Unlock CloudFront's New Logging Potential with Athena Partition Projection

When I first set out to use Athena for querying CloudFront logs, I thought it would be a breeze. Just set up the Glue database, create a table, and start querying, right? Wrong. The problem hit me when I realized the log files had a flat structure—no partitions, no hierarchy. Every query ended up scanning massive amounts of data, even when I just needed a small slice of information.

To make things worse, manually adding partitions for each batch of logs felt like an endless chore. It was clear that this setup wasn’t sustainable for our growing traffic. But then came AWS's announcement: Apache Parquet support for CloudFront logs, along with Hive-compatible folder structures. That’s when it clicked—if I combined this with Athena Partition Projection, it would be a total breakthrough.

CloudFront Logs: Then and Now

Previously, CloudFront logs were delivered in plain text (CSV) format. While this format was simple, it wasn’t optimized for querying large datasets. Logs were delivered to S3 in a flat structure.

Old log example (CSV format)

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2024-11-25T15:00:00Z,192.168.1.1,GET,www.example.com,/index.html,200,120,Chrome

Flat structure of file name

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s3://cloudfront--logs/E123ABC456DEF-2024-11-25-12-00-00-abcdef0123456789.gz

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After the update,

CloudFront logs can now be delivered in Apache Parquet format. Parquet is a columnar storage format that improves query performance and reduces storage space significantly.

The same log data, when stored in Parquet, is compressed and structured like this:

Wrapping It Up

CloudFront logs just got a lot easier to work with. Whether you're using the new Apache Parquet format with Hive-compatible folders or combining it with Athena Partition Projection, you can now query your logs faster, cheaper, and with way less hassle. It’s been a game-changer for me, and I hope it will be for you too.

But that’s not all. You can also deliver CloudFront logs to CloudWatch in JSON or text format for real-time monitoring, or even use Kinesis Data Firehose to process logs on the fly. AWS has made it super flexible to work with CloudFront logs, so you can choose the setup that works best for you.

Happy logging! 😊

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