What is Spark Stage? Explained

What is Spark Stage? In the context of Apache Spark, a stage is a unit of parallelism in a Spark job. It represents a set of tasks that can be executed together as part of a single job.

1. Spark Stage

Spark application is broken down into Jobs for each and every action and Jobs are broken down into stages for every wider shuffle transformation and finally, stages are broken into tasks.

A Stage is a collection of tasks that share the same shuffle dependencies, meaning that they must exchange data with one another during execution.

When a Spark job is submitted, it is broken down into stages based on the operations defined in the code. Each stage is composed of one or more tasks that can be executed in parallel across multiple nodes in a cluster. Stages are executed sequentially, with the output of one stage becoming the input to the next stage.

Stages are important because they allow Spark to perform parallel computation efficiently. By breaking down a job into stages, Spark can schedule tasks in a way that maximizes parallelism while minimizing the amount of data that needs to be shuffled between nodes. This can lead to significant performance gains, especially for large-scale data processing jobs.

2. Types of Spark Stages

There are two types in Spark:

Narrow Stages: Narrow stages are stages where the data does not need to be shuffled. Each task in a narrow stage operates on a subset of the partitions of its parent RDD. Narrow stages are executed in parallel and can be pipelined.
Wide Stages: Wide stages are stages where the data needs to be shuffled across the nodes in the cluster. This is because each task in a wide stage operates on all the partitions of its parent RDD. Wide stages involve a data shuffle and are typically more expensive than narrow stages.

A typical Spark job consists of multiple stages. Each stage is a sequence of transformations and actions on the input data. When a Spark job is submitted, Spark evaluates the execution plan and divides the job into multiple stages based on the dependencies between the transformations. Spark executes each stage in parallel, where each stage can have multiple tasks running on different nodes in the cluster.

3. Examples of Spark Stage

Let’s consider a few examples to understand Spark stages in more detail.

Example 1

Let’s create an RDD, Filter it and then perform a map operation on it


# Create RDD
val data = spark.sparkContext.parallelize(Seq(1, 2, 3, 4, 5, 6))

# RDD filter
val filtered = data.filter(_ % 2 == 0)

# map()
val mapped = filtered.map(_ * 2)

# Collect
val result = mapped.collect()

You can look at the Spark Stages in Web UI, to navigate to the Spark stage, follow these steps:

Open your web browser and enter the URL of your Spark application’s Web UI. The URL should be in the format http://<driver-node>:4040, where <driver-node> is the IP address or hostname of the node where your Spark driver program is running.
Once you’ve accessed the Spark Web UI, you should see a list of active and completed Spark applications. Click on the link for the application that you want to view.
On the application detail page, you should see a list of completed stages. Click on the link for the stage that you want to view.
The stage detail page will display DAG as shown below with the stage, including the input and output data, the job that the stage belongs to, and the tasks that were executed as part of the stage.

In this example, there are three stages:

the first stage is when the RDD is created using parallelize,
the second stage is when the data is filtered using filter, and
the third stage is when the filtered data is mapped using map. The collect method triggers the execution of all three stages.

Example 2

Now, create two RDDs, perform a join operation, and then write the result to a file:


val sc = spark.sparkContext
val rdd1 = sc.parallelize(Seq(("a",55),("b",56),("c",57)))
val rdd2 = sc.parallelize(Seq(("a",60),("b",65),("d",61)))
val joinrdd = rdd1.cartesian(rdd2)
joinrdd.saveAsTextFile("/path/to/output")

In this example, there are four stages:

the first two stages are when the data is read from the sample using parallelize and transformed into RDDs.
The third stage is when the two RDDs are joined using join.
The fourth stage is when the result of the join is mapped to a string and written to a file using saveAsTextFile.

4. Conclusion

Understanding the concept of stages is important in Spark because it helps developers optimize the performance of their Spark jobs. By designing their transformations to minimize data shuffling, developers can reduce the number of wide stages and improve the performance of their jobs. Additionally, understanding the structure of a job and its stages can help developers troubleshoot issues and optimize their applications for better performance.

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