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Friday, October 20, 2023

Extracting key insights from Amazon S3 entry logs with AWS Glue for Ray

Clients of all sizes and industries use Amazon Easy Storage Service (Amazon S3) to retailer knowledge globally for quite a lot of use circumstances. Clients need to understand how their knowledge is being accessed, when it’s being accessed, and who’s accessing it. With exponential progress in knowledge quantity, centralized monitoring turns into difficult. It’s also essential to audit granular knowledge entry for safety and compliance wants.

This weblog submit presents an structure answer that enables prospects to extract key insights from Amazon S3 entry logs at scale. We’ll partition and format the server entry logs with Amazon Internet Companies (AWS) Glue, a serverless knowledge integration service, to generate a catalog for entry logs and create dashboards for insights.

Amazon S3 entry logs

Amazon S3 entry logs monitor and log Amazon S3 API requests made to your buckets. These logs can monitor exercise, corresponding to knowledge entry patterns, lifecycle and administration exercise, and safety occasions. For instance, server entry logs may reply a monetary group’s query about what number of requests are made and who’s making what kind of requests. Amazon S3 entry logs present object-level visibility and incur no further price apart from storage of logs. They retailer attributes corresponding to object dimension, complete time, turn-around time, and HTTP referer for log information. For extra particulars on the server entry log file format, supply, and schema, see Logging requests utilizing server entry logging and Amazon S3 server entry log format.

Key concerns when utilizing Amazon S3 entry logs:

  1. Amazon S3 delivers server entry log information on a best-effort foundation. Amazon S3 doesn’t assure the completeness and timeliness of them, though supply of most log information is inside a couple of hours of the recorded time.
  2. A log file delivered at a particular time can comprise information written at any level earlier than that point. A log file could not seize all log information for requests made as much as that time.
  3. Amazon S3 entry logs present small unpartitioned recordsdata saved as space-separated, newline-delimited information. They are often queried utilizing Amazon Athena, however this answer poses excessive latency and elevated question price for patrons producing logs in petabyte scale. High 10 Efficiency Tuning Ideas for Amazon Athena embrace changing the info to a columnar format like Apache Parquet and partitioning the info in Amazon S3.
  4. Amazon S3 itemizing can turn into a bottleneck even in the event you use a prefix, notably with billions of objects. Amazon S3 makes use of the next object key format for log recordsdata:

TargetPrefix is non-compulsory and makes it less complicated so that you can find the log objects. We use the YYYY-mm-DD-HH format to generate a manifest of logs matching a particular prefix.

Structure overview

The next diagram illustrates the answer structure. The answer makes use of AWS Serverless Analytics providers corresponding to AWS Glue to optimize knowledge structure by partitioning and formatting the server entry logs to be consumed by different providers. We catalog the partitioned server entry logs from a number of Areas. Utilizing Amazon Athena and Amazon QuickSight, we question and create dashboards for insights.

Architecture Diagram

As a primary step, allow server entry logging on S3 buckets. Amazon S3 recommends delivering logs to a separate bucket to keep away from an infinite loop of logs. Each the person knowledge and logs buckets have to be in the identical AWS Area and owned by the identical account.

AWS Glue for Ray, a knowledge integration engine possibility on AWS Glue, is now typically out there. It combines AWS Glue’s serverless knowledge integration with Ray (ray.io), a preferred new open-source compute framework that helps you scale Python workloads. The Glue for Ray job will partition and retailer the logs in parquet format. The Ray script additionally comprises checkpointing logic to keep away from re-listing, duplicate processing, and lacking logs. The job shops the partitioned logs in a separate bucket for simplicity and scalability.

The AWS Glue Knowledge Catalog is a metastore of the situation, schema, and runtime metrics of your knowledge. AWS Glue Knowledge Catalog shops data as metadata tables, the place every desk specifies a single knowledge retailer. The AWS Glue crawler writes metadata to the Knowledge Catalog by classifying the info to find out the format, schema, and related properties of the info. Working the crawler on a schedule updates AWS Glue Knowledge Catalog with new partitions and metadata.

Amazon Athena offers a simplified, versatile strategy to analyze petabytes of information the place it lives. We are able to question partitioned logs instantly in Amazon S3 utilizing normal SQL. Athena makes use of AWS Glue Knowledge Catalog metadata like databases, tables, partitions, and columns beneath the hood. AWS Glue Knowledge Catalog is a cross-Area metadata retailer that helps Athena question logs throughout a number of Areas and supply consolidated outcomes.

Amazon QuickSight permits organizations to construct visualizations, carry out case-by-case evaluation, and shortly get enterprise insights from their knowledge anytime, on any system. You should utilize different enterprise intelligence (BI) instruments that combine with Athena to construct dashboards and share or publish them to offer well timed insights.

Technical structure implementation

This part explains the right way to course of Amazon S3 entry logs and visualize Amazon S3 metrics with QuickSight.

Earlier than you start

There are a couple of stipulations earlier than you get began:

  1. Create an IAM function to make use of with AWS Glue. For extra data, see Create an IAM Position for AWS Glue within the AWS Glue documentation.
  2. Guarantee that you’ve got entry to Athena out of your account.
  3. Allow entry logging on an S3 bucket. For extra data, see Allow Server Entry Logging within the Amazon S3 documentation.

Run AWS Glue for Ray job

The next screenshots information you thru making a Ray job on Glue console. Create an ETL job with Ray engine with the pattern Ray script offered. Within the Job particulars tab, choose an IAM function.

Create AWS Glue job

AWS Glue job details

Go required arguments and any non-compulsory arguments with `--{arg}` within the job parameters.

AWS Glue job parameters

Save and run the job. Within the Runs tab, you’ll be able to choose the present execution and think about the logs utilizing the Log group identify and Id (Job Run Id). You may also graph job run metrics from the CloudWatch metrics console.

CloudWatch metrics console

Alternatively, you’ll be able to choose a frequency to schedule the job run.

AWS Glue job run schedule

Be aware: Schedule frequency is determined by your knowledge latency requirement.

On a profitable run, the Ray job writes partitioned log recordsdata to the output Amazon S3 location. Now we run an AWS Glue crawler to catalog the partitioned recordsdata.

Create an AWS Glue crawler with the partitioned logs bucket as the info supply and schedule it to seize the brand new partitions. Alternatively, you’ll be able to configure the crawler to run primarily based on Amazon S3 occasions. Utilizing Amazon S3 occasions improves the re-crawl time to establish the adjustments between two crawls by itemizing all of the recordsdata from a partition as an alternative of itemizing the complete S3 bucket.

AWS Glue Crawler

You possibly can view the AWS Glue Knowledge Catalog desk by way of the Athena console and run queries utilizing normal SQL. The Athena console shows the Run time and Knowledge scanned metrics. Within the following screenshots under, you will note how partitioning improves efficiency by lowering the quantity of information scanned.

There are vital wins once we partition and format server entry logs as parquet. In comparison with the unpartitioned uncooked logs, the Athena queries 1/scanned 99.9 p.c much less knowledge, and a pair of/ran 92 p.c quicker. That is evident from the next Athena SQL queries, that are related however on unpartitioned and partitioned server entry logs respectively.

SELECT “operation”, “requestdatetime”
FROM “s3_access_logs_db”.”unpartitioned_sal”
GROUP BY “requestdatetime”, “operation”

Amazon Athena query

Be aware: You possibly can create a desk schema on uncooked server entry logs by following the instructions at How do I analyze my Amazon S3 server entry logs utilizing Athena?

SELECT “operation”, “requestdate”, “requesthour” 
FROM “s3_access_logs_db”.”partitioned_sal” 
GROUP BY “requestdate”, “requesthour”, “operation”

Amazon Athena query

You possibly can run queries on Athena or construct dashboards with a BI device that integrates with Athena. We constructed the next pattern dashboard in Amazon QuickSight to offer insights from the Amazon S3 entry logs. For extra data, see Visualize with QuickSight utilizing Athena.

Amazon QuickSight dashboard

Clear up

Delete all of the sources to keep away from any unintended prices.

  1. Disable the entry go surfing the supply bucket.
  2. Disable the scheduled AWS Glue job run.
  3. Delete the AWS Glue Knowledge Catalog tables and QuickSight dashboards.

Why we thought-about AWS Glue for Ray

AWS Glue for Ray presents scalable Python-native distributed compute framework mixed with AWS Glue’s serverless knowledge integration. The first motive for utilizing the Ray engine on this answer is its flexibility with job distribution. With the Amazon S3 entry logs, the most important problem in processing them at scale is the article depend reasonably than the info quantity. It’s because they’re saved in a single, flat prefix that may comprise tons of of thousands and thousands of objects for bigger prospects. On this uncommon edge case, the Amazon S3 itemizing in Spark takes many of the job’s runtime. The article depend can be giant sufficient that the majority Spark drivers will run out of reminiscence throughout itemizing.

In our take a look at mattress with 470 GB (1,544,692 objects) of entry logs, giant Spark drivers utilizing AWS Glue’s G.8X employee kind (32 vCPU, 128 GB reminiscence, and 512 GB disk) ran out of reminiscence. Utilizing Ray duties to distribute Amazon S3 itemizing dramatically decreased the time to record the objects. It additionally saved the record in Ray’s distributed object retailer stopping out-of-memory failures when scaling. The distributed lister mixed with Ray knowledge and map_batches to use a pandas operate in opposition to every block of information resulted in a extremely parallel and performant execution throughout all levels of the method. With Ray engine, we efficiently processed the logs in ~9 minutes. Utilizing Ray reduces the server entry logs processing price, including to the decreased Athena question price.

Ray job run particulars:

Ray job logs

Ray job run details

Please be happy to obtain the script and take a look at this answer in your growth atmosphere. You possibly can add further transformations in Ray to raised put together your knowledge for evaluation.


On this weblog submit, we detailed an answer to visualise and monitor Amazon S3 entry logs at scale utilizing Athena and QuickSight. It highlights a strategy to scale the answer by partitioning and formatting the logs utilizing AWS Glue for Ray. To discover ways to work with Ray jobs in AWS Glue, see Working with Ray jobs in AWS Glue. To discover ways to speed up your Athena queries, see Reusing question outcomes.

In regards to the Authors

Cristiane de Melo is a Options Architect Supervisor at AWS primarily based in Bay Space, CA. She brings 25+ years of expertise driving technical pre-sales engagements and is chargeable for delivering outcomes to prospects. Cris is captivated with working with prospects, fixing technical and enterprise challenges, thriving on constructing and establishing long-term, strategic relationships with prospects and companions.

Archana Inapudi is a Senior Options Architect at AWS supporting Strategic Clients. She has over a decade of expertise serving to prospects design and construct knowledge analytics, and database options. She is captivated with utilizing expertise to offer worth to prospects and obtain enterprise outcomes.

Nikita Sur is a Options Architect at AWS supporting a Strategic Buyer. She is curious to be taught new applied sciences to resolve buyer issues. She has a Grasp’s diploma in Info Techniques – Massive Knowledge Analytics and her ardour is databases and analytics.

Zach Mitchell is a Sr. Massive Knowledge Architect. He works inside the product staff to reinforce understanding between product engineers and their prospects whereas guiding prospects by their journey to develop their enterprise knowledge structure on AWS.

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