Pyspark Wholesome Tutorial- Links to refer, PDfs

How does Pyspark works in general?

PySpark is the Python API for Apache Spark, an open-source, distributed computing system designed for large-scale data processing. It allows users to perform big data processing and analytics tasks using Python. Here’s a general overview of how PySpark works:

Core Concepts

  1. SparkContext (sc):
    • The entry point for any Spark functionality.
    • Represents the connection to a Spark cluster.
    • Responsible for managing the configuration, job execution, and task scheduling.
  2. SparkSession:
    • The main entry point for DataFrame and SQL functionality.
    • Encapsulates the SparkContext and provides methods to create DataFrames, read data, run SQL queries, etc.
  3. RDD (Resilient Distributed Dataset):
    • The fundamental data structure of Spark.
    • Represents an immutable distributed collection of objects that can be processed in parallel.
    • Supports two types of operations: transformations (e.g., map, filter) and actions (e.g., collect, count).
  4. DataFrame:
    • A higher-level abstraction of RDD.
    • Represents distributed data organized into named columns, similar to a table in a relational database.
    • Provides a domain-specific language for structured data manipulation.
    • Can be created from various data sources like CSV, JSON, Parquet, databases, etc.
  5. Transformations and Actions:
    • Transformations: Lazy operations that define a new RDD/DataFrame from an existing one (e.g., map, filter, join). These operations are not executed immediately.
    • Actions: Operations that trigger the execution of transformations and return a result to the driver or write data to an external storage system (e.g., collect, count, write).

Execution Flow

  1. Initialization:
    • Initialize a SparkSession which automatically creates a SparkContext.The SparkContext connects to the cluster manager (e.g., YARN, Mesos, Kubernetes, or standalone cluster) to allocate resources.
    from pyspark.sql import SparkSession spark = SparkSession.builder \ .appName("Example App") \ .getOrCreate()
  2. Data Loading:
    • Load data from various sources (HDFS, S3, local file system, databases, etc.) into DataFrames or RDDs.
    pythonCopy codedf ="path/to/file.csv", header=True, inferSchema=True)
  3. Transformations:
    • Apply transformations to DataFrames or RDDs to create new DataFrames or RDDs.
    pythonCopy codetransformed_df = df.filter(df["age"] > 30).select("name", "age")
  4. Actions:
    • Trigger the execution of the transformations by performing actions.
  5. Execution:
    • When an action is called, Spark constructs a directed acyclic graph (DAG) of stages and tasks.
    • The DAG scheduler breaks the job into stages based on data shuffling.
    • The task scheduler sends the tasks to the cluster nodes for execution.
    • Each worker node executes the tasks and performs the necessary computations.
  6. Result Collection:
    • After the execution, the results are collected and returned to the driver program or written to an external storage system.

Key Features

  • In-Memory Computation: Spark performs computations in memory, reducing the time spent reading and writing data to disk.
  • Lazy Evaluation: Transformations are lazily evaluated, meaning they are not executed until an action is called. This allows Spark to optimize the execution plan.
  • Fault Tolerance: RDDs are resilient and can recompute lost data using lineage information.
  • Scalability: Spark can scale from a single machine to thousands of cluster nodes.
  • Support for Various Data Sources: Spark can read from and write to a wide range of data sources, including HDFS, S3, HBase, Cassandra, and more.
  • Rich API: Provides high-level APIs in Java, Scala, Python (PySpark), and R.

Example Workflow

Here’s an example workflow that demonstrates the key steps in a PySpark application:

from pyspark.sql import SparkSession

# Initialize SparkSession
spark = SparkSession.builder.appName("PySpark Example").getOrCreate()

# Load data into a DataFrame
df ="path/to/file.csv", header=True, inferSchema=True)

# Perform transformations
filtered_df = df.filter(df["age"] > 30)
grouped_df = filtered_df.groupBy("occupation").count()

# Perform an action

# Stop the SparkSession

In this example:

  1. A SparkSession is initialized.
  2. Data is loaded from a CSV file into a DataFrame.
  3. Transformations are applied to filter and group the data.
  4. An action (show) is called to trigger the execution and display the results.
  5. The SparkSession is stopped to release the resources.

PySpark abstracts much of the complexity of distributed computing, allowing you to focus on data processing and analysis tasks using familiar Python syntax.

Explain Pyspark configuration step by step

Configuring PySpark involves setting up the environment and defining various parameters to optimize the performance and behavior of your Spark application. Here’s a step-by-step guide to PySpark configuration:

Step 1: Install PySpark

Before configuring PySpark, ensure it is installed. You can install it using pip:

bashCopy codepip install pyspark

Step 2: Initialize SparkSession

The entry point for using PySpark is the SparkSession. You can configure various parameters during its initialization.

pythonCopy codefrom pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName("MyApp") \
    .config("spark.some.config.option", "some-value") \

Step 3: Common Configuration Parameters

Here are some common configurations you might need to set:

  1. Application Name:pythonCopy codespark = SparkSession.builder.appName("MyApp").getOrCreate()
  2. Master URL: This specifies the cluster manager to connect to. For a local setup, use local, local[N], or local[*].pythonCopy codespark = SparkSession.builder.master("local[*]").getOrCreate()
  3. Memory and Cores: Configure the amount of memory and number of cores for the driver and executor.pythonCopy codespark = SparkSession.builder \ .config("spark.driver.memory", "4g") \ .config("spark.executor.memory", "4g") \ .config("spark.executor.cores", "2") \ .getOrCreate()
  4. Dynamic Allocation: Enable dynamic allocation to automatically adjust the number of executors based on the workload.pythonCopy codespark = SparkSession.builder \ .config("spark.dynamicAllocation.enabled", "true") \ .config("spark.dynamicAllocation.minExecutors", "1") \ .config("spark.dynamicAllocation.maxExecutors", "10") \ .getOrCreate()
  5. Shuffle Partitions: Adjust the number of partitions used for shuffling data.pythonCopy codespark = SparkSession.builder \ .config("spark.sql.shuffle.partitions", "200") \ .getOrCreate()
  6. Spark Logging Level: Set the logging level to control the verbosity of Spark logs.pythonCopy codespark.sparkContext.setLogLevel("WARN")

Step 4: Environment Variables

Some configurations can be set through environment variables before starting the Spark application.

  1. SPARK_HOME: Set the Spark installation directory.bashCopy codeexport SPARK_HOME=/path/to/spark
  2. PYSPARK_PYTHON: Set the Python interpreter to be used.bashCopy codeexport PYSPARK_PYTHON=python3
  3. PYSPARK_DRIVER_PYTHON: Set the Python interpreter for the driver node.bashCopy codeexport PYSPARK_DRIVER_PYTHON=python3
  4. Additional Configuration Files: You can specify additional configurations using spark-defaults.conf and

Step 5: Using Configuration Files

  1. spark-defaults.conf: This file contains default Spark configurations. Place it in $SPARK_HOME/conf/.text spark.master local[*] MyApp spark.executor.memory 4g spark.driver.memory 4g spark.sql.shuffle.partitions 200
  2. This file contains environment-specific configurations. Place it in $SPARK_HOME/conf/.sh export SPARK_MASTER_HOST= export SPARK_WORKER_CORES=2 export SPARK_WORKER_MEMORY=4g

Step 6: Running the Spark Application

After setting up the configurations, you can run your PySpark application. Here’s a simple example script:

from pyspark.sql import SparkSession

# Initialize SparkSession with configurations
spark = SparkSession.builder \
.appName("MyApp") \
.master("local[*]") \
.config("spark.driver.memory", "4g") \
.config("spark.executor.memory", "4g") \
.config("spark.sql.shuffle.partitions", "200") \

# Your Spark application code
data = [("Alice", 34), ("Bob", 45), ("Cathy", 29)]
columns = ["Name", "Age"]
df = spark.createDataFrame(data, columns)

# Stop the SparkSession

Save this script as and run it using:

bashCopy codepython

Step 7: Advanced Configurations

For more advanced configurations, you can refer to the Spark Configuration Guide.

In summary, configuring PySpark involves setting up the SparkSession with appropriate parameters, using environment variables, and optionally defining configurations in files like spark-defaults.conf and This ensures that your Spark application runs optimally based on the resources available and the nature of the tasks it performs.

A job execution in Pyspark .. step by step explanation of How and what happens

Understanding how a PySpark job is executed involves grasping the internal mechanisms of Spark’s architecture and its execution flow. Here’s a step-by-step explanation of what happens when you run a PySpark job:

Step 1: Initialization

  1. Creating SparkSession:
    • The entry point for a Spark application is creating a SparkSession. This session connects to a Spark cluster and sets up the necessary configurations.
    from pyspark.sql import SparkSession spark = SparkSession.builder \ .appName("Example App") \ .master("local[*]") \ .getOrCreate()
  2. Initializing SparkContext:
    • Internally, SparkSession creates a SparkContext which represents the connection to the cluster and initializes the Spark execution environment.

Step 2: Data Loading

  1. Reading Data:
    • Data is loaded into DataFrames or RDDs. Spark abstracts the data source and provides a unified interface for various formats (CSV, JSON, Parquet, etc.).
    df ="path/to/file.csv", header=True, inferSchema=True)

Step 3: Transformations

  1. Defining Transformations:
    • Transformations are operations on DataFrames/RDDs that define a new dataset from an existing one. These operations are lazy, meaning they build up a lineage of transformations but are not executed immediately.
    pythonCopy codefiltered_df = df.filter(df["age"] > 30) selected_df ="name", "age")

Step 4: Actions

  1. Triggering Actions:
    • Actions are operations that trigger the execution of the transformations and return results to the driver program or write data to an external storage system. Examples include show(), collect(), count(), write, etc.

Step 5: Job Execution

  1. Logical Plan:
    • When an action is called, Spark builds a logical plan. This plan describes the series of transformations that need to be applied to the data.
  2. Optimization:
    • Spark’s Catalyst optimizer takes the logical plan and optimizes it. This involves applying various optimization rules, such as predicate pushdown, constant folding, and projection pruning.
  3. Physical Plan:
    • The optimized logical plan is converted into a physical plan, which describes the actual execution steps. This plan includes details on how the data will be read, processed, and written.
  4. DAG Creation:
    • Spark constructs a Directed Acyclic Graph (DAG) of stages and tasks based on the physical plan. Each stage consists of tasks that can be executed in parallel.

Step 6: Task Scheduling

  1. Task Scheduling:
    • The DAG scheduler divides the job into stages based on data shuffling. Each stage is a set of tasks that can be executed concurrently. Tasks are the smallest unit of work in Spark, and each task is assigned to a partition of the data.
  2. Task Execution:
    • The task scheduler sends tasks to the executor nodes. Executors are worker processes responsible for executing the tasks and returning the results to the driver.

Step 7: Execution on Executors

  1. Task Execution:
    • Each executor runs the tasks on its partitions of the data. If a task involves reading data, the executor reads the data from the storage system (e.g., HDFS, S3, local file system).
  2. Shuffle Operations:
    • For operations requiring data to be redistributed across nodes (e.g., join, groupBy), Spark performs a shuffle. This involves sending data over the network from one executor to another.
  3. In-Memory Computation:
    • Executors perform computations in memory whenever possible to speed up processing. Intermediate results may be written to disk if they do not fit in memory.

Step 8: Collecting Results

  1. Result Collection:
    • After all tasks in a stage complete, the results are sent back to the driver program. For actions like collect(), the results are collected in the driver’s memory. For actions like write, the results are saved to the specified storage system.
    result = selected_df.collect() # Collects the results to the driver

Step 9: Post-Execution

  1. Resource Cleanup:
    • After the job completes, the resources allocated for the job (executors, memory, etc.) are released. This is automatically managed by Spark.
  2. Stopping SparkSession:
    • It is good practice to stop the SparkSession to release the resources explicitly.

Summary of Execution Flow

  1. Initialization: Create a SparkSession and SparkContext.
  2. Data Loading: Load data into DataFrames/RDDs.
  3. Transformations: Define transformations (lazy operations).
  4. Actions: Trigger actions to start execution.
  5. Job Execution: Spark constructs logical plans, optimizes them, creates physical plans, and builds a DAG.
  6. Task Scheduling: Tasks are scheduled and sent to executors.
  7. Execution on Executors: Executors run tasks, perform computations, and handle shuffling.
  8. Collecting Results: Results are collected and returned to the driver or written to storage.
  9. Post-Execution: Clean up resources and stop SparkSession.

This flow highlights the distributed nature of Spark and the lazy evaluation mechanism that optimizes and efficiently executes big data processing tasks.

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