What is PySpark Optimization and Why Is it Important? 

Apache PySpark is the Python API for Apache Spark, an open-source, distributed computing system that is designed for high-speed processing of large data sets. PySpark allows users to leverage the power of Spark using the familiar Python programming language.

PySpark is implemented using the Py4J Java library, which allows Python programs to dynamically call Java code. While this is convenient for Python users, it also adds overhead compared to working with Spark with its native programming language, Scala.

In general, PySpark can be expected to have lower performance than Spark with Scala. The performance of PySpark depends on several factors, including the size and complexity of the data being processed, the hardware and infrastructure it is running on, and the specific data processing tasks being performed. 

PySpark performance can be optimized by using techniques like data serialization and caching, and the Spark DataFrame API, which provides optimized execution plans and automatic optimization for many data processing tasks.

In this article we cover the following PySpark optimization techniques:

1. Use DataFrame/Dataset over RDD
2. Avoid UDFs (User Defined Functions)
3. Disable DEBUG and INFO Log Levels
4. Use Small Scripts and Multiple Environments in PySpark
5. Number of Partitions and Partition Size in PySpark

1. Use DataFrame/Dataset over RDD 

Spark’s Resilient Distributed Datasets (RDDs) are a data structure that enables distributed data processing in Spark. However, RDDs have a number of limitations, including the fact that they are low-level and do not provide an optimized execution plan for many data processing tasks.

To address these limitations, Spark introduced the DataFrame and Dataset APIs, which provide a higher-level, more optimized interface for distributed data processing. DataFrames and Datasets are built on top of RDDs and provide a number of benefits over RDDs, including:

• Improved performance: An optimized execution engine that can generate efficient execution plans for many data processing tasks, leading to better performance compared to RDDs.
• Strong typing: Allows for better error checking and improved code readability.
• Better integration with other tools: DataFrames/Datasets integrate well with SQL, pandas, and other tools, providing a consistent interface for data processing across languages (Python, Scala, Java).

The DataFrame/Dataset API also leverages two technologies that help improve performance:

• Project Tungsten: A broad set of technology improvements that are designed to make Spark run faster and use resources more efficiently. It includes several components, such as off-heap memory management, bytecode generation, and binary data encoding, which work together to improve the performance of Spark’s data processing engine.
• Catalyst Optimizer: A query optimization engine that is used by Spark to generate efficient execution plans for DataFrame and Dataset queries. It uses a variety of techniques, such as predicate pushdown, column pruning, and code generation, to optimize the execution of data processing tasks.

Therefore, using DataFrames and Datasets can often lead to improved performance and easier-to-maintain code compared to using RDDs. It is generally recommended to use DataFrames and Datasets over RDDs whenever possible to take advantage of their optimized execution engine and improved integration with other tools.

2. Avoid UDFs (User Defined Functions)

User Defined Functions (UDFs) are functions that are defined by the user and can be used to extend the functionality of Spark’s SQL and DataFrame APIs. While UDFs can be useful in some cases, they can also have a negative impact on the performance of PySpark applications.

One of the main reasons UDFs can impact performance is that they are executed on the driver node of a Spark application, rather than on the worker nodes where data is distributed. This can lead to increased communication overhead and slower execution times, especially for large data sets and complex UDFs. In addition, UDFs negate all the performance benefits of the DataFrame/Dataset API (see previous section).

To avoid the negative performance impact of UDFs, it is generally recommended to use Spark’s built-in functions and APIs whenever possible. These functions and APIs are optimized for distributed data processing and can often be used to achieve the same results as UDFs with better performance.

There are also a number of alternatives to UDFs that can be used to extend the functionality of Spark’s SQL and DataFrame APIs, including:

• Spark SQL functions: These are built-in functions that are provided by Spark and can be used to perform common data processing tasks.
• Window functions: These are functions that can be used to perform operations on a set of rows over a window of data.
• Aggregate functions: These are functions that can be used to perform operations on a group of rows, such as calculating the average or sum of a set of values.

3. Disable DEBUG and INFO Log Levels

PySpark uses the Log4j library for logging, and can output messages at different levels of severity, including DEBUG, INFO, WARN, and ERROR. 

By default, PySpark applications output all messages, including those with severity levels of DEBUG and INFO, to the console or log file. This can be useful for debugging and troubleshooting, but it can also have a negative impact on the performance of the application, especially for large data sets and complex data processing tasks.

To optimize the performance of PySpark applications, it is often recommended to disable or reduce the amount of logging output. This can be done by setting the log level to a higher severity level, such as WARN or ERROR, or by using a logging configuration file to specify which log messages should be output.

Disabling or reducing the amount of logging output can help to improve the performance of PySpark applications by reducing the overhead of generating and outputting log messages.

4. Use Small Scripts and Multiple Environments in PySpark

Small scripts are easier to maintain, test, and troubleshoot than large scripts, and they can also be faster to execute. By breaking down a large PySpark application into smaller, more focused scripts, it is often possible to improve the performance and maintainability of the application.

Multiple environments, such as development, staging, and production environments, can also help to optimize the performance of PySpark applications. By separating different stages of the development and deployment process, it is possible to more easily identify and fix performance issues, and to deploy changes more quickly and reliably.

Using small scripts and multiple environments can also make it easier to use techniques such as continuous integration and delivery (CI/CD) to automate the development and deployment process. This can further improve the performance and reliability of PySpark applications by reducing the time and effort required to deploy changes and fix issues.

5. Number of Partitions and Partition Size in PySpark

The number of partitions and the size of each partition in a PySpark application can have a significant impact on the performance of the application. In general, using a larger number of smaller partitions can lead to better performance, while using a smaller number of larger partitions can lead to worse performance.

There are several factors that can influence the optimal number of partitions and partition size for a PySpark application, 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.

To optimize the number of partitions and partition size, it is often recommended to:

• Use a larger number of smaller partitions: This can help to improve the parallelism and scalability of the application, and to reduce the overhead of shuffling data between partitions.
• Experiment with different partition sizes: Different partition sizes may work better for different data sets and data processing tasks. It may be useful to try different partition sizes and measure the performance of the application to determine the optimal partition size.
• Use the Spark configuration settings: Spark provides several configuration settings that can be used to control the number of partitions and the partition size, such as spark.sql.shuffle.partitions and spark.default.parallelism. These settings can be used to fine-tune the partitioning of data in a PySpark application.
• Consider the Spark recommended partition size: The Spark programming guide recommends a partition size of 128 MB.

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 optimization techniques

Related content: Read our guide to PySpark tutorial