AWS Big Data Blog

The AWS Glue Data Catalog now supports storage optimization of Apache Iceberg tables

The AWS Glue Data Catalog now enhances managed table optimization of Apache Iceberg tables by automatically removing data files that are no longer needed. Along with the Glue Data Catalog’s automated compaction feature, these storage optimizations can help you reduce metadata overhead, control storage costs, and improve query performance.

Iceberg creates a new version called a snapshot for every change to the data in the table. Iceberg has features like time travel and rollback that allow you to query data lake snapshots or roll back to previous versions. As more table changes are made, more data files are created. In addition, any failures during writing to Iceberg tables will create data files that aren’t referenced in snapshots, also known as orphan files. Time travel features, though useful, may conflict with regulations like GDPR that require permanent data deletion. Because time travel allows accessing data through historical snapshots, additional safeguards are needed to maintain compliance with data privacy laws. To control storage costs and comply with regulations, many organizations have created custom data pipelines that periodically expire snapshots in a table that are no longer needed and remove orphan files. However, building these custom pipelines is time-consuming and expensive.

With this launch, you can enable Glue Data Catalog table optimization to include snapshot and orphan data management along with compaction. You can enable this by providing configurations such as a default retention period and maximum days to keep orphan files. The Glue Data Catalog monitors tables daily, removes snapshots from table metadata, and removes the data files and orphan files that are no longer needed. The Glue Data Catalog honors retention policies for Iceberg branches and tags referencing snapshots. You can now get an always-optimized Amazon Simple Storage Service (Amazon S3) layout by automatically removing expired snapshots and orphan files. You can view the history of data, manifest, manifest lists, and orphan files deleted from the table optimization tab on the AWS Glue Data Catalog console.

In this post, we show how to enable managed retention and orphan file deletion on an Apache Iceberg table for storage optimization.

Solution overview

For this post, we use a table called customer in the iceberg_blog_db database, where data is added continuously by a streaming application—around 10,000 records (file size less than 100 KB) every 10 minutes, which includes change data capture (CDC) as well. The customer table data and metadata are stored in the S3 bucket. Because the data is updated and deleted as part of CDC, new snapshots are created for every change to the data in the table.

Managed compaction is enabled on this table for query optimization, which results in new snapshots being created when compaction rewrites several small files into a few compacted files, leaving the old small files in storage. This results in data and metadata in Amazon S3 growing at a rapid pace, which can become cost-prohibitive.

Snapshots are timestamped versions of an iceberg table. Snapshot retention configurations allow customers to enforce how long to retain snapshots and how many snapshots to retain. Configuring a snapshot retention optimizer can help manage storage overhead by removing older, unnecessary snapshots and their underlying files.

Orphan files are files that are no longer referenced by the Iceberg table metadata. These files can accumulate over time, especially after operations like table deletions or failed ETL jobs. Enabling orphan file deletion allows AWS Glue to periodically identify and remove these unnecessary files, freeing up storage.

The following diagram illustrates the architecture.

architecture

In the following sections, we demonstrate how to enable managed retention and orphan file deletion on the AWS Glue managed Iceberg table.

Prerequisite

Have an AWS account. If you don’t have an account, you can create one.

Set up resources with AWS CloudFormation

This post includes a CloudFormation template for a quick setup. You can review and customize it to suit your needs. The template generates the following resources:

  • An S3 bucket to store the dataset, Glue job scripts, and so on
  • Data Catalog database
  • An AWS Glue job that creates and modifies sample customer data in your S3 bucket with a Trigger every 10 mins
  • AWS Identity and Access Management (AWS IAM) roles and policies – glueroleoutput

To launch the CloudFormation stack, complete the following steps:

  1. Sign in to the AWS CloudFormation console.
  2. Choose Launch Stack.
    Launch Cloudformation Stack
  3. Choose Next.
  4. Leave the parameters as default or make appropriate changes based on your requirements, then choose Next.
  5. Review the details on the final page and select I acknowledge that AWS CloudFormation might create IAM resources.
  6. Choose Create.

This stack can take around 5-10 minutes to complete, after which you can view the deployed stack on the AWS CloudFormation console.

CFN

Note down the role glueroleouput value that will be used when enabling optimization setup.

From the Amazon S3 console, note the Amazon S3 bucket and you can monitor how the data will be continuously updated every 10 mins with the AWS Glue Job.

S3 buckets

Enable snapshot retention

We want to remove metadata and data files of snapshots older than 1 day and the number of snapshots to retain a maximum of 1. To enable snapshot expiry, you enable snapshot retention on the customer table by setting the retention configuration as shown in the following steps, and AWS Glue will run background operations to perform these table maintenance operations, enforcing these settings one time per day.

  1. Sign in to the AWS Glue console as an administrator.
  2. Under Data Catalog in the navigation pane, choose Tables.
  3. Search for and select the customer table.
  4. On the Actions menu, choose Enable under Optimization.
    GDC table
  5. Specify your optimization settings by selecting Snapshot retention.
  6. Under Optimization configuration, select Customize settings and provide the following:
    1. For IAM role, choose role created as CloudFormation resource.
    2. Set Snapshot retention period as 1 day.
    3. Set Minimum snapshots to retain as 1.
    4. Choose Yes for Delete expire files.
  8. Select the acknowledgement check box and choose Enable.

optimization enable

Alternatively, you can install or update the latest AWS Command Line Interface (AWS CLI) version to run the AWS CLI to enable snapshot retention. For instructions, refer to Installing or updating the latest version of the AWS CLI. Use the following code to enable snapshot retention:

aws glue create-table-optimizer
--catalog-id 112233445566
--database-name iceberg_blog_db
--table-name customer
--table-optimizer-configuration
'{
"roleArn": "arn:aws:iam::112233445566:role/<glueroleoutput>",
"enabled": true,
"retentionConfiguration": {
"icebergConfiguration": {
"snapshotRetentionPeriodInDays": 1,
"numberOfSnapshotsToRetain": 1,
"cleanExpiredFiles": true
}
}
}'
--type retention
--region us-east-1

Enable orphan file deletion

We want to remove metadata and data files that aren’t referenced of snapshots older than 1 day and the number of snapshots to retain a maximum of 1. Complete the steps to enable orphan file deletion on the customer table, and AWS Glue will run background operations to perform these table maintenance operations enforcing these settings one time per day.

  1. Under Optimization configuration, select Customize settings and provide the following:
    1. For IAM role, choose role created as CloudFormation resource.
    2. Set Delete orphan file period as 1 day.
  2. Select the acknowledgement check box and choose Enable.

Alternatively, you can use the AWS CLI to enable orphan file deletion:

aws glue create-table-optimizer
--catalog-id 112233445566
--database-name iceberg_blog_db
--table-name customer
--table-optimizer-configuration
'{
"roleArn": "arn:aws:iam::112233445566:role/<glueroleoutput>",
"enabled": true,
"orphanFileDeletionConfiguration": {
"icebergConfiguration": {
"orphanFileRetentionPeriodInDays": 1
}
}
}'
--type orphan_file_deletion
--region us-east-1

Based on the optimizer configuration, you will start seeing the optimization history in the AWS Glue Data Catalog

runs

Validate the solution

To validate the snapshot retention and orphan file deletion configuration, complete the following steps:

  1. Sign in to the AWS Glue console as an administrator.
  2. Under Data Catalog in the navigation pane, choose Tables.
  3. Search for and choose the customer table.
  4. Choose the Table optimization tab to view the optimization job run history.

runs

Alternatively, you can use the AWS CLI to verify snapshot retention:

aws glue get-table-optimizer --catalog-id 112233445566 --database-name iceberg_blog_db --table-name customer --type retention

You can also use the AWS CLI to verify orphan file deletion:

aws glue get-table-optimizer --catalog-id 112233445566 --database-name iceberg_blog_db --table-name customer --type orphan_file_deletion

Monitor CloudWatch metrics for Amazon S3

The following metrics show a steep increase in the bucket size as streaming of customer data happens along with CDC, leading to an increase in the metadata and data objects as snapshots are created. When snapshot retention (“snapshotRetentionPeriodInDays“: 1, “numberOfSnapshotsToRetain“: 50) and orphan file deletion (“orphanFileRetentionPeriodInDays“: 1) enabled, there is drop in the total bucket size for the customer prefix and the total number of objects as the maintenance takes place, eventually leading to optimized storage.

metrics

Clean up

To avoid incurring future charges, delete the resources you created in the Glue, Data Catalog, and S3 bucket used for storage.

Conclusion

Two of the key features of Iceberg are time travel and rollbacks, allowing you to query data at previous points in time and roll back unwanted changes to your tables. This is facilitated through the concept of Iceberg snapshots, which are a complete set of data files in the table at a point in time. With these new releases, the Data Catalog now provides storage optimizations that can help you reduce metadata overhead, control storage costs, and improve query performance.

To learn more about using the AWS Glue Data Catalog, refer to Optimizing Iceberg Tables.

A special thanks to everyone who contributed to the launch: Sangeet Lohariwala, Arvin Mohanty, Juan Santillan, Sandya Krishnanand, Mert Hocanin, Yanting Zhang and Shyam Rathi.

About the Authors

Sandeep Adwankar is a Senior Product Manager at AWS. Based in the California Bay Area, he works with customers around the globe to translate business and technical requirements into products that enable customers to improve how they manage, secure, and access data.

Srividya Parthasarathy is a Senior Big Data Architect on the AWS Lake Formation team. She enjoys building data mesh solutions and sharing them with the community.

Paul Villena is a Senior Analytics Solutions Architect in AWS with expertise in building modern data and analytics solutions to drive business value. He works with customers to help them harness the power of the cloud. His areas of interests are infrastructure as code, serverless technologies, and coding in Python.