As a developer working with AWS Glue and Apache Spark, one of the most powerful tools in your performance optimization toolkit is caching. Caching can significantly reduce computation time and resource usage, especially in complex ETL (Extract, Transform, Load) pipelines where the same data is reused across multiple operations. In this blog, I’ll explain how caching works in AWS Glue Spark, why it’s efficient, and when to use it, using a practical example inspired by a typical Glue job. My goal is to make this concept clear and approachable for developers who are new to Spark or looking to optimize their Glue jobs.

What is Caching in Spark?

In Apache Spark, caching is the process of storing a DataFrame or RDD (Resilient Distributed Dataset) in memory (or disk) so that it can be reused across multiple operations without recomputing it. Spark’s lazy evaluation means that transformations (like filters, joins, or window functions) are not executed immediately. Instead, they build a logical execution plan. Only when an action (like writing to S3, creating a view, or collecting results) is triggered does Spark compute the data.

Without caching, if a DataFrame is used in multiple parts of your job, Spark will recompute it each time it’s needed. This can be expensive, especially if the DataFrame is derived from complex transformations or involves reading data from external storage like Amazon S3. Caching allows you to compute the DataFrame once, store it in memory, and reuse it, saving both time and resources.

A Real-World Example: Why Caching Matters

Let’s walk through a simplified AWS Glue Spark job to illustrate how caching works and why it’s efficient. Imagine you’re processing student data to generate a report. You have a DataFrame called stu_latest, which contains the latest student records after applying some expensive transformations, such as window functions to deduplicate records. This DataFrame is used in two parts of your pipeline:

1. To join with course data to create a report on student-course enrollment.
2. To detect spoofed student records based on some logic.

Here’s a sample Glue job code snippet to set the stage:

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, row_number
from pyspark.sql.window import Window

# Initialize Spark session
spark = SparkSession.builder.appName("StudentETL").getOrCreate()

# Read student data from S3
students_df = spark.read.parquet("s3://my-bucket/students/")

# Apply window function to get the latest student record per ID
window_spec = Window.partitionBy("student_id").orderBy(col("timestamp").desc())
stu_latest = students_df.withColumn("rn", row_number().over(window_spec)).filter(col("rn") == 1).drop("rn")

# Cache stu_latest
stu_latest.cache()

# Path 1: Join with course data
course_stu = stu_latest.join(courses_df, "student_id")
bc_stu_course = course_stu.groupBy("course_id").agg({"student_id": "count"})
bc_user_course = bc_stu_course.join(users_df, "course_id")
bc_user_course_stu = bc_user_course.join(stu_latest, "student_id")

# Path 2: Detect spoofed students
stu_spoofed = stu_latest.filter(col("is_spoofed") == True)

# Combine results
final_df = bc_user_course_stu.union(stu_spoofed.select(bc_user_course_stu.columns))

# Action: Create a temporary view (triggers computation)
final_df.createOrReplaceTempView("new_data")

# Write final output to S3
final_df.write.parquet("s3://my-bucket/output/")

In this example, stu_latest is a critical DataFrame that’s used in two execution paths:

• Path 1: Joins and aggregations to create a student-course report.
• Path 2: Filtering to identify spoofed student records.

Let’s break down what happens when this code runs, focusing on when stu_latest is computed and why caching it is efficient.

When is stu_latest Computed?

Spark’s lazy evaluation means that transformations like withColumn, filter, and join don’t compute stu_latest immediately. Instead, Spark builds a directed acyclic graph (DAG) of transformations. The computation of stu_latest is triggered only when an action requires its data.

In our example, the first action that triggers computation is:

final_df.createOrReplaceTempView("new_data")

This action forces Spark to compute final_df, which depends on bc_user_course_stu and stu_spoofed, both of which depend on stu_latest. Spark traces the dependency chain backward and computes stu_latest as part of the execution plan.

Without caching, Spark would compute stu_latest twice:

1. Once for the course-related path (bc_user_course_stu).
2. Once for the spoofed records path (stu_spoofed).

This is inefficient because stu_latest is derived from an expensive window function that involves:

• Reading data from S3 (I/O-intensive).
• Partitioning and sorting data across the cluster (CPU-intensive).

By calling stu_latest.cache(), you instruct Spark to:

1. Compute stu_latest once when the first action triggers it.
2. Store the result in memory (or disk, if memory is full).
3. Reuse the cached version for all subsequent operations.

The Dependency Chain: Visualizing the Reuse

To understand why caching stu_latest is efficient, let’s trace the dependency chain:

stu_latest
├── Path 1: Course Data
│   ├── stu_with_arrays (join with courses)
│   ├── course_stu
│   ├── bc_stu_course (group by)
│   ├── bc_user_course (join with users)
│   ├── bc_user_course_stu (join with stu_latest again)
│   └── final_df
└── Path 2: Spoofed Detection
    ├── stu_spoofed (filter)
    └── final_df

When final_df.createOrReplaceTempView("new_data") executes:

• Spark computes the entire dependency chain.
• stu_latest is needed in both Path 1 (for joins and aggregations) and Path 2 (for filtering spoofed records).
• Without caching, the window function for stu_latest would run twice, duplicating I/O and CPU work.
• With caching, stu_latest is computed once, stored in memory, and reused for both paths.

Why is Caching Efficient?

Caching stu_latest is efficient for several reasons:

1. Reused Across Multiple Paths: stu_latest is used in two execution paths. Caching ensures it’s computed only once, avoiding redundant calculations.
2. Expensive Transformations: The window function to create stu_latest involves partitioning and sorting, which are resource-intensive. Caching prevents Spark from repeating this work.
3. Reduced I/O Overhead: Reading data from S3 is slow compared to accessing in-memory data. Caching stu_latest eliminates multiple S3 reads.
4. Minimal Memory Trade-Off: While caching consumes memory, the performance gain from avoiding recomputation often outweighs the memory cost. In AWS Glue, you can monitor memory usage and adjust the worker type (e.g., G.1X or G.2X) or number of workers to accommodate caching.
5. Scalability: In large-scale ETL jobs, where datasets are in the terabyte range, caching intermediate results like stu_latest can save hours of processing time.

When Should You Cache in AWS Glue?

Caching is not always the answer, as it consumes memory and may not be necessary for small datasets or simple transformations. Here are some guidelines for when to cache in AWS Glue:

• Cache Reused DataFrames: If a DataFrame is used multiple times in your job (e.g., in multiple joins, filters, or aggregations), caching it can save significant time.
• Cache Expensive Computations: DataFrames derived from complex operations like window functions, large joins, or heavy aggregations are good candidates for caching.
• Monitor Memory Usage: In AWS Glue, use the Spark UI or CloudWatch metrics to ensure caching doesn’t cause memory spills to disk, which can negate performance gains.
• Unpersist When Done: If a cached DataFrame is no longer needed, call unpersist() to free up memory. For example:

stu_latest.unpersist()

• Consider Data Size: For very large DataFrames, caching may not fit in memory. In such cases, consider persisting to disk with stu_latest.persist(StorageLevel.DISK_ONLY) or optimizing your job to reduce the DataFrame’s size before caching.

Common Misconceptions About Caching

One common misunderstanding is that caching is unnecessary if all operations are transformations, since transformations are lazily evaluated. While it’s true that transformations only build the execution plan, the plan’s execution (triggered by an action) may involve recomputing the same DataFrame multiple times if it’s not cached. In our example, even though stu_latest is defined by transformations, its reuse in multiple paths means caching saves significant computation.

Another misconception is that caching always improves performance. If a DataFrame is used only once or is very small, caching may add overhead without benefits. Always analyze your job’s DAG (via the Spark UI) to decide whether caching is worthwhile.

Best Practices for Caching in AWS Glue

To make the most of caching in your Glue jobs:

1. Profile Your Job: Use the Spark UI to identify DataFrames that are recomputed multiple times. The DAG visualization can show you where caching will have the most impact.
2. Cache Strategically: Cache DataFrames that are reused or expensive to compute, but avoid caching every intermediate result.
3. Tune Glue Resources: Ensure your Glue job has enough memory (e.g., use G.2X workers for larger datasets) to support caching without spilling to disk.
4. Test Incrementally: Start with caching one or two key DataFrames, measure the performance impact, and adjust as needed.
5. Clean Up: Unpersist cached DataFrames when they’re no longer needed to free up memory for other operations.

Conclusion

Caching in AWS Glue Spark is a powerful technique for optimizing ETL jobs, especially when dealing with complex transformations and reused DataFrames. By storing intermediate results like stu_latest in memory, you can avoid redundant computations, reduce I/O overhead, and significantly speed up your jobs. The key is to understand your job’s dependency chain, identify reused or expensive DataFrames, and cache strategically.

In our example, caching stu_latest ensured that an expensive window function was computed only once, even though the DataFrame was used in multiple execution paths. This approach is particularly valuable in AWS Glue, where I/O operations (like reading from S3) and distributed computations can be costly.

As you build your Glue jobs, experiment with caching, monitor performance via the Spark UI, and fine-tune your resource allocation. With these practices, you’ll be well-equipped to write efficient, scalable ETL pipelines that make the most of Spark’s capabilities.

Happy coding, and may your Glue jobs run faster than ever!