Job, Stage and Task in Apache Spark | PySpark interview questions

In this video, we explain the concept of Job, Stage and Task in Apache Spark or PySpark. We have gone in-depth to help you understand the topic, but it's important to remember that theory alone may not be enough.
To reinforce your knowledge, we've created many problems for you to practice on the same topic in the community section of our RUclips channel. You can find a link to all the questions in the description below.
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In Apache Spark, the concepts of jobs, stages, and tasks are fundamental to understanding how Spark executes distributed data processing. Here's a breakdown of each term:
Jobs:
A job in Spark represents a computation triggered by an action, such as `count()`, `collect()`, `save()`, etc.
When you perform an action on a DataFrame or RDD, Spark submits a job.
Each job is broken down into smaller, manageable units called stages. The division of a job into stages is primarily based on the transformations applied to the data and their dependencies.
Stages:
A stage consists of a sequence of transformations that can be performed without shuffling the entire dataset across the partitions.
Stages are divided by transformations that require a shuffle, such as `groupBy()` or `reduceByKey()`.
Each stage has its own set of tasks that execute the same code but on different partitions of the dataset, and Spark tries to minimize shuffling between stages to optimize performance.
Tasks:
A task is the smallest unit of work in Spark. It represents the computation performed on a single partition of the dataset.
When Spark executes a stage, it divides the data into tasks, each of which processes a slice of data in parallel.
Tasks within a stage are executed on the worker nodes of the Spark cluster. The number of tasks is determined by the number of partitions in the RDD or DataFrame.
How They Work Together?
When an action is called on a dataset:
1. Spark creates a job for that action. The job is a logical plan to execute the action.
2. The job is divided into stages based on the transformations applied to the dataset. Each stage groups together transformations that do not require shuffling the data.
3. Each stage is further divided into tasks, where each task operates on a partition of the data. The tasks are executed in parallel across the Spark cluster.
Understanding these components is crucial for debugging, optimizing, and managing Spark applications, as they directly relate to how Spark plans and executes distributed data processing.
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[@Simrankotiya10]

Great explaination

[@debabratabar2008]

is below correct ?
df_count = example_df.count() ----> transformation
example_df.count() ---> job ?

[@NiteeshKumarPinjala]

No, count() it self is an action. In First line itself it will create Job

[@ChetanSharma-oy4ge]

What if count function we used along with some variable and transformation?

[@TheBigDataShow]

count is a tricky action. Most Data Engineers actually get confused with this. Ideally, count() is an action and should create a brand new JOB but Apache spark is a very smart computing engine and it uses its source and predicate pushdown and purning, if source stores the value of count() in their meta data then it will directly fetch the value of count() instead of creating a brand new JOB.

[@ChetanSharma-oy4ge]

@@TheBigDataShow Great, Thanks for answering ...do we have some other examples as well? or the resources from where i can get these concepts?

[@siddheshchavan2069]

Can you make end to end data engineering projects?

[@TheBigDataShow]

I have already created one. Please check the channel. There is no prerequisite for this 3-hour long video and project. You just need to know the basics of PySpark. Please check the link.
ruclips.net/video/BlWS4foN9cY/видео.htmlsi=qL0ZSXBELEEKe2L2

[@siddheshchavan2069]

@@TheBigDataShow great, thanks!