September 2024
Intermediate to advanced
400 pages
10h 47m
Spanish
Content preview from Aprender Spark, 2ª Edición
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Capítulo 3. Las API estructuradas de Apache Spark
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En este capítulo, exploraremos las principales motivaciones para añadir estructura a Apache Spark, cómo esas motivaciones condujeron a la creación de API de alto nivel (DataFrames y Datasets), y su unificación en Spark 2.x en todos sus componentes. También examinaremos el motor Spark SQL que sustenta estas API estructuradas de alto nivel.
Cuando se introdujo Spark SQL en las primeras versiones de Spark 1.x, seguido de DataFrames como sucesor de SchemaRDDs en Spark 1.3, obtuvimos nuestro primer atisbo de estructura en Spark. Spark SQL introdujo funciones operativas expresivas de alto nivel, imitando una sintaxis similar a SQL, y DataFrames, que sentó las bases para una mayor estructura en versiones posteriores, allanó el camino hacia operaciones de alto rendimiento en las consultas computacionales de Spark.
Pero antes de hablar de las API estructuradas más recientes, echemos un breve vistazo a lo que supone no tener estructura en Spark echando un vistazo al sencillo modelo de API de programación RDD.
Spark: ¿Qué hay debajo de un RDD?
El RDD es la abstracción más básica de Spark. Hay tres características vitales asociadas a un RDD:
-
Dependencias
-
Particiones (con alguna información de localidad)
-
Función de cálculo: Partición =>
Iterator[T]
Los tres son parte integrante del sencillo modelo API de programación ...
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