SQL vs PySpark: Understanding the Difference in Data Processing

As businesses generate and rely on vast amounts of data, choosing the right tool to process it efficiently becomes critical. SQL and PySpark are two popular options, but they differ significantly in how they handle data, especially at scale. In this blog, we will explore their differences with a practical example and show why PySpark often outperforms traditional SQL for big data processing.

What Are SQL and PySpark?

• SQL (Structured Query Language): A standard language used to manage and query data in relational databases. SQL works efficiently for smaller datasets or on single-node systems.
• PySpark: The Python API for Apache Spark, a distributed data processing engine. PySpark is designed to handle large-scale data processing across multiple nodes in a cluster.

Scenario

Let’s compare SQL and PySpark using the following example:

• Data Setup:
  • Customers Table: 1 million records
  • Orders Table: 10 million records
  • Products Table: 1 million records
• Task: Find customers who purchased electronics after January 1, 2023.

This involves:

1. Filtering data (based on date and category).
2. Joining three tables (‘Customers,’ ‘Orders,’ and ‘Products’).

How SQL Processes the Query

SQL Query:

SELECT c.CustomerName, o.OrderID, p.ProductName
FROM Customers c
JOIN Orders o ON c.CustomerID = o.CustomerID
JOIN Products p ON o.ProductID = p.ProductID
WHERE o.OrderDate >= '2023-01-01'
  AND p.Category = 'Electronics';

SQL Execution Steps:

1. Load All Data:
   • SQL loads all data into memory before processing.
   • Imagine picking up all notebooks in the shop even if you only need a few specific ones.
   • Total: 12 million rows.
2. Filter Later:
   • SQL filters data (electronics + recent orders) after loading and joining.
   • This makes it slower because unnecessary data is handled in the early steps.
3. Joins:
   • SQL combines data step by step:
     • First, combine Customers and Orders.
     • Then, combine the result with Products.
   • Joins create large temporary datasets, which take time.
4. Output Results:
   • Finally, SQL gives you the filtered result: e.g., 50,000 rows (filtered records of electronics orders).
5. Time Taken: ~14 minutes (because everything is done one step at a time on a single machine).

Why it’s Slow: SQL handles the entire dataset at once and processes joins sequentially, often leading to bottlenecks on a single machine.

How PySpark Processes the Query

PySpark Code:

customers = spark.read.csv("customers.csv", header=True)
orders = spark.read.csv("orders.csv", header=True)
products = spark.read.csv("products.csv", header=True)

filtered_orders = orders.filter("OrderDate >= '2023-01-01'")
filtered_products = products.filter("Category = 'Electronics'")

joined_data = (
    filtered_orders
    .join(customers, "CustomerID")
    .join(filtered_products, "ProductID")
)

result = joined_data.select("CustomerName", "OrderID", "ProductName")
result.show()

PySpark Execution Steps:

1. Load Only What’s Needed:
   • PySpark works smarter by loading only filtered data.
   • For example:
     • Orders after 2023 → 500,000 rows (not 10 million).
     • Electronics → 100,000 rows (not 1 million).
   • This is like picking just the specific notebooks you need from the shop.
2. Filter Early:
   • PySpark applies filters before joining.
   • This reduces the amount of data handled in later steps, making it much faster.
3. Distributed Joins:
   • PySpark splits the work across many computers.
   • Each computer processes a smaller chunk of the data, then combines the results.
   • This is like having multiple workers handle small sections of the shop simultaneously.
4. Output Results:
   • The final result: 50,000 rows (filtered electronics orders).
5. Time Taken: ~4 minutes (because multiple computers work together efficiently).

Why it’s Fast: PySpark leverages distributed computing, filters early, and avoids handling unnecessary data.

Step	SQL (Traditional)	PySpark (Distributed)
Data Loading	Load all 12M rows.	Load filtered 600K rows.
Filter Application	Filters applied late.	Filters applied early.
Joins	Sequential processing.	Parallel, distributed.
Execution Time	~14 minutes.	~4 minutes.

Conclusion

Both SQL and PySpark have their use cases. For small datasets or quick queries, SQL is sufficient. However, for large-scale data processing, PySpark is the clear winner due to its distributed architecture and advanced optimizations.

Choosing between SQL and PySpark depends on your data size, infrastructure, and specific requirements. If your business deals with massive datasets regularly, adopting PySpark can save time and resources.