Why Avoid UDFs in Spark & PySpark?

User-Defined Functions (UDFs) are a powerful feature in Apache Spark and PySpark that allow users to define their own custom functions to perform complex data operations. However, while UDFs can be useful in certain situations, they can also be a source of performance issues and other problems in Spark applications. In this article, we will explore what makes UDFs problematic in Spark and PySpark, and what alternatives exist for performing data operations that can help overcome these issues.

What is Spark UDF and Why do we need it?

UDFs are custom functions written by users that can be applied to data in a Spark/PySpark DataFrame or RDD. They can be helpful when working with complex data types or performing non-standard operations that are not supported by Spark’s built-in functions.

Below is an example of UDF in Spark with Scala

import org.apache.spark.sql.functions.udf
import org.apache.spark.sql.functions.col
import org.apache.spark.sql.{Row, SparkSession}

object SparkUDF extends App{

  //Create spark session object

  import spark.implicits._
  val columns = Seq("Seqno","Quote")
  val data = Seq(("1", "Be the change that you wish to see in the world"))
  val df = data.toDF(columns:_*)

  // Create UDF
  val convertCase =  (str:String) => {
    val arr = str.split(" ")
    arr.map(f=>  f.substring(0,1).toUpperCase + f.substring(1,f.length)).mkString(" ")
  }
  val convertUDF = udf(convertCase)

  // Use UDF on DataFrame
  df.select(col("Seqno"),
    convertUDF(col("Quote")).as("Quote") ).show(false)
}

Why using UDFs are Bottle-neck to Performance

User-Defined Functions (UDFs) in Spark can incur performance issues due to serialization overhead, necessitating the conversion of data between internal and external representations. Additionally, UDFs may trigger data movement across nodes, introducing network overhead.

Spark struggles to optimize complex UDF logic, leading to suboptimal execution plans and reduced performance. Manual resource management for UDFs can be challenging, impacting overall job efficiency.

Why to Avoid UDFs in PySpark

One of the main issues with UDFs is that they can be slow to execute. Since UDFs are written in Python, they are not compiled like PySpark’s built-in functions, which are implemented in Java. This means that UDFs can be significantly slower when processing large datasets, especially when compared to native PySpark functions. Additionally, UDFs can be memory-intensive, which can lead to out-of-memory errors when working with large datasets.

Disadvantages of UDF

While User-Defined Functions (UDFs) in Spark offer flexibility and extensibility, there are some disadvantages associated with their usage. Here are some drawbacks of using UDFs in Spark:

Performance Overhead:

• UDFs introduce some performance overhead compared to built-in Spark functions. The execution of UDFs involves the cost of serialization, data transfer, and deserialization.

Limited Optimization Opportunities:

• Spark might not be able to optimize UDFs as effectively as built-in functions. This could lead to suboptimal execution plans and, consequently, slower performance.

Type Safety and Error Handling:

• UDFs might not provide the same level of type safety and error handling as built-in functions. Incorrect data types or errors within UDFs can lead to runtime issues that are harder to debug.

Data Movement Overhead:

• When using UDFs, there could be additional data movement between nodes in a distributed environment, leading to increased network overhead.

Resource Management:

• Managing resources (memory, CPU) for UDFs might require manual tuning, and improper resource allocation could impact overall job performance.

Limited Language Support:

• UDFs are typically written in languages like Scala, Java, or Python. While this covers a broad range of use cases, it might not be as versatile as using native Spark functions for certain operations.

Dependency Management:

• UDFs might have external dependencies that need to be managed separately. Ensuring that these dependencies are available across all worker nodes can be challenging.

Debugging Complexity:

• Debugging UDFs can be more challenging than debugging Spark SQL or DataFrame operations. The development and debugging process might be less user-friendly.

Compatibility with Spark Ecosystem:

• Some components of the Spark ecosystem, such as Spark Streaming or Spark MLlib, might not seamlessly support UDFs, limiting their use in certain contexts.

Potential for Non-Optimized Execution Plans:

• Depending on the complexity of the UDF logic, Spark might struggle to generate optimized execution plans, leading to inefficient query execution.

Despite these disadvantages, UDFs remain a valuable tool for extending Spark’s functionality when necessary. Careful consideration of when and how to use UDFs is essential to mitigate these drawbacks and ensure optimal performance.

Conclusion

In conclusion, UDFs can be helpful when working with complex data types or performing non-standard operations in PySpark. However, they come with a number of downsides that can make them problematic in larger-scale applications.

To overcome these issues, it is important to consider using PySpark’s built-in functions, SQL functions, and libraries whenever possible. These alternatives are optimized for performance and are easier to distribute and debug than UDFs, making them a more practical choice for large-scale data processing applications.

Happy Learning !!