Working with AWS Glue job bookmarks in PySpark

AWS Glue is a fully managed ETL (Extract, Transform, Load) service that makes it easy to prepare and transform data at scale. One powerful feature of AWS Glue is job bookmarks, which help manage data processing state by tracking previously processed data. This ensures that each time your ETL script runs, it only processes new or changed data—a critical feature for incremental data processing.

In this blog, we’ll explore how to work with AWS Glue job bookmarks in PySpark, including what they are, how to enable or disable them, and practical use cases in ETL pipelines.


What Are Job Bookmarks in AWS Glue?

Job bookmarks are checkpoints that help AWS Glue jobs keep track of previously processed data. When enabled, they prevent duplicate processing by skipping files or records that have already been read in earlier runs.

Job bookmarks work best with partitioned data in services like Amazon S3, Amazon RDS, and Amazon Redshift.


Benefits of Using Job Bookmarks

Efficiency: Only new or modified data is processed.

Idempotency: Prevents reprocessing of data during retries or reruns.

Automation: Simplifies the design of incremental ETL pipelines.


Enabling Job Bookmarks in Glue

When creating or editing a Glue job, you can enable job bookmarks in the AWS Management Console or via the AWS CLI.

bash


aws glue update-job \

  --job-name my-glue-job \

  --job-update '{

      "JobBookmarkOption": "job-bookmark-enable"

  }'


The three bookmark options are:

"job-bookmark-enable": Enables bookmarking (default behavior).

"job-bookmark-disable": Disables bookmarking.

"job-bookmark-pause": Temporarily pauses bookmark tracking.


Using Job Bookmarks in PySpark ETL Scripts

When you read data using glueContext.create_dynamic_frame_from_options() or glueContext.create_dynamic_frame.from_catalog(), AWS Glue automatically applies bookmark logic if enabled.


Example: Reading from S3 with Bookmarks Enabled

python

Copy

Edit

import sys

from awsglue.context import GlueContext

from awsglue.job import Job

from awsglue.utils import getResolvedOptions

from pyspark.context import SparkContext


args = getResolvedOptions(sys.argv, ['JOB_NAME'])

sc = SparkContext()

glueContext = GlueContext(sc)

spark = glueContext.spark_session


job = Job(glueContext)

job.init(args['JOB_NAME'], args)


# Read only new data using bookmarks

datasource = glueContext.create_dynamic_frame.from_catalog(

    database="my_database",

    table_name="my_table",

    transformation_ctx="datasource"

)


# Your transformation logic here

datasource_transformed = datasource.drop_fields(['unnecessary_column'])


# Write to target

glueContext.write_dynamic_frame.from_options(

    frame=datasource_transformed,

    connection_type="s3",

    connection_options={"path": "s3://my-output-bucket/"},

    format="parquet"

)


job.commit()

Testing and Debugging with Bookmarks

When testing, you may want to disable bookmarks to reprocess all data.

Check the AWS Glue Run Details for information on bookmark checkpoints.

Use "job-bookmark-pause" when experimenting without losing bookmark history.


Best Practices

Partition your data in S3 for efficient bookmarking.

Avoid manual deletion of bookmark checkpoints unless resetting is necessary.

Use job arguments to control behavior during testing or production.


Conclusion

AWS Glue job bookmarks are essential for building efficient, scalable, and production-grade ETL pipelines. When combined with PySpark, they provide a seamless way to handle incremental data processing with minimal effort. By understanding how to use and manage bookmarks, you can optimize your ETL jobs and avoid processing duplicate data, reducing both compute time and cost.


Learn AWS Data Engineer Training

Read More: Running Spark ML models on Amazon EMR
Read More: Using AWS Secrets Manager in data pipelines
Read More: Trigger-based data partitioning in S3



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