Top 15 Spark Interview Questions in less than 15 minutes Part-2

To enhance your career as a Cloud Data Engineer, Check trendytech.in/?src=youtube&su... for curated courses developed by me.
I have trained over 20,000+ professionals in the field of Data Engineering in the last 5 years.
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.
Link of Free SQL & Python series developed by me are given below -
SQL Playlist - • SQL tutorial for every...
Python Playlist - • Complete Python By Sum...
Don't miss out - Subscribe to the channel for more such informative interviews and unlock the secrets to success in this thriving field!
Social Media Links :
LinkedIn - / bigdatabysumit
Twitter - / bigdatasumit
Instagram - / bigdatabysumit
Student Testimonials - trendytech.in/#testimonials
Tags
#mockinterview #bigdata #career #dataengineering #data #datascience #dataanalysis #productbasedcompanies #interviewquestions #apachespark #google #interview #faang #companies #amazon #walmart #flipkart #microsoft #azure #databricks #jobs

[@vaibhavj12]

Helpful❤

[@piyushjain5852]

how number of stages = no of wide transformations + 1 ?

[@sugunanindia]

In Apache Spark, the number of stages in a job is determined by the wide transformations present in the execution plan. Here's a detailed explanation of why the number of stages is equal to the number of wide transformations plus one: ### Transformations in Spark #### Narrow Transformations Narrow transformations are operations where each input partition contributes to exactly one output partition. Examples include: - `map` - `filter` - `flatMap` These transformations do not require data shuffling and can be executed in a single stage. #### Wide Transformations Wide transformations are operations where each input partition can contribute to multiple output partitions. These transformations require data shuffling across the network. Examples include: - `reduceByKey` - `groupByKey` - `join` Wide transformations result in a stage boundary because data must be redistributed across the cluster. ### Understanding Stages #### Stages A stage in Spark is a set of tasks that can be executed in parallel on different partitions of a dataset without requiring any shuffling of data. A new stage is created each time a wide transformation is encountered because the data needs to be shuffled across the cluster. ### Calculation of Stages Given the nature of transformations, the rule "number of stages = number of wide transformations + 1" can be explained as follows: 1. **Initial Stage**: The first stage begins with the initial set of narrow transformations until the first wide transformation is encountered. 2. **Subsequent Stages**: Each wide transformation requires a shuffle, resulting in the end of the current stage and the beginning of a new stage. Thus, for `n` wide transformations, there are `n + 1` stages: - The initial stage. - One additional stage for each wide transformation. ### Example Consider the following Spark job: ```python from pyspark import SparkContext sc = SparkContext.getOrCreate() # Sample RDD rdd = sc.parallelize([(1, 2), (3, 4), (3, 6)]) # Narrow transformation: map rdd1 = rdd.map(lambda x: (x[0], x[1] * 2)) # Wide transformation: reduceByKey (requires shuffle) rdd2 = rdd1.reduceByKey(lambda x, y: x + y) # Another narrow transformation: filter rdd3 = rdd2.filter(lambda x: x[1] > 4) # Wide transformation: groupByKey (requires shuffle) rdd4 = rdd3.groupByKey() # Action: collect result = rdd4.collect() print(result) ``` **Analysis of Stages**: 1. **Stage 1**: Includes `parallelize`, `map`. This is all narrow transformations. 2. **Stage 2**: Starts with `reduceByKey` (a wide transformation) which triggers a shuffle. 3. **Stage 3**: Includes `filter`, which is a narrow transformation. 4. **Stage 4**: Starts with `groupByKey` (another wide transformation) which triggers another shuffle. So, there are two wide transformations (`reduceByKey` and `groupByKey`) and three stages (`number of wide transformations + 1`). ### Conclusion The number of stages in a Spark job is driven by the need to shuffle data between transformations. Each wide transformation introduces a new stage due to the shuffle it triggers, resulting in the formula: `number of stages = number of wide transformations + 1`. This understanding is crucial for optimizing and debugging Spark applications.