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

[@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.

[@basedsonu]

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.

[@vaibhavj12]

Helpful❤