Why foreach() is called an action

n Apache Spark, the foreach() operation is considered an action because it triggers the actual execution of the Spark job and produces side effects, such as writing data to external storage systems, printing output to the console, or modifying data outside the Spark ecosystem.

To understand this, it’s essential to grasp the difference between transformations and actions in Spark:

1. Transformations:
   • These are operations that return a new RDD (Resilient Distributed Dataset) or DataFrame by applying a function to the elements of the existing one.
   • Transformations are lazy, meaning they don’t trigger execution immediately. Instead, Spark builds an execution plan and only performs computation when an action is invoked.
   • Examples: map(), filter(), flatMap(), etc.
2. Actions:
   • Actions trigger the execution of the transformations (i.e., Spark will begin processing the data).
   • They return results or perform operations that produce side effects.
   • Examples: collect(), count(), save(), foreach(), etc.

Now, regarding the foreach() method:

• foreach() is an action because it applies a specified function to each element of an RDD or DataFrame. It does not return a transformed dataset, but instead performs side effects like printing data to the console or saving it to an external system (e.g., a database or a file).
• Why is it an action?:
  • When you call foreach(), Spark sends tasks to the workers that execute the function on the data distributed across the cluster.
  • The result of foreach() is typically a side-effect operation (like printing or writing to an external store), not a new RDD/DataFrame. Therefore, it forces the computation to actually happen and the results to be processed.

To delve deeper into how the foreach() action works in Apache Spark, it’s helpful to explore not just the concept but also what happens internally when you call foreach().

Example 1: Basic foreach() with an RDD

Let’s first start with an RDD and look at a simple example of how foreach() works.

rdd = sc.parallelize([1, 2, 3, 4])
rdd.foreach(lambda x: print(x))

What happens internally?

1. RDD Creation:
   • The rdd is created using sc.parallelize([1, 2, 3, 4]), which splits the data across different partitions (typically determined by the number of available cores or nodes in the cluster).
   • Spark’s RDD abstraction allows parallelized data processing by distributing the dataset across multiple nodes.
2. Calling foreach():
   • The foreach() action triggers execution. Unlike transformations (like map() or filter()), Spark will not immediately execute the operation. Instead, it builds a DAG (Directed Acyclic Graph) of tasks.
3. Task Scheduling:
   • The function passed to foreach() (in this case, the lambda x: print(x)) needs to be executed on each partition in the cluster.
   • For each partition, Spark will launch a task (a unit of work). The task will execute the function (in this case, print(x)), one element at a time.
4. Execution on Worker Nodes:
   • Spark then sends each partition’s tasks to the worker nodes (distributed workers in the cluster). These workers process the elements locally within their partitions.
5. Side Effect:
   • The foreach() method performs a side effect (in this case, printing to the console). This means that no new RDD is created after the foreach() call. Instead, the actions performed in foreach() affect the external world (like the console or a file system).
   • Side effects like printing don’t get returned to the driver program; the worker nodes simply execute the action without giving any output back to the driver.
6. Completion:
   • After the worker nodes complete their tasks, the job is considered finished. There is no result returned to the driver (as opposed to something like collect() or count(), which returns data to the driver).
   • Once all tasks finish, the job is marked as complete, and Spark’s task scheduler handles fault tolerance, retries, and other cluster-level optimizations.

Example 2: Writing to an External Data Store

Let’s consider another example where foreach() writes to a file instead of just printing values.

rdd = sc.parallelize([1, 2, 3, 4])
rdd.foreach(lambda x: open('output.txt', 'a').write(f'{x}\n'))

What happens internally?

1. RDD Creation:
   • Just like in the previous example, the RDD is split across different partitions, allowing parallel processing.
2. Calling foreach():
   • The function (lambda x: open('output.txt', 'a').write(f'{x}\n')) is passed to each partition. For every partition, the task will execute the function on the data it holds.
3. Task Execution on Worker Nodes:
   • Each worker node receives a subset of the data and runs the function to append values to the output file. Here’s what’s interesting:
     • Multiple workers can be writing to the same file. This may cause issues like overwriting data or inefficient writes if the file is not managed carefully.
     • No returns: The result of writing to the file doesn’t come back to the driver. There is no returned dataset.
4. Side Effects:
   • In this case, the side effect is writing data to the file. Even though the foreach() operation executes a function across partitions, no new RDD is created or returned after the action is performed.
   • Side effect (writing data to external systems) occurs independently on each partition without informing the driver about the outcome.

How foreach() Works Under the Hood

Internally, the key components that are involved in foreach() are:

1. RDD Partitioning:
   • The dataset is split into partitions. Spark determines the number of partitions based on the cluster size or default configurations.
   • Each partition holds a subset of the data.
2. Task Creation:
   • For each partition, a task is created to apply the given function (in foreach()) to the elements of the partition.
   • These tasks are scheduled and dispatched to worker nodes.
3. Task Execution:
   • Each worker node executes the function on the data within its assigned partition. The function may have side effects, but it does not alter the data on the Spark side (i.e., it doesn’t return new RDDs).
4. Driver and Worker Communication:
   • Once the tasks are finished, the worker nodes report back to the driver with status updates (success, failure, etc.), but no data is returned to the driver from the foreach() operation.
5. Fault Tolerance:
   • Spark handles any task failure by retrying the task on another worker if needed. This ensures that even if some tasks fail, the overall job can complete successfully.

Why is foreach() Considered an Action?

• Triggers Execution:
  • Unlike lazy transformations, foreach() is an eager action that triggers Spark to run the computation immediately.
• No New RDD:
  • It doesn’t return a new RDD, and that’s why it’s not a transformation. Instead, it performs an operation with side effects on the existing data.
• Side Effects:
  • Since foreach() produces side effects, the function you pass into it will affect the external environment (e.g., writing to a file or printing to the console).
• Execution Pipeline:
  • foreach() initiates the execution pipeline: it schedules tasks, performs the operation, and communicates with worker nodes to process the data.

Conclusion

In summary, foreach() is an action in Apache Spark because it triggers execution on the worker nodes, performs operations with side effects, and does not return any new RDD or DataFrame. It’s a way to interact with external systems or perform computations that don’t necessarily need to return data but instead affect something outside of Spark (like printing or saving results).