement de flux est une exigence clé dans de nombreuses applications de big data. Dès qu'une application calcule quelque chose de précieux - par exemple, un rapport sur l'activité des clients ou un nouveau modèle d'apprentissage automatique - une organisation voudra calculer ce résultat en continu dans un cadre de production. Par conséquent, les organisations de toutes tailles commencent à intégrer le traitement de flux, souvent même dans la première version d'une nouvelle application.
Heureusement, Apache Spark a une longue histoire de prise en charge de haut niveau de la diffusion en continu. En 2012, le projet a intégré Spark Streaming et son API DStreams, l'une des premières API à permettre le traitement des flux à l'aide d'opérateurs fonctionnels de haut niveau tels que map et reduce. Des centaines d'organisations utilisent désormais DStreams en production pour de grandes applications en temps réel, traitant souvent des téraoctets de données par heure. À l'instar de l'API RDD (Resilient Distributed Dataset), l'API DStreams repose toutefois sur des opérations de relativement bas niveau sur des objets Java/Python qui limitent les possibilités d'optimisation de plus haut niveau. Ainsi, en 2016, le projet Spark a ajouté Structured Streaming, une nouvelle API de streaming construite directement ...
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