Where PySpark and SparkSQL Fit Best in the Enterprise

[jmeulema]

• 1. Context
• 2. Performance Differences Between SparkSQL and PySpark DataFrame API
• 3. Functional Differences Between SparkSQL and PySpark
• 4. Additional Considerations Based on Real-World Usage
• 5. Conclusion

 

1. Context

When building a data architecture, a common question comes up: Should data engineers use PySpark for the bronze/silver layers, while data analysts rely on SparkSQL for silver/gold?

On the surface, this split makes sense, PySpark is powerful for complex transformations, while SparkSQL provides a simpler, declarative interface for analysts. But before making that call, two key factors need to be considered:

• Performance: Are there noticeable differences in execution speed between SparkSQL and PySpark?
• Functional: Does PySpark offer capabilities that SparkSQL lacks?

This blog dives into these questions to help determine the best approach for different personas in a Databricks environment, all within the Medallion Architecture framework, which organizes data into bronze, silver, and gold layers to improve quality and accessibility.

2. Performance Differences Between SparkSQL and PySpark DataFrame API

Both SparkSQL and the PySpark DataFrame API leverage the same Catalyst Optimizer, producing identical execution plans for equivalent queries. This means that, in theory, there should be no intrinsic performance differences between the two APIs. However, several practical factors can influence perceived performance:

1. Expression of Operations:
1. Performance differences arise not from the APIs themselves, but from how transformations are expressed.
2. Poorly structured PySpark DataFrame operations (e.g., unnecessary .collect(), using Python UDFs instead of built-in functions) can impact performance.
3. Similarly, inefficient SQL queries (e.g., complex subqueries instead of joins) can lead to suboptimal execution plans.
2. Declarative vs. Imperative Nature:
1. SQL is purely declarative, which can allow the optimizer to infer intent earlier and apply optimizations more effectively.
2. The PySpark DataFrame API is more imperative, meaning developers might introduce inefficiencies if they don’t structure their transformations optimally.
3. Perceived Performance Differences:
1. Generally, it is widely accepted that DataFrame operations are not inherently slower than SQL.
2. Differences are typically due to how developers write their queries, rather than fundamental API limitations.

Summary
For equivalent transformations, both SparkSQL and PySpark DataFrame API perform the same. The choice between them should be based on:

• Personal preference
• Ease of expressing complex transformations
• Integration with other tools (e.g., Python for ML workflows, SQL for analyst-friendly queries)

 

3. Functional Differences Between SparkSQL and PySpark

While both APIs share the same execution engine, they differ significantly in usability, flexibility, and testing capabilities.

Feature	PySpark DataFrame API	SparkSQL
Execution Engine	✅ Uses Catalyst Optimizer	✅ Uses Catalyst Optimizer
Performance	🔄 Equivalent to SparkSQL for the same logic	🔄 Equivalent to PySpark for the same logic
Unit Testing	✅ Supported (e.g., pytest, unittest, mocking DataFrames)	❌ Not directly supported (SQL queries are harder to test in isolation)
Code Reusability	✅ Can write reusable transformation functions in Python	❌ SQL queries are less modular and harder to reuse
Error Handling & Debugging	✅ Easier with Python’s exception handling	❌ Debugging SQL errors can be harder due to limited stack traces
Complex Transformations	✅ Easier (e.g., UDFs, loops, business logic)	❌ Harder to express in pure SQL
Interoperability	✅ Can integrate with external Python libraries (e.g., ML, Pandas)	❌ Limited to Spark SQL functions
Performance Optimization	🔄 Equivalent, but DataFrames provide more control via partitioning and caching	🔄 Equivalent, but SQL allows the optimizer to infer intent earlier

 

4. Additional Considerations Based on Real-World Usage

• Unit Testing in SQL: Since SQL is harder to unit-test in isolation, organizations relying heavily on SQL-based transformations should explore frameworks for SQL unit testing. Examples are
• Dbt-unit-testing
• pytest with Databricks SQL connector.
• DQX is a data quality framework for Apache Spark that enables you to define, monitor, and react to data quality issues in your data pipelines. https://databrickslabs.github.io/dqx/docs/guide/
• Hybrid Approach: While SparkSQL is preferred for its readability and analyst empowerment, complex transformations can still be handled using Python functions within SparkSQL (e.g., registering Python UDFs in SparkSQL, as outlined in this article).
• Concurrency: If your workload requires higher concurrency, such as multiple users running similar queries, Databricks SQL (DBSQL) is the most efficient choice. However, for ETL-style workloads, a job cluster is likely the better fit. Both options support Photon, enhancing cost efficiency and accelerating query performance for data transformations.
• Analyst Empowerment: Organizations have found that using SparkSQL for ETL increases transparency. Analysts can trace how transformations occur within jobs without needing Python expertise, reducing daily inquiries about ETL logic.

 

5. Conclusion

• Data Engineers (Bronze → Silver): PySpark is preferable due to its unit testing capabilities, better debugging tools, and transformation flexibility.
• Data Analysts (Silver → Gold): SparkSQL is a natural fit as it is easier for SQL-savvy analysts to use and performs equally well.
• For Maintainability & Testability: PySpark is the better option.
• For Ad-hoc Analysis & Readability: SparkSQL is ideal.

Ultimately, organizations can leverage a hybrid approach, balancing the power of PySpark for transformation logic with the accessibility of SparkSQL for analytics and discovery. Evaluating real-world workloads, cost implications, and developer skill sets will guide the optimal choice in Databricks environments.