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

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"𝐀 8 𝐰𝐞𝐞𝐤 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 𝐝𝐞𝐬𝐢𝐠𝐧𝐞𝐝 𝐭𝐨 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮 𝐜𝐫𝐚𝐜𝐤 𝐭𝐡𝐞 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬 𝐨𝐟 𝐭𝐨𝐩 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐛𝐚𝐬𝐞𝐝 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐛𝐲 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐢𝐧𝐠 𝐚 𝐭𝐡𝐨𝐮𝐠𝐡𝐭 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐚𝐧𝐝 𝐚𝐧 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐭𝐨 𝐬𝐨𝐥𝐯𝐞 𝐚𝐧 𝐮𝐧𝐬𝐞𝐞𝐧 𝐏𝐫𝐨𝐛𝐥𝐞𝐦."
𝐇𝐞𝐫𝐞 𝐢𝐬 𝐡𝐨𝐰 𝐲𝐨𝐮 𝐜𝐚𝐧 𝐫𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 -
𝐑𝐞𝐠𝐢𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 𝐋𝐢𝐧𝐤 (𝐂𝐨𝐮𝐫𝐬𝐞 𝐀𝐜𝐜𝐞𝐬𝐬 𝐟𝐫𝐨𝐦 𝐈𝐧𝐝𝐢𝐚) : rzp.io/l/SQLINR
𝐑𝐞𝐠𝐢𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 𝐋𝐢𝐧𝐤 (𝐂𝐨𝐮𝐫𝐬𝐞 𝐀𝐜𝐜𝐞𝐬𝐬 𝐟𝐫𝐨𝐦 𝐨𝐮𝐭𝐬𝐢𝐝𝐞 𝐈𝐧𝐝𝐢𝐚) : rzp.io/l/SQLUSD
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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[@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.

[@epicdigger4110]

bhai ne bola bapu dikhta toh bapu dikhta

[@SabyasachiManna-sw2cg]

To answer your questions, Let's assume you have one wide transform function reduceByKey() , it will create two stages stage0 and stage1 where shuffling is involved. I hope it helps you.