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PySpark vs. Spark: 7 Key Differences and How to Choose

Alon Berger

Product Marketing Manager, Intel Granulate

What Is Apache Spark? 

Apache Spark is a unified analytics engine for large-scale data processing. It supports batch processing, real-time data streaming, machine learning, and graph processing. Developed by the Apache Software Foundation, Spark enables applications in Hadoop clusters to run up to 100 times faster in memory and 10 times faster even when running on disk.

Spark provides a rich set of APIs in Java, Scala, Python (through PySpark), and R, making it accessible to users from various programming backgrounds. Its core abstraction, the Resilient Distributed Dataset (RDD), enables fault-tolerant processing by distributing data across multiple nodes in a cluster. 

What Is PySpark? 

PySpark is the Python API for Apache Spark, enabling Python programmers to take advantage of Spark’s distributed data processing capabilities. Built on the same core as Apache Spark, PySpark leverages the Py4j library to allow Python programs to interface with the Spark engine, executing Spark jobs and accessing Spark’s powerful data processing and machine learning libraries. 

PySpark enables Python developers to efficiently write Spark applications using familiar Python syntax and paradigms, facilitating seamless integration with existing Python libraries like Pandas, NumPy, and Scikit-learn. This integration makes it possible to build data workflows in the Python ecosystem while leveraging Spark’s big data capabilities.

In this article:

PySpark vs. Spark: Key Differences

PySpark differs from Apache Spark in several key areas. 

1. Language 

PySpark offers Python support for Spark through its API, allowing Python developers to write Spark applications using Python. This brings the simplicity and versatility of Python to the data processing capabilities of Spark, making it useful for data scientists familiar with Python.

Apache Spark, primarily written in Scala, requires knowledge of Scala or Java to develop applications. While it supports multiple languages including Python through PySpark, R, and Java, its native language is Scala, meaning that certain features or optimizations might be more readily available or performant in this language.

2. Development Environment

PySpark leverages the existing Python ecosystem, including libraries and development tools familiar to Python developers. It allows them to work within their preferred environment while accessing Spark’s data processing functions. Integration with Jupyter Notebooks and other Python IDEs makes PySpark accessible for data analysis and iterative development.

Spark requires development directly in Scala or Java. This may involve a steeper learning curve for those not already familiar with these languages or the JVM ecosystem. However, it provides a more seamless experience when working with complex data processing tasks, which benefit from the JVM’s performance optimizations and type safety offered by Scala.  

3. Performance 

PySpark may experience some performance overhead due to the need to communicate between Python and JVM processes. This can impact execution times, particularly for high-volume data processing tasks. However, this performance difference is often mitigated by Spark’s ability to execute operations in memory and its efficient handling of distributed data processing.

Spark, when used with its native Scala API, can achieve optimal performance. Scala’s static typing and JVM optimization enable Spark to run complex data processing tasks more efficiently. This reduces the layers of abstraction and potential bottlenecks that might be introduced when using PySpark, making it more suitable for projects requiring execution speed. 

4. Data Processing 

PySpark provides an API for data processing, enabling operations such as filtering, aggregating, and joining datasets in a distributed manner. It simplifies the development of data pipelines by allowing these tasks to be defined in Python code. PySpark’s integration with popular Python libraries for data analysis, such as Pandas and NumPy, further enhances its utility for preprocessing large datasets before analysis or machine learning tasks.

Apache Spark’s functionality revolves around its data processing engine, which can handle batch and real-time data processing at scale. It uses advanced execution strategies, such as Catalyst Optimizer for SQL queries and Tungsten Execution Engine for physical execution plans, to optimize data processing workflows. The Scala API offers more direct control over these optimizations. 

5. Data Ingestion 

PySpark simplifies the process of data ingestion from various sources, including HDFS, Kafka, and cloud storage services like AWS S3. It easily integrates with Python’s library ecosystem, enabling preprocessing and manipulation of ingested data using familiar Pythonic operations. This enables ETL (Extract, Transform, Load) workflows and simplifies the handling of diverse data formats.

Spark supports data ingestion across a range of sources. Its built-in connectors and APIs enable direct access to storage systems, databases, and streaming sources.  

6. Deployment

Deployment of PySpark and Apache Spark applications can be achieved across various environments, including standalone clusters, Hadoop YARN, Apache Mesos, and cloud services like AWS EMR, Google Cloud Dataproc, and Microsoft Azure HDInsight. 

PySpark applications benefit from the ease of deployment inherent to Python scripts, allowing integration with CI/CD pipelines and containerization technologies such as Docker and Kubernetes. This flexibility is useful for quick iterations and deployment of data processing tasks in diverse operational environments.

Spark’s deployment capabilities are equally versatile, supporting a range of cluster managers and cloud platforms. 

7. Community and Documentation 

PySpark benefits from both the Python and Apache Spark communities. While its specific community is smaller than Apache Spark’s, PySpark users can leverage the broader Python ecosystem for additional libraries and tools. 

Spark has a larger and more mature community, resulting in extensive documentation, forums, and third-party resources. This knowledge aids in troubleshooting and learning, as developers can easily find solutions or guidance for common issues. Additionally, the larger community contributes to a wider range of plugins and integrations, enhancing Spark’s capabilities.

Apache Spark vs. PySpark: How to Choose? 

The choice between Apache Spark and PySpark depends on the project’s requirements, team expertise, and the desired development environment. Key considerations include:

  • Team expertise: Consider the programming languages your team is most familiar with. If your team excels in Python, PySpark provides a seamless way to leverage Spark’s capabilities without a steep learning curve. For teams more comfortable with JVM languages like Scala or Java, Apache Spark might be more appropriate.
  • Integration needs: Evaluate how the choice between PySpark and Apache Spark fits into your existing tech stack. PySpark’s integration with Python libraries and tools makes it ideal for projects already using Python for data science tasks. If your ecosystem revolves around JVM-based tools or you require direct access to Spark’s core features for optimization, Apache Spark could offer better integration.
  • Performance considerations: While PySpark offers the ease of Python, it may introduce overhead in communication between Python and JVM processes, affecting performance for certain types of data-intensive operations. Apache Spark, particularly when used with Scala, can leverage JVM optimizations for potentially higher performance in processing large datasets.
  • Development speed vs. optimization needs: PySpark tends to enable faster development cycles due to Python’s simplicity and extensive library support. This makes it suitable for rapid prototyping and projects with shorter timelines. For applications where execution efficiency is the priority, especially at scale, the native use of Apache Spark may provide more opportunities for fine-tuning performance.

Improving Spark and PySpark Performance with App-Level Optimization

Intel® Tiber™ App-Level Optimization offers continuous and autonomous performance improvements for Spark and PySpark workloads. It improves data engineering, data science, and machine learning efficiency by reducing processing times and increasing throughput.

Key features include optimized dynamic allocation, JVM execution, memory arena optimization, and Python optimization for PySpark. The solution integrates with major data storage and infrastructure, helping teams complete more Spark jobs in less time, thereby reducing costs and resource usage.

Learn more about App-Level Optimization for Spark and PySpark

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