Understanding PySpark: Features, Ecosystem, and Optimization

What is PySpark?

PySpark is a Python library for Apache Spark that allows users to interface with Spark using Python. It provides a programming interface to the Spark ecosystem, allowing users to harness the power of Spark from Python.

PySpark is particularly useful for data scientists and data engineers who want to use Spark for data processing and analytics tasks, but are more comfortable working with Python than with Scala, the programming language that Spark is written in. PySpark allows these users to leverage their Python skills to work with Spark, and makes it easier to integrate Spark into their existing Python-based workflow.

PySpark provides a number of features and benefits, including:

A familiar Python API for working with Spark
Support for a wide range of data sources and formats, including structured, semi-structured, and unstructured data
Integration with other popular Python libraries and tools, such as NumPy, pandas, and scikit-learn
The ability to scale Spark jobs across a cluster of machines

This is part of an extensive series of guides about open source.

In this article:

Why Is PySpark Needed?
Key Features of PySpark
Running Spark with PySpark vs. Scala
How Does PySpark Work with Pandas?
PySpark Performance Optimization Best Practices

Why Is PySpark Needed?

PySpark is needed because it provides a Python interface to the Spark ecosystem. Without PySpark, users who want to use Spark for data processing and analytics tasks would have to use Scala, the programming language that Spark is written in. While Scala is a powerful language, it may not be as familiar or comfortable to some users, particularly those who are more familiar with Python.

PySpark enables these users to leverage their Python skills to work with Spark, and makes it easier to integrate Spark into their existing Python-based workflow. It also allows users to take advantage of the many powerful libraries and tools that are available in the Python ecosystem, such as NumPy, pandas, and scikit-learn.

In addition, PySpark allows users to scale Spark jobs across a cluster of machines, making it possible to handle large-scale data processing tasks efficiently. This is particularly useful for data scientists and data engineers who need to work with large volumes of data on a regular basis.

While there are several other robust big data options in the Python ecosystem, PySpark has distinct advantages:

PySpark vs. Pandas: Pandas is a Python library for data manipulation and analysis. It is particularly well-suited for working with small to medium-sized data sets, and provides a range of powerful features for cleaning, filtering, and manipulating data. PySpark is generally more suitable for working with large-scale data sets, as it can scale to handle very large volumes of data and can be run in a distributed environment.
PySpark vs. Dask: Dask is a distributed computing library for Python that is designed to be scalable and flexible. It can be used for a range of tasks, including data processing, machine learning, and scientific computing. Dask is generally more lightweight and easier to use than PySpark, but is not as powerful or scalable for very large-scale data processing tasks.
PySpark vs. Apache Flink: Apache Flink is an open-source platform for distributed stream and batch data processing. It is designed to be highly scalable and efficient, and can handle a wide range of data processing tasks, including real-time stream processing, machine learning, and SQL queries. While Flink is considered more powerful and flexible than PySpark, PySpark is easier to use and typically requires less resources to run.

Key Features of PySpark

Real-Time Computing

PySpark is designed for fast processing of large-scale data sets, and is particularly well-suited for real-time data processing tasks. It can handle high volumes of data and process it at high speeds, making it possible to analyze and act on data in near real-time. It works with Spark Streaming to process real-time data inputs from multiple sources.

Support for Several Languages

PySpark provides a Python interface to the Spark ecosystem, allowing users to harness the power of Spark from Python. This is particularly useful for users who are more familiar with Python than with Scala, the programming language that Spark is written in. PySpark also supports a range of other languages, including R, Java, and Scala.

Disk and Memory Caching

PySpark is designed to be highly scalable and efficient, and it includes features such as disk and memory caching to help improve the performance of data processing tasks, while ensuring disk consistency. These features allow PySpark to store data in memory or on disk, and to retrieve it quickly when needed, which can help to improve the speed and efficiency of data processing jobs.

Rapid Processing

PySpark is designed for fast processing of large-scale data sets, and it includes a range of features and optimizations that help to improve the speed and efficiency of data processing tasks. These features include support for in-memory data processing, parallel processing, and distributed computing, which can all help to improve the performance of PySpark jobs.

Running Spark with PySpark vs. Scala

Scala is a lightweight form of the Java language, and is the native language of the Spark platform. There are a few key differences when running Spark with Scala vs. PySpark:

Choice of libraries

Both Scala and PySpark have a wide range of packages and libraries available that can be used to extend their functionality. Scala has a number of powerful libraries and frameworks available, including the Scala Standard Library, Akka, and Play. PySpark has access to the many data science libraries and tools available in the Python ecosystem, including NumPy, pandas, and scikit-learn.

Performance

Scala and PySpark are both fast and efficient, and can handle large-scale data processing tasks with ease. However, Scala is generally considered to be faster and more efficient than PySpark, as it is specifically designed for distributed computing and can take advantage of features such as static typing and functional programming. PySpark is built on top of Scala, and so it may not be able to take full advantage of these features. Depending on the algorithms being implemented and the types of calls made, PySpark performance can be significantly worse than Spark with Scala.

Learning curve

Scala can be more difficult to learn than Python, particularly for users who are not familiar with functional programming concepts. PySpark, on the other hand, is generally easier to learn, as it provides a Python interface to Spark and can be used with many of the same libraries and tools that are commonly used in the Python ecosystem. This can make PySpark a more appealing choice for users who are more familiar with Python, or who want to integrate Spark into an existing Python-based workflow.

Learn more in our detailed guide to PySpark vs. Scala (coming soon)

How Does PySpark Work with Pandas?

PySpark is designed to work seamlessly with the Python library pandas, which is a widely-used tool for data manipulation and analysis. PySpark provides a number of functions and methods that can be used to efficiently read, transform, and write data using pandas.

PySpark provides a distributed computing engine for large-scale data processing, while pandas provides a rich set of functions and methods for data manipulation and analysis. By using PySpark and pandas together, it is possible to take advantage of the strengths of both tools to perform efficient data processing and analysis.

Here are a few examples of how PySpark can be used to work with pandas:

Reading data from pandas: PySpark enables reading data from a pandas DataFrame into a Spark DataFrame.
Converting between PySpark and pandas: PySpark can convert data between PySpark DataFrames and pandas DataFrames.
Using pandas with PySpark: PySpark DataFrames can be passed to pandas functions, allowing users to leverage the full power of pandas for data manipulation and analysis.

Learn more in our detailed guide to PySpark with pandas (coming soon)

PySpark Performance Optimization Best Practices

PySpark is generally slower than Spark with Scala due to the overhead of the Python-Java interoperability. When PySpark is used to process data, the data must be passed between the Python and Java environments, which can lead to slower execution times compared to using Spark with Scala, which is implemented entirely in Java.

There are several factors that can influence the relative performance of PySpark and Spark with Scala, including the size and complexity of the data being processed, the hardware and infrastructure the application is running on, and the specific data processing tasks being performed.

There are several best practices that can be used to optimize the performance of PySpark applications, including:

Use DataFrames and Datasets over RDDs: DataFrames and Datasets provide an optimized execution engine and better integration with other tools, which can lead to improved performance compared to RDDs.
Avoid User Defined Functions (UDFs): UDFs can have a negative impact on performance due to the overhead of executing them on the driver node. It is generally recommended to use Spark’s built-in functions and APIs whenever possible.
Disable or reduce logging output: Logging can have a negative impact on performance, especially for large data sets and complex data processing tasks. Disabling or reducing the amount of logging output can help to improve the performance of the application.
Use small scripts and multiple environments: Small scripts are easier to maintain and test, and can also be faster to execute. Multiple environments can help to improve the performance and reliability of the application by separating different stages of the development and deployment process.
Optimize the number of partitions and partition size: The number of partitions and the size of each partition can have a significant impact on the performance of the application. It is generally recommended to use a larger number of smaller partitions and to experiment with different partition sizes to determine the optimal configuration.

By following these best practices, it is often possible to improve the performance of PySpark applications and make them more effective and scalable.

Optimizing PySpark With Intel Tiber App-Level Optimization

Intel Tiber App-Level Optimization optimizes Apache Spark on a number of levels. With Intel Tiber App-Level Optimization, Spark executor dynamic allocation is optimized based on job patterns and predictive idle heuristics. It also autonomously and continuously optimizes JVM runtimes and tunes the Spark infrastructure itself. All of these optimizations that are applicable to the Spark execution engine, also provide optimization for PySpark.

Learn more in our detailed guide to:

PySpark tutorial
PySpark optimization techniques
PySpark best practices (coming soon)

See Additional Guides on Key Open Source Topics

Together with our content partners, we have authored in-depth guides on several other topics that can also be useful as you explore the world of open source.

Understanding PySpark: Features, Ecosystem, and Optimization

Bar Yochai Shaya

Director of Solution Engineering & Technical Sales, Intel Granulate