Today, we’re making available a new capability of AWS Glue Data Catalog that allows generating column-level statistics for AWS Glue tables. These statistics are now integrated with the cost-based optimizers (CBO) of Amazon Athena and Amazon Redshift Spectrum, resulting in improved query performance and potential cost savings.

Data lakes are designed for storing vast amounts of raw, unstructured, or semi-structured data at a low cost, and organizations share those datasets across multiple departments and teams. The queries on these large datasets read vast amounts of data and can perform complex join operations on multiple datasets. When talking with our customers, we learned that one the challenging aspect of data lake performance is how to optimize these analytics queries to execute faster.

The data lake performance optimization is especially important for queries with multiple joins and that is where cost-based optimizers helps the most. In order for CBO to work, column statistics need to be collected and updated based on changes in the data. We’re launching capability of generating column-level statistics such as number of distinct, number of nulls, max, and min on files such as Parquet, ORC, JSON, Amazon ION, CSV, XML on AWS Glue tables. With this launch, customers now have integrated end-to-end experience where statistics on Glue tables are collected and stored in the AWS Glue Catalog, and made available to analytics services for improved query planning and execution.

Using these statistics, cost-based optimizers improves query run plans and boosts the performance of queries run in Amazon Athena and Amazon Redshift Spectrum. For example, CBO can use column statistics such as number of distinct values and number of nulls to improve row prediction. Row prediction is the number of rows from a table that will be returned by a certain step during the query planning stage. The more accurate the row predictions are, the more efficient query execution steps are. This leads to faster query execution and potentially reduced cost. Some of the specific optimizations that CBO can employ include join reordering and push-down of aggregations based on the statistics available for each table and column.

For customers using data mesh with AWS Lake Formation permissions, tables from different data producers are cataloged in the centralized governance accounts. As they generate statistics on tables on centralized catalog and share those tables with consumers, queries on those tables in consumer accounts will see query performance improvements automatically. In this post, we’ll demonstrate the capability of AWS Glue Data Catalog to generate column statistics for our sample tables.

Solution overview

To demonstrate the effectiveness of this capability, we employ the industry-standard TPC-DS 3 TB dataset stored in an Amazon Simple Storage Service (Amazon S3) public bucket. We’ll compare the query performance before and after generating column statistics for the tables, by running queries in Amazon Athena and Amazon Redshift Spectrum. We are providing queries that we used in this post and we encourage to try out your own queries following workflow as illustrated in the following details.

The workflow consists of the following high level steps:

1. Cataloging the Amazon S3 Bucket: Utilize AWS Glue Crawler to crawl the designated Amazon S3 bucket, extracting metadata, and seamlessly storing it in the AWS Glue data catalog. We’ll query these tables using Amazon Athena and Amazon Redshift Spectrum.
2. Generating column statistics: Employ the enhanced capabilities of AWS Glue Data Catalog to generate comprehensive column statistics for the crawled data, thereby providing valuable insights into the dataset.
3. Querying with Amazon Athena and Amazon Redshift Spectrum: Evaluate the impact of column statistics on query performance by utilizing Amazon Athena and Amazon Redshift Spectrum to execute queries on the dataset.

The following diagram illustrates the solution architecture.

Walkthrough

To implement the solution, we complete the following steps:

1. Set up resources with AWS CloudFormation.
2. Run AWS Glue Crawler on Public Amazon S3 bucket to list the 3TB TPC-DS dataset.
3. Run queries on Amazon Athena and Amazon Redshift and note down query duration
4. Generate statistics for AWS Glue Data Catalog tables
5. Run queries on Amazon Athena and Amazon Redshift and compare query duration with previous run
6. Optional: Schedule AWS Glue column statistics jobs using AWS Lambda and the Amazon EventBridge Scheduler

Set up resources with AWS CloudFormation

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

• An Amazon Virtual Private Cloud (Amazon VPC), public subnet, private subnets and route tables.
• An Amazon Redshift Serverless workgroup and namespace.
• An AWS Glue crawler to crawl the public Amazon S3 bucket and create a table for the Glue Data Catalog for TPC-DS dataset
• AWS Glue catalog databases and tables
• An Amazon S3 bucket to store athena result.
• AWS Identity and Access Management (AWS IAM) users and policies.
• AWS Lambda and Amazon Event Bridge scheduler to schedule the AWS Glue Column statistics

To launch the AWS CloudFormation stack, complete the following steps:

Note: The AWS Glue data catalog tables are generated using the public bucket s3://blogpost-sparkoneks-us-east-1/blog/BLOG_TPCDS-TEST-3T-partitioned/, hosted in the us-east-1 region. If you intend to deploy this AWS CloudFormation template in a different region, it is necessary to either copy the data to the corresponding region or share the data within your deployed region for it to be accessible from Amazon Redshift.

1. Log in to the AWS Management Console as AWS Identity and Access Management (AWS IAM) administrator.
2. Choose Launch Stack to deploy a AWS CloudFormation template.
   
3. Choose Next.
4. On the next page, keep all the option as default or make appropriate changes based on your requirement 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 10 minutes to complete, after which you can view the deployed stack on the AWS CloudFormation console.

Run the AWS Glue Crawlers created by the AWS CloudFormation stack

To run your crawlers, complete the following steps:

1. On the AWS Glue console to AWS Glue Console, choose Crawlers under Data Catalog in the navigation pane.
2. Locate and run two crawlers tpcdsdb-without-stats and tpcdsdb-with-stats. It may take few mins to complete.
   

Once the crawler completes successfully, it would create two identical databases tpcdsdbnostats and tpcdsdbwithstats. The tables in tpcdsdbnostats will have No Stats and we’ll use them as reference. We generate statistics on tables in tpcdsdbwithstats. Please verify that you have those two databases and underlying tables from the AWS Glue Console. The tpcdsdbnostats database will look like below. At this time there are no statistics generated on these tables.

Run provided query using Amazon Athena on no-stats tables

To run your query in Amazon Athena on tables without statistics, complete the following steps:

1. Download the athena queries from here.
2. On the Amazon Athena Console, choose the provided query one at a time for tables in database tpcdsdbnostats.
3. Run the query and note down the Run time for each query.

Run provided query using Amazon Redshift Spectrum on no-stats tables

To run your query in Amazon Redshift, complete the following steps:

1. Download the Amazon Redshift queries from here.
2. On the Redshift query editor v2, execute the Redshift Query for tables without stats section from downloaded query.
3. Run the query and note down the query execution of each query.

Generate statistics on AWS Glue Catalog tables

To generate statistics on AWS Glue Catalog tables, complete the following steps:

1. Navigate to the AWS Glue Console and choose the databases under Data Catalog.
2. Click on tpcdsdbwithstats database and it will list all the available tables.
3. Select any of these tables (e.g., call_center).
4. Go to Column statistics – new tab and choose Generate statistics.
5. Keep the default option. Under Choose columns keep Table (All columns) and Under Row sampling options Keep All rows, Under IAM role choose AWSGluestats-blog and select Generate statistics.
   
   

You’ll be able to see status of the statistics generation run as shown in the following illustration:

After generate statistics on AWS Glue Catalog tables, you should be able to see detailed column statistics for that table:

Reiterate steps 2–5 to generate statistics for all necessary tables, such as catalog_sales, catalog_returns, warehouse, item, date_dim, store_sales, customer, customer_address, web_sales, time_dim, ship_mode, web_site, web_returns. Alternatively, you can follow the “Schedule AWS Glue Statistics Runs” section near the end of this blog to generate statistics for all tables. Once done, assess query performance for each query.

Run provided query using Athena Console on stats tables

1. On the Amazon Athena console, execute the Athena Query for tables with stats section from downloaded query.
2. Run and note down the query execution of each query.
   

In our sample run of the queries on the tables, we observed the query execution time as per the below table. We saw clear improvement in the query performance, ranging from 13 to 55%.

Athena query time improvement

Run the provided query using Amazon Redshift Spectrum on statistics tables

1. On the Amazon Redshift query editor v2, execute the Redshift Query for tables with stats section from downloaded query.
2. Run the query and note down the query execution of each query.

In our sample run of the queries on the tables, we observed the query execution time as per the below table. We saw clear improvement in the query performance, ranging from 13 to 89%.

Amazon Redshift Spectrum query time improvement

Schedule AWS Glue statistics Runs

In this segment of the post, we’ll guide you through the steps of scheduling AWS Glue column statistics runs using AWS Lambda and the Amazon EventBridge Scheduler. To streamline this process, a AWS Lambda function and an Amazon EventBridge scheduler were created as part of the CloudFormation stack deployment.

1. AWS Lambda function setup:

To begin, we utilize an AWS Lambda function to trigger the execution of the AWS Glue column statistics job. The AWS Lambda function invokes the start_column_statistics_task_run API through the boto3 (AWS SDK for Python) library. This sets the groundwork for automating the column statistics update.

Let’s explore the AWS Lambda function:

• • Go to the AWS Glue Lambda Console.
  • Select Functions and locate the GlueTableStatisticsFunctionv1.
  • For a clearer understanding of the AWS Lambda function, we recommend reviewing the code in the Code section and examining the environment variables under Configuration.
    

2. Amazon EventBridge scheduler configuration

The next step involves scheduling the AWS Lambda function invocation using the Amazon EventBridge Scheduler. The scheduler is configured to trigger the AWS Lambda function daily at a specific time – in this case, 08:00 PM. This ensures that the AWS Glue column statistics job runs on a regular and predictable basis.

Now, let’s explore how you can update the schedule:

Cleaning up

To avoid unwanted charges to your AWS account, delete the AWS resources:

1. Sign into the AWS CloudFormation console as the AWS IAM administrator used for creating the AWS CloudFormation stack.
2. Delete the AWS CloudFormation stack you created.

Conclusion

In this post, we showed you how you can use AWS Glue Data Catalog to generate column-level statistics for AWS Glue tables. These statistics are now integrated with cost-based optimizer from Amazon Athena and Amazon Redshift Spectrum, resulting in improved query performance and potential costs savings. Refer to Docs for support for Glue Catalog Statistics across various AWS analytical services.

If you have questions or suggestions, submit them in the comments section.

About the Authors

Sandeep Adwankar is a Senior Technical 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.

Navnit Shukla serves as an AWS Specialist Solution Architect with a focus on Analytics. He possesses a strong enthusiasm for assisting clients in discovering valuable insights from their data. Through his expertise, he constructs innovative solutions that empower businesses to arrive at informed, data-driven choices. Notably, Navnit Shukla is the accomplished author of the book titled Data Wrangling on AWS. He can be reached via LinkedIn.

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• Source: https://aws.amazon.com/blogs/big-data/enhance-query-performance-using-aws-glue-data-catalog-column-level-statistics/