Writing scalable PySpark code for Glue ETL

As organizations continue to generate massive volumes of data, scalable and efficient ETL (Extract, Transform, Load) processes have become essential. AWS Glue, a fully managed serverless data integration service, makes it easier to prepare and load data for analytics. At the core of AWS Glue is Apache Spark, and developers use PySpark — Spark’s Python API — to write transformation logic.

Writing PySpark code for small datasets is straightforward. But as data grows in volume and complexity, performance and scalability become critical. This blog explores key strategies to write scalable, optimized PySpark code within AWS Glue for enterprise-grade ETL pipelines.

Understand the Glue Context

AWS Glue provides a specialized Spark environment. When writing PySpark scripts in Glue, you’ll work with:

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from awsglue.context import GlueContext

from pyspark.context import SparkContext

sc = SparkContext()

glueContext = GlueContext(sc)

spark = glueContext.spark_session

You’ll also use DynamicFrame, Glue’s abstraction over Spark DataFrames that offers additional benefits like schema evolution and better compatibility with semi-structured data.

1. Use DynamicFrames Efficiently

Glue’s DynamicFrame allows seamless integration with various AWS services and supports schema inference. Convert between DynamicFrame and DataFrame only when necessary:

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dyf = glueContext.create_dynamic_frame.from_catalog(database="my_db", table_name="my_table")

df = dyf.toDF()

# Use DataFrame API for complex transformations

df_filtered = df.filter(df["status"] == "active")

# Convert back to DynamicFrame for writing

dyf_filtered = DynamicFrame.fromDF(df_filtered, glueContext, "dyf_filtered")

Use DynamicFrame primarily for reading and writing data. For heavy transformations, the standard DataFrame API is faster and more memory-efficient.

2. Partition Smartly

When working with large datasets, partitioning improves performance by allowing Spark to parallelize tasks:

Use repartition() to increase parallelism:

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df = df.repartition("country", "year")

Avoid coalesce() unless you're shrinking data for a final write — it reduces the number of partitions.

When writing data, use partition columns to improve downstream query performance:

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sink = glueContext.getSink(

    connection_type="s3", 

    path="s3://my-bucket/output/",

    enableUpdateCatalog=True,

    partitionKeys=["country", "year"]

)

sink.writeFrame(dyf_filtered)

3. Avoid Common Performance Pitfalls

Minimize Shuffles: Operations like join, groupBy, and distinct can cause data shuffles — expensive network operations. Reduce them by filtering early and using broadcast joins when one dataset is small.

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small_df = spark.read.csv("s3://my-bucket/small.csv")

broadcast_df = broadcast(small_df)

result = big_df.join(broadcast_df, "id")

Cache Only When Needed: Caching data that’s reused multiple times can improve performance, but unnecessary caching will consume memory.

Use Columns Selectively: Always project only the needed columns to reduce memory use:

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df = df.select("id", "status", "country")

4. Write in Efficient Formats

For large datasets, prefer columnar formats like Parquet or ORC, which are optimized for analytics and reduce I/O costs:

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dyf_filtered.toDF().write.mode("overwrite").parquet("s3://my-bucket/processed/")

Glue also supports writing in these formats through DynamicFrame sinks.

5. Monitor and Tune

Use AWS Glue job metrics and CloudWatch logs to monitor job performance. Pay attention to:

Number of partitions

Shuffle read/write sizes

Memory and executor usage

Adjust Spark settings like --job-language python and --conf spark.sql.shuffle.partitions for better tuning.

Conclusion

Writing scalable PySpark code in AWS Glue is essential for reliable, cost-effective ETL pipelines. By using the right abstractions (DynamicFrame vs DataFrame), managing partitions wisely, avoiding costly operations, and choosing efficient data formats, you can process terabytes of data efficiently. As with any big data tool, continual monitoring, tuning, and profiling are key to long-term scalability and performance.

Learn AWS Data Engineer with Data Analytics
Read More: Creating partitioned tables in Glue Catalog

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