PySpark is the Python API for Apache Spark, allowing users to write distributed data processing tasks using familiar Python syntax.
Purpose: It enables big data computation by splitting large datasets across a cluster and processing them in parallel.
Key Components:
- SparkSession: Main entry point for Spark functionality.
- RDDs: Low-level distributed datasets.
- DataFrames: High-level structured data abstraction similar to pandas.
- Catalyst Optimizer: Optimizes DataFrame and SQL operations for performance.
Example:
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("Example").getOrCreate()
df = spark.read.csv("data.csv", header=True, inferSchema=True)
df.show()Use Context: Used within environments such as Databricks, which manage Spark clusters and simplify distributed computing workflows.
pyspark.sql Module
Definition:
pyspark.sql is the core module for structured data in PySpark, supporting both DataFrame operations and SQL queries.
Key Features:
- Provides the DataFrame API for manipulating tabular data.
- Enables SQL queries through temporary views.
- Performs lazy evaluation and query optimization via Catalyst.
- Integrates with other Spark components like MLlib and Structured Streaming.
Why Important: Allows analysts and engineers to work with massive structured datasets efficiently using a familiar SQL-like interface.
PySpark vs pandas
| Feature | pandas | PySpark |
|---|---|---|
| Data scale | In-memory (single machine) | Distributed (cluster) |
| Execution | Eager (immediate) | Lazy (optimized plan) |
| Integration | Python only | SQL, MLlib, Streaming, Databricks |
| Failure handling | Limited | Fault-tolerant (lineage-based recovery) |
Analogy:
PySpark is like pandas + SQL, but distributed and scalable.
Usage Pattern:
- For local, small datasets → use pandas.
- For large-scale or production data → use PySpark.
SparkSession
Definition:
A SparkSession is the entry point for all Spark functionality in PySpark.
Responsibilities:
- Creates and manages DataFrames.
- Executes SQL queries.
- Connects to the cluster for distributed execution.
Example:
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("MyApp").getOrCreate()Importance: Without a SparkSession, you cannot use Spark DataFrames, read data, or run SQL queries.
PySpark Use Cases
- ETL and Data Cleaning
- Data Aggregation and Reporting
- Joining Large Datasets
- SQL Query Execution
- Machine Learning Preparation