Understanding Spark Jobs, DAGs, Stages, Tasks, and Partitions: A Deep Dive with Examples

Apache Spark is a powerful distributed computing framework that is widely used for big data processing and analytics. Understanding how Spark processes data through jobs, Directed Acyclic Graphs (DAGs), stages, tasks, and partitions is crucial for optimizing your Spark applications and gaining deeper insights into their performance. In this blog post, we will discuss these key concepts and provide examples to illustrate their relationship and behavior in different scenarios.

Example 1: Basic Transformation and Action

from pyspark.sql import SparkSession

spark = SparkSession.builder.master("local").appName("example1").getOrCreate()
data = [1, 2, 3, 4, 5]
rdd = spark.sparkContext.parallelize(data, 2)
rdd2 = rdd.map(lambda x: x * 2)
result = rdd2.collect()

In this example, we create an RDD from a list of numbers, apply a map transformation, and finally perform a collect action. The collect action triggers the following:

1 Job

1 DAG

1 Stage (since there are no shuffles)

2 Tasks (one for each partition)

2 Partitions (specified during RDD creation)

Let's break down the code and explain the counts in detail:

1. Importing the required module:

from pyspark.sql import SparkSession

This line imports the SparkSession module, which is the entry point for using the DataFrame and Dataset API in Spark.

2. Creating a SparkSession:

spark = SparkSession.builder.master("local").appName("example1").getOrCreate()

Here, we create a SparkSession with the name "example1" and set the master URL to "local," which means Spark will run locally with one worker thread. The getOrCreate() function retrieves an existing SparkSession or creates a new one if none exists.

3. Defining the data:

data = [1, 2, 3, 4, 5]

We create a Python list containing five integers.

4. Creating an RDD:

rdd = spark.sparkContext.parallelize(data, 2)

We create an RDD named rdd by calling the parallelize() method on the SparkContext object. The second argument, 2, specifies the number of partitions to divide the data into. In this case, two partitions are created, so we end up with 2 Partitions.

5. Applying a map transformation:

rdd2 = rdd.map(lambda x: x * 2)

We apply a map() transformation to the rdd object, which doubles each element in the RDD. This line of code doesn't trigger any computation since Spark employs lazy evaluation for transformations.

6. Performing a collect action:

result = rdd2.collect()

The collect() action triggers the actual computation in Spark. It retrieves all elements from the rdd2 object and returns them as a list. Since this is the first action encountered in the code, Spark will create 1 Job and 1 DAG.

7. Understanding Stages: In this example, there is only one transformation (map()) before the collect() action, and it doesn't involve any shuffle operation. As a result, there is only 1 Stage in the DAG.

8. Understanding Tasks: Each partition in an RDD corresponds to one task per stage. In this example, we have two partitions, so there will be 2 Tasks in the single stage.

To summarize, this example demonstrates a basic Spark job with one action (collect()), resulting in the following counts:

• 1 Job: Triggered by the collect() action.

• 1 DAG: Created for the single job.

• 1 Stage: The map transformation doesn't involve a shuffle operation, so there is only one stage.

• 2 Tasks: One task per partition.

• 2 Partitions: Specified during RDD creation using parallelize().

Example 2: Multiple Actions

from pyspark.sql import SparkSession

spark = SparkSession.builder.master("local").appName("example2").getOrCreate()
data = [1, 2, 3, 4, 5]
rdd = spark.sparkContext.parallelize(data, 2)
rdd2 = rdd.map(lambda x: x * 2)
result1 = rdd2.collect()
result2 = rdd2.count()

In this example, we have two actions: collect and count. Each action will create:

1 Job per action (2 Jobs total)

1 DAG per job (2 DAGs total)

1 Stage per job (2 Stages total, as there are no shuffles)

2 Tasks per stage (4 Tasks total, one for each partition)

2 Partitions (specified during RDD creation)

Let's break down the code and explain the counts in detail:

1. Importing the required module:

from pyspark.sql import SparkSession

This line imports the SparkSession module, which is the entry point for using the DataFrame and Dataset API in Spark.

2. Creating a SparkSession:

spark = SparkSession.builder.master("local").appName("example2").getOrCreate()

Here, we create a SparkSession with the name "example2" and set the master URL to "local," which means Spark will run locally with one worker thread. The getOrCreate() function retrieves an existing SparkSession or creates a new one if none exists.

3. Defining the data:

data = [1, 2, 3, 4, 5]

We create a Python list containing five integers.

4. Creating an RDD:

rdd = spark.sparkContext.parallelize(data, 2)

We create an RDD named rdd by calling the parallelize() method on the SparkContext object. The second argument, 2, specifies the number of partitions to divide the data into. In this case, two partitions are created, so we end up with 2 Partitions.

5. Applying a map transformation:

rdd2 = rdd.map(lambda x: x * 2)

We apply a map() transformation to the rdd object, which doubles each element in the RDD. This line of code doesn't trigger any computation since Spark employs lazy evaluation for transformations.

6. Performing a collect action:

result1 = rdd2.collect()

The collect() action triggers the actual computation in Spark. It retrieves all elements from the rdd2 object and returns them as a list. Since this is the first action encountered in the code, Spark will create 1 Job and 1 DAG for this action.

7. Performing a count action:

result2 = rdd2.count()

The count() action triggers another computation in Spark. It counts the number of elements in the rdd2 object and returns the result as an integer. Since this is another action, Spark will create an additional Job and DAG for this action. This brings the total count to 2 Jobs and 2 DAGs.

8. Understanding Stages: In this example, there is only one transformation (map()) before each action, and it doesn't involve any shuffle operation. As a result, there is only 1 Stage for each action, which means a total of 2 Stages.

9. Understanding Tasks: Each partition in an RDD corresponds to one task per stage. In this example, we have two partitions, so there will be 2 Tasks for each stage, resulting in 4 Tasks in total.

To summarize, this example demonstrates a Spark job with two actions (collect() and count()), resulting in the following counts:

• 2 Jobs: Triggered by the collect() and count() actions.

• 2 DAGs: Created for each job.

• 2 Stages: The map transformation doesn't involve a shuffle operation, so there is only one stage per job.

• 4 Tasks: One task per partition for each stage

Example 3: Shuffle Operations

from pyspark.sql import SparkSession

spark = SparkSession.builder.master("local").appName("example3").getOrCreate()
data = [("apple", 3), ("banana", 2), ("orange", 4), ("apple", 1), ("orange", 6)]
rdd = spark.sparkContext.parallelize(data, 2)
rdd2 = rdd.reduceByKey(lambda a, b: a + b)
result = rdd2.collect()

In this example, we have an RDD with key-value pairs and use the reduceByKey transformation, which introduces a shuffle operation. This will create:

1 Job

1 DAG

2 Stages (1 for the map operation and 1 for the reduce operation)

2 Tasks per stage (4 Tasks total, one for each partition)

2 Partitions (specified during RDD creation)

Let's break down the code and explain the counts in detail:

1. Importing the required module:

from pyspark.sql import SparkSession

This line imports the SparkSession module, which is the entry point for using the DataFrame and Dataset API in Spark.

2. Creating a SparkSession:

spark = SparkSession.builder.master("local").appName("example3").getOrCreate()

Here, we create a SparkSession with the name "example3" and set the master URL to "local," which means Spark will run locally with one worker thread. The getOrCreate() function retrieves an existing SparkSession or creates a new one if none exists.

3. Defining the data:

data = [("apple", 3), ("banana", 2), ("orange", 4), ("apple", 1), ("orange", 6)]

We create a Python list containing five tuples, each with a string representing a fruit and an associated integer value.

4. Creating an RDD:

rdd = spark.sparkContext.parallelize(data, 2)

We create an RDD named rdd by calling the parallelize() method on the SparkContext object. The second argument, 2, specifies the number of partitions to divide the data into. In this case, two partitions are created, so we end up with 2 Partitions.

5. Applying a reduceByKey transformation:

rdd2 = rdd.reduceByKey(lambda a, b: a + b)

We apply a reduceByKey() transformation to the rdd object, which sums the integer values with the same key (fruit name). This line of code doesn't trigger any computation since Spark employs lazy evaluation for transformations. However, the reduceByKey() operation requires a shuffle to aggregate the values with the same key across partitions, which will result in multiple stages.

6. Performing a collect action:

result = rdd2.collect()

The collect() action triggers the actual computation in Spark. It retrieves all elements from the rdd2 object and returns them as a list. Since this is the first action encountered in the code, Spark will create 1 Job and 1 DAG for this action.

7. Understanding Stages: In this example, the reduceByKey() transformation introduces a shuffle operation. As a result, Spark divides the computation into two stages: one stage for the map operation (applying the lambda function) and another stage for the reduce operation (aggregating the values with the same key). Therefore, there are 2 Stages in this example.

8. Understanding Tasks: Each partition in an RDD corresponds to one task per stage. In this example, we have two partitions, so there will be 2 Tasks for each stage, resulting in 4 Tasks in total.

To summarize, this example demonstrates a Spark job with a shuffle operation introduced by the reduceByKey() transformation, resulting in the following counts:

• 1 Job: Triggered by the collect() action.

• 1 DAG: Created for the single job.

• 2 Stages: One stage for the map operation and another stage for the reduce operation due to the shuffle introduced by reduceByKey().

• 4 Tasks: One task per partition for

Thumb rules

When examining Spark code, you can follow these thumb rules to estimate the number of Jobs, DAGs, Stages, Tasks, and Partitions:

1. Jobs:

• One job is created for each action (e.g., collect(), count(), saveAsTextFile(), etc.) encountered in the code.

• Each job is independent and has its own DAG.

2. DAGs:

• One Directed Acyclic Graph (DAG) is created for each job.

• A DAG is a representation of the sequence of transformations and dependencies between stages in a job.

3. Stages:

• Stages are created based on the transformations in the code.

• Transformations that don't require a shuffle operation (e.g., map(), filter(), etc.) are grouped into a single stage.

• Whenever a shuffle operation is required (e.g., reduceByKey(), groupBy(), etc.), a new stage is created.

• A stage's tasks execute the same set of transformations on different partitions.

4. Tasks:

• Tasks are the smallest units of execution in Spark.

• The number of tasks depends on the number of partitions and stages.

• Each partition has one task per stage.

• To find the total number of tasks, multiply the number of partitions by the number of stages.

5. Partitions:

• Partitions are specified during RDD or DataFrame creation, either explicitly or by using the default value defined by Spark.

• For RDDs, you can specify the number of partitions using the parallelize() or textFile() methods.

• For DataFrames, you can set the number of partitions using spark.conf.set("spark.sql.shuffle.partitions", num_partitions) or let Spark use the default value (200).

• Partitions can also be changed using operations like repartition(), coalesce(), or partitionBy() for DataFrames.

By following these thumb rules, you can quickly estimate the number of Jobs, DAGs, Stages, Tasks, and Partitions when reviewing Spark code. Keep in mind that these are just rough estimates, as the actual number of tasks or partitions may vary depending on the data and the specific operations involved.

Conclusion:

In conclusion, understanding how Spark divides computation into Jobs, DAGs, Stages, Tasks, and Partitions is crucial for optimizing the performance and resource utilization of your Spark applications. Throughout this post, we have discussed the different components of a Spark application and provided examples to help illustrate how these components are created based on the code's transformations and actions. By following the thumb rules we've provided, you can analyze Spark code more efficiently and accurately estimate the number of Jobs, DAGs, Stages, Tasks, and Partitions that will be generated during execution.

Apache Spark's design enables it to process large-scale data efficiently by parallelizing tasks across multiple partitions and workers. Its lazy evaluation approach helps optimize the execution plan by only processing the required data when an action is called. However, to take full advantage of Spark's capabilities, it is essential to have a solid understanding of the core concepts and components we've discussed in this post.

We began our discussion by examining Jobs, the fundamental unit of execution in Spark. Each action encountered in the code initiates a new Job, which corresponds to a single, independent computation. Jobs are composed of one or more stages, which are then further divided into tasks.

DAGs represent the sequence of transformations and dependencies between stages within a Job. These directed acyclic graphs are crucial for understanding the flow of data through the computation and identifying potential bottlenecks or areas for optimization. By examining the DAGs, you can gain insights into how your data is being processed and make adjustments to improve performance.

Stages are the next level of granularity in Spark's execution model. They are formed by grouping together transformations that don't require a shuffle operation. Whenever a shuffle is necessary, a new stage is created. Understanding the number and structure of stages in your application can help you identify opportunities for optimization and potential performance issues.

Tasks are the smallest units of execution in Spark and are responsible for processing individual partitions of data. Each partition has one task per stage, and the total number of tasks is the product of the number of partitions and stages. By monitoring the progress and execution time of tasks, you can identify bottlenecks and adjust your application to better utilize resources and improve performance.

Finally, we discussed Partitions, which are the fundamental units of data in Spark. Partitions determine how data is split and processed in parallel across the cluster. The number of partitions can have a significant impact on the performance and resource usage of your application. Understanding and adjusting the number of partitions based on your data and cluster resources can help optimize your application's performance.

In summary, understanding the relationships and interactions between Jobs, DAGs, Stages, Tasks, and Partitions is essential for writing efficient and scalable Spark applications. By applying the thumb rules and insights provided in this post, you can better analyze and optimize your Spark code, leading to improved performance, more efficient resource utilization, and ultimately, more successful data processing applications. As a developer, it is crucial to continue learning and experimenting with these concepts to gain a deeper understanding of how Spark works under the hood and how to harness its full potential.