PySpark, as part of the Apache Spark ecosystem, follows a master-slave architecture(Or Driver- Executor Architecture) and provides a structured approach to distributed data processing.

Here’s a breakdown of the PySpark architecture with diagrams to illustrate the key components and their interactions.

1. Overview of PySpark Architecture

The architecture of PySpark involves the following main components:

• Driver Program: The main control program that manages the entire Spark application. When the driver program executes, it calls the original program of the app and generates a Spark Context.
• Cluster Manager: Manages the resources and schedules tasks on the cluster. Examples include YARN, Mesos, or Spark’s standalone cluster manager.
• Workers: Execute tasks on the cluster. Each worker runs one or more executors.
• Executors: Run the tasks assigned by the driver on the worker nodes and store the data partitions.

2. Diagram of PySpark Architecture

Here’s a visual representation of the PySpark architecture:

+-------------------------------------------+
|                 Driver                    |
|  +-------------------------------------+  |
|  |           SparkContext              |  |
|  |                                     |  |
|  |  +-------------------------------+  |  |
|  |  |       Cluster Manager         |  |  |
|  |  |                               |  |  |
|  |  |  +------------+  +----------+ |  |  |
|  |  |  |   Worker 1  |  | Worker 2 | |  |  |
|  |  |  | +----------+|  |+--------+| |  |  |
|  |  |  | | Executor ||  || Executor|| |  |  |
|  |  |  | |          ||  ||         || |  |  |
|  |  |  +------------+  +----------+ |  |  |
|  |  |                               |  |  |
|  |  +-------------------------------+  |  |
|  +-------------------------------------+  |
+-------------------------------------------+

3. Components Explained

• Driver Program: The entry point for the Spark application. It contains the SparkContext, which is the main interface for interacting with Spark. Spark Context includes all the basic functions. You can assume Spark Context as a gateway to all Spark’s functionality. The driver is responsible for:
  • Creating RDDs, DataFrames, Datasets.
  • Defining transformations and actions.
  • Managing the lifecycle of Spark applications.
• Cluster Manager: Manages the cluster resources and schedules tasks. The SparkContext connects to the cluster manager to negotiate resources and submit tasks. The cluster manager works with the Spark Context and also manages the execution of various jobs inside the cluster. The cluster manager can be:
  • Standalone: Spark’s built-in cluster manager.
  • YARN: Hadoop’s resource manager.
  • Mesos: A distributed systems kernel.
• Workers: Nodes in the cluster that execute the tasks. Each worker node hosts one or more executors.
• Executors: Run on worker nodes and are responsible for:
  • Executing code assigned by the driver.
  • Storing data for in-memory processing and disk storage.
  • Reporting the status and results of computations back to the driver.

4. Detailed Diagram with Data Flow

Here’s a more detailed diagram showing the data flow and interaction between components:

+---------------------------+                +-----------------------+
|        Driver             |                |     Cluster Manager    |
|                           |                |                       |
|  +---------------------+  |                |  +------------------+ |
|  | SparkContext        |  |                |  | Resource Manager  | |
|  +---------+-----------+  |                |  +--------+---------+ |
|            |              |                |           |           |
|            v              |                |           v           |
|   +-------------------+   |                |  +------------------+ |
|   |     DAG Scheduler  |<-------------------->| Task Scheduler   | |
|   +---------+---------+   |                |  +--------+---------+ |
|             |              |                |           |           |
|             v              |                |           v           |
|  +----------+------------+ |                |  +------------------+ |
|  | Task Scheduler        |<-------------------->| Worker Manager   | |
|  +----------+------------+ |                |  +------------------+ |
|             |              |                |                       |
|             v              |                +-----------------------+
|  +----------+------------+
|  |     Executors         |
|  +-----------------------+
|                           |
+---------------------------+

                  |                  
                  v                  
            +----------------------------+
            | Worker Nodes               |
            |                            |
            |  +----------------------+  |
            |  |        Executor 1    |  |
            |  +----------------------+  |
            |  |        Executor 2    |  |
            |  +----------------------+  |
            |                            |
            +----------------------------+

Detailed Component Descriptions

• Driver Program:
  • SparkContext: Initializes Spark application, connects to cluster manager, and creates RDDs.
  • DAG Scheduler: Translates logical plans into a physical execution plan, creating a Directed Acyclic Graph (DAG) of stages.
  • Task Scheduler: Schedules tasks to run on executors, handles retries on failure.
• Cluster Manager:
  • Resource Manager: Manages cluster resources and assigns them to applications.
  • Task Scheduler: Assigns tasks to executors based on available resources.
• Worker Nodes:
  • Executors: Run the tasks, store the intermediate results in memory or disk, and communicate results back to the driver.

Data Flow

1. Submit Application: The driver program is submitted to the cluster.
2. Initialize SparkContext: SparkContext connects to the cluster manager.
3. Resource Allocation: Cluster manager allocates resources to the application.
4. Task Scheduling: Driver schedules tasks through the DAG scheduler and task scheduler.
5. Execution: Executors on worker nodes execute the tasks.
6. Data Storage: Intermediate results are stored in memory or on disk.
7. Completion: Executors return the results to the driver, which processes and provides the final output.

This architecture allows PySpark to efficiently process large-scale data in a distributed environment, leveraging the power of parallel computation and fault tolerance.

In Different Way:-

Here’s a breakdown of PySpark architecture using diagrams:

1. High-Level Overview:

+--------------------+         +--------------------+         +---------------------+
|       Driver       |         | Cluster Manager     |         | Worker Nodes (N)    |
+--------------------+         +--------------------+         +---------------------+
     |                     |         | (YARN, Mesos,       |         | (Executor Processes) |
     |                     |         | Standalone)        |         |                     |
     | Submits application |         +--------------------+         |                     |
     | and coordinates    |                                 |                     |
     | tasks              |                                 |   Spark Tasks       |
+--------------------+         +--------------------+         +---------------------+
     | (SparkContext)   |         |                     |         | (on each Executor) |
     |                     |         |                     |         |                     |
     |-----------------|         |                     |         |-----------------|
     |  Libraries (SQL,  |         |                     |         |  Data Processing   |
     |  MLlib, Streaming) |         |                     |         |   (RDDs, DataFrames) |
     |-----------------|         |                     |         |-----------------|

• Driver: The program running your PySpark application. It submits the application to the cluster manager, coordinates tasks, and interacts with Spark libraries.
• Cluster Manager: Manages resources in the cluster, allocating resources (machines) to applications like PySpark. Examples include YARN (Hadoop), Mesos, or Spark’s standalone mode.
• Worker Nodes: Machines in the cluster that run Spark applications. Each node has an Executor process that executes Spark tasks.

2. Data Processing Flow:

+--------------------+         +--------------------+         +---------------------+
|       Driver       |         | Cluster Manager     |         | Worker Nodes (N)    |
+--------------------+         +--------------------+         +---------------------+
     | Submits job     |         |                     |         | (Executor Processes) |
     | (transforms)     |         |                     |         |                     |
     |-----------------|         |                     |         |-----------------|
     |  SparkContext   |         |                     |         |  RDD Operations   |
     |-----------------|         |                     |         |   (map, filter, etc) |
     |  Transform Data  |         |                     |         |   (on each partition) |
     |  (RDDs)          |         |                     |         |-----------------|
     |-----------------|         |                     |         |  Shuffle & Aggregation |
     |   Shuffle Data   |         |                     |         |   (if needed)        |
     |   (if needed)     |         |                     |         |-----------------|
     |-----------------|         |                     |         |   Write Results    |
     |   Save Results   |         +--------------------+         |   (to storage)     |
     +--------------------+                                 +---------------------+

• The driver submits a Spark job with transformations to be applied to the data.
• SparkContext in the driver translates the job into tasks for each partition of the data.
• Executor processes on worker nodes execute these tasks on their assigned data partitions.
• Shuffling (data exchange) might occur between executors if operations require data from different partitions (e.g., joins).
• Finally, the results are written to storage or used for further processing.

3. Spark Libraries:

+--------------------+
|       Driver       | (imports libraries)
+--------------------+
     |
     |-----------------|
     |  SparkContext   |
     |-----------------|
     |  Spark SQL      |
     |  (DataFrame/SQL) |
     |-----------------|
     |  MLlib          |
     |  (Machine Learning)|
     |-----------------|
     |  Spark Streaming |
     |  (Real-time)    |
     |-----------------|

• PySpark provides various libraries accessible through the SparkContext:
  • Spark SQL: Enables SQL-like operations on DataFrames and Datasets.
  • MLlib: Offers machine learning algorithms and tools for building and deploying models.
  • Spark Streaming: Allows processing of continuous data streams.

These diagrams provide a visual representation of PySpark’s architecture, highlighting the key components and data processing flow. As you delve deeper into PySpark, these visuals can serve as a foundation for understanding its functionalities.

The Spark UI provides a web interface that gives insight into the execution of your Spark jobs. It’s a valuable tool for monitoring and debugging your Spark applications. The UI is accessible through a web browser at http://<driver-node>:4040 for a standalone Spark application, but the port may vary depending on your cluster configuration.

Here’s a breakdown of the key tabs available in the Spark UI:

1. Jobs

• Overview: This tab displays a list of all the Spark jobs that have been executed or are currently running.
• Key Information:
  • Job ID: A unique identifier for each job.
  • Description: A brief description of the job, often showing the operation performed.
  • Submitted: The time when the job was submitted.
  • Duration: How long the job took to run.
  • Stages: Number of stages in the job and their completion status.
  • Tasks: Number of tasks in the job and their status (e.g., succeeded, failed).

2. Stages

• Overview: Shows details about each stage of your Spark job. A stage corresponds to a set of tasks that can be executed together without shuffling.
• Key Information:
  • Stage ID: A unique identifier for each stage.
  • Description: Describes the operation performed in the stage.
  • Tasks: Number of tasks in the stage.
  • Input/Shuffle Read/Write: Amount of data read/written during the stage.
  • Duration: Time taken by the stage.
  • Aggregated Metrics: Detailed metrics like task time, GC time, input size, and more.

3. Tasks

• Overview: Provides detailed information about individual tasks within a stage.
• Key Information:
  • Task ID: A unique identifier for each task.
  • Launch Time: The time when the task was started.
  • Executor ID: The ID of the executor that ran the task.
  • Host: The node where the task was executed.
  • Duration: Time taken by the task.
  • GC Time: Time spent in garbage collection.
  • Input/Output Metrics: Detailed input/output data metrics, including the number of records read or written.

4. Storage

• Overview: Displays information about RDDs and DataFrames that are cached or persisted.
• Key Information:
  • RDD ID: Unique identifier for the RDD/DataFrame.
  • Name: The name of the cached RDD/DataFrame.
  • Storage Level: How the data is stored (e.g., MEMORY_ONLY, DISK_ONLY).
  • Size in Memory/On Disk: Amount of data stored in memory and/or on disk.
  • Partitions: Number of partitions and where they are stored.

5. Environment

• Overview: Shows the environment settings and configurations used by the Spark application.
• Key Information:
  • Runtime Information: Details about the Spark version, JVM version, Scala version, etc.
  • Spark Properties: Configuration properties (e.g., spark.executor.memory, spark.serializer).
  • Hadoop Properties: Configuration properties related to Hadoop and HDFS.
  • JVM Information: Details about the JVM settings.
  • Classpath Entries: List of all the libraries and their locations used by the Spark application.

6. Executors

• Overview: Provides detailed information about the executors running on the cluster.
• Key Information:
  • Executor ID: Unique identifier for each executor.
  • Host: The node on which the executor is running.
  • RDD Blocks: Number of RDD blocks stored on the executor.
  • Storage Memory: Memory used by the executor for storage.
  • Task Time: Total time spent by the executor in executing tasks.
  • Failed Tasks: Number of tasks that failed on this executor.
  • Logs: Links to the executor logs for further debugging.

7. SQL (for Spark SQL queries)

• Overview: Displays the details of SQL queries executed by the Spark SQL engine.
• Key Information:
  • Execution ID: A unique identifier for each SQL query.
  • Description: SQL query string or a description of the operation.
  • Duration: Time taken to execute the query.
  • Job IDs: Jobs associated with the query.
  • Physical Plan: A visualization of the physical execution plan of the query.

8. Streaming (for Spark Streaming jobs)

• Overview: Displays information related to Spark Streaming jobs.
• Key Information:
  • Batch Duration: The time interval of each batch in Spark Streaming.
  • Scheduling Delay: The delay between the scheduled and actual start of the batch.
  • Processing Time: Time taken to process each batch.
  • Total Delay: Sum of the scheduling and processing delays.
  • Input Rate: Rate at which data is being ingested.
  • Processing Rate: Rate at which data is being processed.

9. JDBC/ODBC Server (for SQL interactions via JDBC or ODBC)

• Overview: Displays the details of queries run through the Spark SQL Thrift Server, which enables connectivity with JDBC/ODBC clients.
• Key Information:
  • Session ID: Identifier for each JDBC/ODBC session.
  • Start Time: The time when the session/query started.
  • User: The user who initiated the query.
  • Statement: The SQL query statement executed.

10. Structured Streaming

• Overview: Provides insights into Structured Streaming queries, their status, and performance metrics.
• Key Information:
  • Query Name: Name of the streaming query.
  • Batch ID: Identifier for each processed micro-batch.
  • Input Rows: Number of rows ingested in the current batch.
  • Processing Time: Time taken to process the batch.
  • Watermark: The watermark time used for event-time processing.

The Spark UI is a comprehensive tool for monitoring, troubleshooting, and optimizing Spark applications. Each tab provides detailed insights into various aspects of Spark’s execution, from high-level job summaries to granular details about tasks, storage, and SQL queries. By using the Spark UI, you can gain a better understanding of your application’s performance and identify areas for improvement.

Q1:-What do we mean by Driver Program in Pyspark architecture. what is the specific code needed to start it? On Which system it executes!

In PySpark (or Spark in general), the Driver Program is a central component that coordinates the execution of a Spark application. It is responsible for:

1. Converting the user code into tasks that can be executed across the cluster.
2. Managing the cluster resources, i.e., coordinating with worker nodes (executors) to distribute and execute the tasks.
3. Maintaining SparkContext and other key structures like the DAG (Directed Acyclic Graph) of tasks.

Driver Program in PySpark Architecture:

The Driver Program in PySpark is typically the entry point of your application, and it contains the user code that defines the logic of the application. It runs on a single node and is responsible for:

1. Job Scheduling: It splits the user code into stages and tasks and submits them to the executors for processing.
2. Task Coordination: The driver program receives task results from the executors and tracks the progress of job execution.
3. Resource Management: The driver interacts with the Cluster Manager (like YARN, Mesos, or Kubernetes) to request resources, i.e., the number of executors and their configurations (cores, memory).

What is Executed in the Driver Program?

1. User Application Code: The PySpark script or notebook code you write is executed on the driver node. This includes operations such as transformations (map, filter) and actions (collect, count) that are called on Spark DataFrames or RDDs.
2. SparkSession and SparkContext: The driver creates and holds the SparkSession and SparkContext objects, which act as the gateway to the Spark cluster.
   • SparkContext: This is the entry point to the cluster and is used to interact with Spark.
   • SparkSession: In PySpark 2.x and later, this is the unified entry point to work with structured data (DataFrames).
3. DAG (Directed Acyclic Graph): The driver constructs a DAG of stages and tasks from the transformations and actions you define in your code.
4. Task Execution: The driver program does not perform data processing itself; instead, it sends tasks to executors that run on worker nodes and collect results back from them.

How the Driver Program Starts:

The driver program is started when you submit your PySpark job or start a PySpark session, such as:

• Submitting a PySpark script: If you run a Python script (e.g., spark-submit my_pyspark_job.py), the driver program will be started on the node where the script is submitted (or on a node allocated by the cluster manager).
• Running in an interactive shell: If you use a PySpark interactive shell (e.g., running pyspark), the driver program is created on the node where the PySpark shell is started.
• Notebooks: In environments like Jupyter notebooks or Databricks, the driver program is started on the cluster master or the head node, and it interacts with worker nodes.

Where Does the Driver Program Execute?

• Standalone Mode: If you’re running Spark in local mode or in a standalone cluster, the driver runs on the same machine where you submit the job.
• Cluster Mode:
  • YARN Cluster Mode: The driver can be deployed on a separate master node allocated by the resource manager (YARN), and it communicates with the executors running on worker nodes.
  • YARN Client Mode: The driver runs on the machine where the job was submitted (e.g., your local machine), while the executors run on the cluster nodes.
  • Kubernetes Cluster Mode: The driver is deployed as a pod in the Kubernetes cluster.

Driver Program Workflow:

Here’s a typical workflow of the driver program:

1. Job Submission: You submit a PySpark job using spark-submit, PySpark shell, or a Jupyter notebook.
2. Driver Initialization:
   • The driver creates a SparkSession and SparkContext, which connects to the cluster manager (e.g., YARN or Kubernetes) to request resources.
3. DAG Creation:
   • The driver reads the user code and creates a DAG (Directed Acyclic Graph) of tasks. The DAG represents the sequence of transformations (e.g., map, filter, groupBy) and actions (e.g., collect, count) in the Spark job.
4. Task Scheduling:
   • The driver breaks down the DAG into stages and tasks, and it schedules these tasks to be executed on executors on the worker nodes.
5. Task Distribution:
   • The driver sends the tasks to executors and monitors their progress. The tasks run in parallel on the worker nodes, and the results are collected back to the driver.
6. Result Collection:
   • The driver collects and aggregates the results from executors once all the tasks are completed.

Driver Execution Modes:

1. Client Mode:
   • In client mode, the driver program runs on the machine where the user submits the job (e.g., local machine or notebook).
   • The executors run on the cluster nodes.
   • Use Case: Useful for development and interactive workloads because it provides quick feedback.
2. Cluster Mode:
   • In cluster mode, the driver runs on a node in the cluster, not on the machine that submitted the job.
   • The cluster manager (YARN, Kubernetes, etc.) decides where to run the driver.
   • Use Case: Ideal for production jobs, as the driver runs within the cluster and doesn’t depend on the user’s machine.

Driver Failures:

• If the driver program fails (due to memory issues, crashes, etc.), the entire Spark job fails. That’s why it’s important to allocate enough memory and resources to the driver, especially when handling large amounts of metadata or shuffles.

Code Example of Driver Logic:

from pyspark.sql import SparkSession

# SparkSession is created in the driver program
spark = SparkSession.builder \
    .appName("Example") \
    .getOrCreate()

# SparkContext is created in the driver
sc = spark.sparkContext

# Driver program defines transformations and actions
df = spark.read.csv("/path/to/file.csv")
filtered_df = df.filter(df["column"] > 100)

# Action triggers job execution
result = filtered_df.collect()  # The driver program collects results from executors

# Stop the SparkSession (and the driver program)
spark.stop()

Summary of Driver Program:

• Role: The driver program is the controller of a Spark job. It defines the logic of the job, creates and manages the DAG, coordinates task execution, and collects the results from the executors.
• Startup: The driver is started when you run a PySpark job, shell, or notebook. It runs either on the local machine (client mode) or a node in the cluster (cluster mode).
• Execution: The driver runs your PySpark code, builds the execution plan, schedules tasks, and monitors the progress of the job.
• Cluster Mode: In cluster mode, the driver runs on the master or a dedicated node, while the executors run on worker nodes.

DAG Schedular and Task Schedular . when are these called or created and what are the roles of these?

In the Spark architecture, both the DAG Scheduler and the Task Scheduler play key roles in how jobs are executed on a cluster. They manage the flow of tasks and the orchestration of execution across the cluster.

1. DAG Scheduler:

The DAG Scheduler is responsible for the high-level scheduling of stages in the job. It translates the user-defined operations in the Spark application into stages and tasks that can be executed in parallel.

When is the DAG Scheduler called?

• The DAG Scheduler is invoked whenever an action (such as count(), collect(), or save()) is called in the Spark job.
• When an action is triggered, the DAG Scheduler is responsible for breaking the logical execution plan (a Directed Acyclic Graph or DAG) into stages and scheduling them for execution.

Role of the DAG Scheduler:

1. Stage Creation: The DAG Scheduler breaks the job’s logical execution plan into stages. A stage represents a set of transformations (like map, filter, etc.) that can be executed without requiring a shuffle (i.e., a boundary between two stages is usually a shuffle).
   • Each stage consists of narrow transformations (like map, filter, flatMap), which can be executed without data movement across partitions.
2. DAG Construction: It builds a DAG (Directed Acyclic Graph) of stages. Each node in the DAG represents a stage, and the edges represent dependencies between stages.
   • Stages are executed sequentially based on their dependencies (i.e., later stages can only run once earlier stages are completed).
3. Handling of Failures: If a task or stage fails, the DAG Scheduler attempts to retry the failed tasks or stages. If recovery is not possible, the job will fail.
4. Scheduling Stages: Once the DAG is constructed, the DAG Scheduler submits each stage to the Task Scheduler for execution on the cluster.

Example:

If your Spark application has the following transformations:

df = spark.read.csv("data.csv")
df_filtered = df.filter(df['value'] > 10)
df_aggregated = df_filtered.groupBy("category").sum("value")
df_aggregated.show()

The DAG Scheduler will:

• Split the job into stages based on transformations:
  • Stage 1: Reading data and filtering it (map/filter).
  • Stage 2: Aggregating the filtered data (a shuffle happens here, causing a stage boundary).

The DAG Scheduler constructs the DAG and then submits each stage to the Task Scheduler.

2. Task Scheduler:

The Task Scheduler is responsible for distributing the tasks of each stage across the worker nodes (executors) in the cluster. Once the DAG Scheduler has divided the job into stages, the Task Scheduler manages the execution of individual tasks within those stages.

When is the Task Scheduler called?

• The Task Scheduler is invoked when the DAG Scheduler submits a stage for execution.
• The Task Scheduler is responsible for converting each stage into a set of parallel tasks and assigning them to the available executors on the worker nodes.

Role of the Task Scheduler:

1. Task Execution: The Task Scheduler divides each stage into tasks, where each task operates on a single partition of the data. The number of tasks in a stage equals the number of partitions in the RDD or DataFrame.
2. Resource Allocation: It interacts with the cluster manager (e.g., YARN, Kubernetes, Mesos) to allocate executors and resources (CPU, memory) for executing the tasks. Each task is assigned to a core on an executor.
3. Task Assignment to Executors: It schedules tasks on the available executors based on the available cores and memory. Tasks are sent to executors in a way that aims to minimize data movement (i.e., tasks are sent to executors where the data is already located, if possible—data locality).
4. Handling Task Failures: If a task fails, the Task Scheduler is responsible for retrying the task (a configurable number of times). If the task fails repeatedly, the stage will be marked as failed.
5. Task Progress Monitoring: The Task Scheduler tracks the status of each task (whether it is running, finished, or failed). It communicates task progress back to the driver.
6. Completion of Stage: Once all tasks within a stage are completed, the Task Scheduler informs the DAG Scheduler, which then moves on to the next stage (if there is one).

Example:

Continuing from the example above:

• The DAG Scheduler splits the job into two stages.
• For Stage 1, if the data is partitioned into 100 partitions, the Task Scheduler will schedule 100 tasks (one task per partition) and send these tasks to the available executors in the cluster.
• Once Stage 1 completes, the Task Scheduler moves on to executing the tasks of Stage 2.

How They Work Together:

1. DAG Scheduler:
   • Translates the high-level operations (like map, filter, groupBy) into stages and submits these stages to the Task Scheduler for execution.
   • It handles stage-level fault tolerance by retrying stages if there are failures.
2. Task Scheduler:
   • Once the DAG Scheduler submits a stage, the Task Scheduler splits that stage into tasks and assigns them to executors for parallel execution.
   • It handles task-level scheduling and task-level fault tolerance.

High-Level Flow:

1. User Submits Action:
   • When an action (e.g., count(), collect()) is called, the driver constructs a logical execution plan.
2. DAG Scheduler:
   • The DAG Scheduler breaks the execution plan into stages, each representing a set of transformations that can be executed in parallel.
   • The DAG is created, and each stage is submitted to the Task Scheduler.
3. Task Scheduler:
   • The Task Scheduler splits each stage into multiple tasks, one for each partition of the data.
   • Tasks are sent to executors on worker nodes for parallel execution.
   • The Task Scheduler tracks the progress of each task and retries failed tasks.
4. Task Execution on Executors:
   • The executors process the tasks on the worker nodes and return the results to the driver.

Summary:

• DAG Scheduler:
  • When: Called when an action is triggered.
  • Role: Breaks down the logical execution plan into stages and submits these stages to the Task Scheduler.
• Task Scheduler:
  • When: Called after the DAG Scheduler submits a stage.
  • Role: Divides the stage into tasks, distributes them across the cluster, monitors execution, and handles failures at the task level.

Together, the DAG Scheduler and Task Scheduler coordinate the parallel processing and execution of jobs in a Spark cluster. The DAG Scheduler focuses on dividing the job into stages, while the Task Scheduler manages the distribution and execution of individual tasks on the worker nodes.

How the stages are decided? What is the Stage Boundry?

In Spark, stages are logical units of execution that are formed based on transformations applied to a DataFrame or RDD. A stage is composed of a series of narrow transformations (operations that don’t require data movement across nodes) that can be executed without requiring data to be shuffled. A stage boundary is created whenever a wide transformation occurs, which involves shuffling data across the network.

What is Shuffling?

• Shuffling is the process of redistributing data across partitions, typically because a wide transformation (like groupBy, join, or reduceByKey) requires data to be grouped or combined in a specific way.
• Shuffling involves moving data between partitions across different worker nodes, which is an expensive operation in terms of both time and resources (network and disk I/O).

Stages and Shuffling:

1. Narrow Transformations: These transformations (e.g., map, filter, flatMap) do not require moving data between partitions. These operations can be pipelined within the same stage.
   • Example: If you’re applying map and filter transformations, they will be grouped together into the same stage because they operate independently on each partition.
2. Wide Transformations: These transformations (e.g., groupBy, reduceByKey, join) require shuffling data between partitions. This means the output of one transformation needs to be moved across nodes for the next operation to proceed. When a wide transformation happens, it causes a stage boundary.
   • Example: A groupBy transformation will require a shuffle because Spark needs to collect all the data for a particular key into the same partition to compute the result.

When Does a Stage Boundary Occur?

A stage boundary is created before shuffling happens. This means:

• The DAG Scheduler detects that a shuffle is required when encountering a wide transformation and splits the job into two stages:
  • Stage 1: Executes all narrow transformations up to the point where the wide transformation occurs.
  • Stage 2: The wide transformation creates the boundary and requires data to be shuffled. Once the shuffle is complete, Stage 2 executes.

How Stages Are Decided:

1. Narrow Transformations in the Same Stage:
   • Narrow transformations (e.g., map, filter, flatMap) are grouped together in a single stage. These transformations can be executed without data movement, as they only require access to the data within the same partition.
2. Wide Transformations Create Stage Boundaries:
   • A wide transformation (e.g., groupByKey, join, reduceByKey) requires data from multiple partitions to be reshuffled (moved across nodes). As soon as a wide transformation is encountered, the DAG Scheduler creates a new stage boundary.
   • Data is shuffled across the network between these stages.
3. Stages are Decided Based on Dependencies:
   • Narrow Dependency: A narrow dependency is when each partition of the parent RDD is used by at most one partition of the child RDD. These are pipelined within the same stage.
   • Wide Dependency: A wide dependency is when multiple partitions in the child RDD depend on the same partition in the parent RDD. This triggers a shuffle, and Spark creates a new stage.

Example of Stage Creation:

Let’s consider an example:

df = spark.read.csv("data.csv")
df_filtered = df.filter(df['value'] > 10)
df_mapped = df_filtered.withColumn("new_column", df['value'] * 2)
df_grouped = df_mapped.groupBy("category").sum("new_column")
df_grouped.show()

• Stage 1:
  • This stage will contain the filter and map operations (filter and withColumn), which are narrow transformations. These operations will be executed within the same stage because they operate independently on each partition, with no data movement required across partitions.
• Stage 2:
  • When the groupBy transformation is encountered, Spark realizes that it needs to shuffle the data across partitions to group the data by the category column. This creates a stage boundary.
  • Stage 1 will process the data and produce output that needs to be shuffled.
  • Stage 2 starts after the shuffle, which redistributes the data across nodes so that all rows with the same category end up in the same partition.

Flow of Execution:

1. Stage 1 (Before Shuffle):
   • Narrow transformations (filter, map) are executed.
   • The output is partitioned and prepared for the shuffle.
2. Shuffle:
   • The data is shuffled, meaning rows are redistributed across partitions based on the category column. This process includes sending data over the network to ensure all the rows with the same key (category) are in the same partition.
3. Stage 2 (After Shuffle):
   • The groupBy operation is executed on the shuffled data.
   • Tasks for this stage run on partitions where the shuffled data has been placed.

Important Points about Shuffling and Stages:

• Shuffling Happens Between Stages: The shuffle happens after Stage 1 completes, and it prepares data for Stage 2. The output from the shuffle becomes the input for the next stage.
• Stage Boundary Happens Before Shuffling: Spark creates a stage boundary when it encounters a wide transformation (e.g., groupBy), which necessitates a shuffle.
• Stages are Independent: Each stage is independent. After one stage completes and data is shuffled (if required), the next stage begins.
• Wide Transformation (Shuffle): A shuffle operation will result in data movement across the network, leading to the creation of a new stage. Examples of wide transformations that require a shuffle include:
  • groupByKey()
  • reduceByKey()
  • join()
  • distinct()
  • repartition()
  • coalesce()

When Does a Shuffle Happen?

A shuffle happens when Spark needs to redistribute data across partitions due to a wide transformation. This shuffle involves:

1. Writing intermediate data to disk.
2. Sending this data across the network to other nodes.
3. Reading the shuffled data into the correct partitions.

Summary of Stage Boundaries and Shuffling:

• Stages are divided based on the need for shuffling.
• Narrow transformations are grouped into the same stage, while wide transformations (that require a shuffle) create a new stage boundary.
• A shuffle happens between stages, not within a stage. Once a shuffle is triggered by a wide transformation, a new stage begins after the shuffle has completed.