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SPARK - LRBA 5

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SPARK - LRBA 5

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About this activity

DataFrames y Datasets - Uso y Diferencias

Created by

HR Mexico
HR Mexico
Mexico

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SPARK - LRBA 5
 

SPARK - LRBA 5Online version

DataFrames y Datasets - Uso y Diferencias

by HR Mexico
1

¿Cuál es la diferencia principal entre DataFrame y Dataset en Scala?

2

¿Qué engine optimiza ambos?

3

En PySpark, ¿qué API usar para type-safety?

4

Overhead performance Datasets vs DataFrames?

5

Creación Dataset tipado requiere:

6

Mejor para MLlib pipelines:

7

AQE optimiza dinámicamente:

8

SaveMode por default:

9

Tungsten beneficio clave:

10

¿Cuándo usar Dataset sobre DataFrame?

11

Broadcast join condición:

12

Encoder rol en Dataset:

13

Caso fraude: mejor inicial:

14

PartitionBy aplica a:

15

Whole-stage codegen:

16

InferSchema=true overhead:

17

Java Dataset requiere:

18

Benchmark TPC-DS:

19

Buena práctica cache:

20

BucketBy beneficio:

Explicación

En Scala, DataFrame = Dataset[Row] untyped; Dataset[T] compile-time safe

Comparten Catalyst para plans, Tungsten para exec off-heap.

PySpark no soporta Dataset API typed

Datasets deserializan a JVM objects en map/filter.

df.as[Person] usa implicits encoder.

Spark MLlib usa DataFrame API.

Adaptive Query Execution post-shuffle

Lanza excepción si tabla existe.

Vectorized exec minimiza JVM overhead.

Compile-time safety nested cases.

Evita shuffle broadcast memoria nodos.

Permite Catalyst en typed ops.

Optimizado Catalyst para ETL joins.

Crea dirs por columna.

Tungsten genera código optimizado.

Explicación: Analiza sample datos; usa schema explícito.

POJO con getters para bean encoder.

Optimizaciones Catalyst/Tungsten.

MEMORY_AND_DISK por default spill.

Explicación: Hash-based distribución buckets.

 
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