Understanding Jobs, Stages, Tasks and Partitions in Apache Spark under 60 seconds #interview

Sumit Mittal
Sumit Mittal
3.7 هزار بار بازدید - 4 ماه پیش - Understanding Jobs, Stages, Tasks and
Understanding Jobs, Stages, Tasks and Partitions in Apache Spark under 60 seconds #interview

Jobs: Refers to a sequence of tasks triggered by an action, like writing data to storage or displaying results.
Stages: Break down jobs into smaller units to optimize parallel execution.
Tasks: Basic units of work within a stage, each operating on a partition of data. Spark schedules tasks dynamically based on available resources.
Partitions: Divides data into smaller chunks, allowing parallel processing across multiple nodes in a cluster. It's crucial for scalability and performance.

Most commonly asked interview questions when you are applying for any data based roles such as data analyst, data engineer, data scientist or data manager.

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4 ماه پیش در تاریخ 1403/01/12 منتشر شده است.
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