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SPARK - LRBA 4

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SPARK - LRBA 4

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Spark Streaming - Procesamiento de Datos en Tiempo Real

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HR Mexico
HR Mexico
Mexico

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SPARK - LRBA 4
 

SPARK - LRBA 4Online version

Spark Streaming - Procesamiento de Datos en Tiempo Real

by HR Mexico
1

¿Cuál es la unidad básica de procesamiento en Spark Streaming DStream?

2

¿Qué garantiza exactly-once semantics en Structured Streaming?

3

En Kafka Direct Stream, ¿cómo se evitan receivers?

4

¿Qué maneja el watermark en Structured Streaming?

5

¿Cuál es el rol de StreamingContext?

6

En updateStateByKey, ¿qué usa StateSpec?

7

¿Qué habilita backpressure?

8

En windowed reduceByKeyAndWindow, parámetros son:

9

¿Output mode para agregaciones full table?

10

¿Checkpoint almacena qué?

11

En Structured Streaming, ¿qué es foreachBatch?

12

¿Mejor práctica para escalabilidad Kafka?

13

¿Qué mide scheduling delay?

14

En fault-tolerance receiver, ¿qué es WAL?

15

¿Trigger para baja latencia experimental?

16

¿Para data late en joins stream-stream?

17

¿Config para max rate Kafka partition?

18

En caso fraude, ¿por qué watermark?

19

¿Diferencia key DStream vs Structured?

20

¿Buena práctica idempotencia?

Explicación

DStreams procesan datos en micro-lotes como RDDs por batch interval.

WAL persiste offsets/transacciones; idempotencia evita duplicados.

Direct API lee offsets directamente de Kafka topics __consumer_offsets.

Define cutoff para procesar/drop late data en agregaciones.

Inicializa context con batch duration; único por app.

StateSpec define función update y spec para storage/timeout.

Ajusta input rate dinámicamente basado en processing delay.

windowDuration para tamaño, slide para overlap (e.g., 30s/10s).

Complete emite toda tabla actualizada por trigger.

Permite recovery completo desde último batch completado.

Aplica función a cada micro-lote output DataFrame.

Tasks paralelos match Kafka partitions para throughput.

Indicador backpressure; alto delay sugiere overload.

Replica datos a checkpoint antes ACK fuente.

Spark 3.0+ modo continuo sub-second (microbatch no).

Watermarks permiten joins con bounds temporales.

Limita input per partition en backpressure mode.

Evita estado infinito; drop data post-threshold.

Structured unifica con Spark SQL, mejor optimización.

Commit offsets solo tras sink exitoso para replay-safe.

 
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