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SPARK - LRBA 7

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SPARK - LRBA 7

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Conectores en LRBA Spark

Created by

HR Mexico
HR Mexico
Mexico

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SPARK - LRBA 7
 

SPARK - LRBA 7Online version

Conectores en LRBA Spark

by HR Mexico
1

¿Cuál es el rol principal de SupportsBatchRead en conectores LRBA?

2

En LRBA, ¿qué opción habilita coalescing dinámico de particiones?

3

Para exactly-once en Kafka connector LRBA, se usa:

4

En JDBC LRBA, ¿qué parámetros definen partitioning?

5

¿Qué interfaz soporta schema evolution en LRBA?

6

En Delta Lake LRBA, ¿qué comando optimiza clustering?

7

LRBA reduce overhead comparado con YARN en:

8

Para custom connector, ¿qué trait registra el nombre corto?

9

¿Qué mide processedRowsPerSecond en query progress?

10

En Hudi LRBA, COPY_ON_WRITE es ideal para:

11

Para tolerancia a fallos, ¿qué es mandatory?

12

¿Qué habilita columnar reads en LRBA?

13

En Elasticsearch LRBA, optimiza bulk con:

14

Skew mitigation en LRBA usa:

15

Protocolo de commit en writers LRBA:

16

Para Kubernetes en LRBA:

17

¿Qué evita duplicados en Kafka producer?

18

MERGE en Delta soporta:

19

Métrica para data availability en query:

20

Mejor práctica para LRBA security:

Explicación

SupportsBatchRead provee PartitionReaderFactory para lecturas distribuidas en InputPartitions, clave para paralelismo en LRBA.

AQE con esta config fusiona particiones pequeñas post-shuffle para eficiencia.

Combina idempotencia con WAL para delivery semántico exacto.

Estos permiten splits paralelos basados en rango de columna.

SupportsMetadata maneja cambios de schema sin fallos.

Z-ordering mejora pruning en queries por columnas especificadas.

Elimina driver central, delegando a lightweight scheduler.

shortName() permite uso vía format("nombre").

Métrica de Spark Streaming para rows procesados/segundo.

Escribe nuevos files sin compactions en runtime.

Almacena estado para recovery exacto.

Formato Tungsten para eficiencia vectorizada.

Controla tamaño de batches para indexing.

Redistribuye datos desiguales para balance.

Asegura atomicidad idempotente.

Config para deployment lightweight.

Producer idempotence detecta y dropa duplicates.

Operaciones ACID-like para lakehouse.

Indica si hay datos listos para procesar.

Cumple estándares como HIPAA con encryption y auth.

 
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