September 2024
Intermediate to advanced
310 pages
9h 26m
Spanish
Content preview from Procesamiento de flujos con Apache Flink
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Capítulo 6. Operadores temporales y de ventana
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En este capítulo trataremos los métodos de la API DataStream para el manejo del tiempo y los operadores basados en el tiempo, como las ventanas. Como aprendiste en "Semántica del tiempo ", los operadores basados en el tiempo de Flink pueden aplicarse con diferentes nociones de tiempo.
En primer lugar, aprenderemos a definir las características temporales, las marcas de tiempo y las marcas de agua. Luego, cubriremos las funciones de proceso, transformaciones de bajo nivel que proporcionan acceso a marcas de tiempo y marcas de agua y pueden registrar temporizadores. A continuación, llegaremos a utilizar la API de ventanas de Flink, que proporciona implementaciones incorporadas de los tipos de ventana más comunes. También obtendrás una introducción a las operaciones de ventana personalizadas, definidas por el usuario, y a las construcciones de ventana básicas, como asignadores, activadores y desalojadores. Por último, hablaremos de cómo unir flujos a tiempo y de estrategias para manejar eventos tardíos.
Configurar las características temporales
Para definir en las operaciones temporales en una aplicación de procesamiento de flujos distribuidos, es importante comprender el significado del tiempo. Cuando especifiques una ventana para recoger eventos en cubos de un minuto, ¿qué eventos contendrá exactamente cada cubo? En ...
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