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.
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.
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how number of stages = no of wide transformations + 1 ?
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.
bhai ne bola bapu dikhta toh bapu dikhta
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.
For wide transformation there is shuffling of data. Like the extra stage is used for aggregation of data.
Sumit sir please add real time use case...for data skewness i feel answer is not correct .salting is the right method...
What is logic behind the answer - 2 wide transformations would result in 3 stages?
Helpful❤
Could you please stop the background music or can you remove background music
Your video are very helpful but the background music 🎶 sometimes irritating
Some questions are too easy and should not be asked in interviews.