Deploy Amazon QuickSight dashboards to watch AWS Glue ETL job metrics and set alarms

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Irrespective of the trade or stage of maturity inside AWS, our prospects require higher visibility into their AWS Glue utilization. Higher visibility can lend itself to positive aspects in operational effectivity, knowledgeable enterprise selections, and additional transparency into your return on funding (ROI) when utilizing the varied options out there via AWS Glue.

As your organization grows, it’s best to have the ability to reply easy questions on your AWS Glue utilization, resembling the next:

  • The place am I spending probably the most with AWS Glue?
  • The place can I save probably the most by benefiting from new AWS Glue options?
  • What does my total utilization appear to be utilizing AWS Glue?

AWS presents companies resembling Amazon QuickSight, a serverless enterprise intelligence (BI) service that allows you to centralize this view and even ask pure language questions of your knowledge, utilizing Amazon QuickSight Q. QuickSight may give enterprise leaders and their expertise counterparts a typical panorama for reporting necessary particulars of their utilization, offering automated narratives to bridge communication gaps.

On this put up, we discover find out how to mix AWS Glue utilization data and metrics with centralized reporting and visualization utilizing QuickSight. This will offer you a extra complete view of your utilization and instruments that can assist you dive deep into your AWS Glue job run atmosphere. You could have metrics out there per job run inside the AWS Glue console, however they don’t cowl all out there AWS Glue job metrics, and the visuals aren’t as interactive in comparison with the QuickSight dashboard.

Though we don’t cowl optimizing your jobs for prices on this put up, you’ll be able to check with Monitor and optimize price on AWS Glue for Apache Spark to learn to fine-tune your AWS Glue jobs for efficiency, effectivity ,and cost-optimization.

Let’s dive in!

Resolution overview

The next diagram illustrates the structure for the given resolution. At a excessive stage, a scheduled occasion triggers an orchestration circulate consisting of a number of knowledge, compute, and analytics sources—the output of which culminates as a set of visuals in a BI dashboard.

solution architecture

Now let’s dig into the technical particulars concerned on this resolution.

An AWS Step Features workflow is scheduled to run as soon as per hour via Amazon EventBridge, which triggers an AWS Lambda perform that calls the AWS Glue GetJob and GetJobRun APIs. We parse this knowledge to verify for jobs which have succeeded, stopped, or failed previously hour, in addition to any streaming jobs. The metadata is extracted from every job run, together with data like runtime, begin time, finish time, auto scaling, variety of staff, and employee sort, and is written to an Amazon DynamoDB desk with TTL (time to dwell) enabled to make sure the desk doesn’t develop too massive.

We transfer right into a parallel state to verify two tables that Amazon Athena writes the output of the federated queries to. Athena first checks to ensure the tables exist in Amazon Easy Storage Service (Amazon S3), the place the information can be saved. If the tables don’t exist, Athena creates them. One federated question gathers AWS Glue metric knowledge from Amazon CloudWatch metrics; the opposite gathers knowledge from the DynamoDB desk the place Lambda writes the AWS Glue job metadata it’s gathering. Each federated queries make the most of acceptable filtering with a purpose to solely scan the mandatory knowledge from every supply.

There’s a selection state for every department. If there isn’t a new knowledge to be added to a desk in Amazon S3, the state ends and waits for the opposite to finish. For instance, there might be an AWS Glue job that’s working whereas the step is evaluating. On this case, the metrics for the job can be inserted within the desk on Amazon S3, however the metadata from DynamoDB wouldn’t arrive till the next hour after the job has succeeded, stopped, or failed.

When new metrics or metadata are discovered, Athena inserts this knowledge to the metrics or metadata tables in Amazon S3, that are each partitioned by the hour. After the information is inserted, the ultimate steps name the QuickSight CreateIngestion API, which triggers knowledge ingestion into QuickSight SPICE to energy interactive evaluation. At this level, the workflow has completed working and can run once more the next hour.

Within the following sections, we present you find out how to arrange the answer, discover the dashboards, and configure alarms.

The code for this resolution will be discovered on the AWS samples GitHub repository.

Stipulations

It is best to have the next conditions:

  • An AWS account with AWS Id and Entry Administration (IAM) privileges adequate to create the answer sources
  • QuickSight Commonplace or Enterprise Version with a QuickSight consumer created
  • An AWS Cloud9 built-in improvement atmosphere (IDE) or your native machine utilizing your most popular IDE with the next packages put in:
  • The AWS Cloud Growth Package (AWS CDK) bootstrapped in your goal AWS account and Area

Deploy resolution sources with the AWS CDK

To provision the sources that construct the dashboard and hold it updated, we offer steps to obtain and deploy the answer through the AWS CDK. The answer was developed with cost-optimization as a precedence, however some sources within the stack will incur prices as soon as deployed.

This resolution generates the next sources:

  • IAM position
  • EventBridge rule
  • Step Features state machine
  • Lambda perform
  • S3 bucket
  • Two AWS Glue tables and one AWS Glue database
  • DynamoDB desk
  • Athena queries invoked by Step Features
  • QuickSight knowledge supply, dataset, evaluation, and dashboard

To deploy the answer, full the next steps:

  1. Clone the supply code from AWS samples GitHub repository to the shopper:
    git clone https://github.com/aws-samples/glue-metrics-in-quicksight

  2. Bootstrap your AWS CDK app:
    cd glue-metrics-in-quicksight
    npm i aws-cdk-lib
    cdk bootstrap

  3. Deploy the answer with the required parameters:
    1. The primary parameter is for a brand new S3 bucket to be created, which holds the AWS Glue metrics and metadata.
    2. The second parameter is required to ensure that QuickSight to assign permissions to the consumer who will handle the property. Check with Managing consumer entry inside Amazon QuickSight to search out your present QuickSight customers.
      cdk deploy --parameters BucketName=New-Distinctive-Bucket-Title --parameters QuicksightUsername=QuickSight-Current-Person

In case your deployment fails, ensure you put in the AWS CDK library and rerun cdk deploy after putting in:

The deployment could take as much as 10 minutes.

After the answer is deployed, the Step Features state machine will consider as soon as per hour if it ought to ingest knowledge into QuickSight. You possibly can run some AWS Glue jobs after the stack is deployed and verify the QuickSight dashboard within the subsequent hour or two, the place the job metadata and metrics can be populated in your evaluation.

Discover the dashboard

The dashboard incorporates two sheets: Glue Jobs and Glue Metrics.

The Glue Jobs sheet consists of all the metadata about your AWS Glue job runs, together with AWS Glue for Apache Spark, AWS Glue for Ray, and AWS Glue streaming ETL. A lot of the visuals even have a hierarchy that you could drill down into with QuickSight, going as little as every particular job run ID. You should utilize controls to filter by date, job identify, and job run ID.

Within the following demonstration, you will notice the pivot desk, which is an easy view of all our job metadata, together with estimated price per job and job run. We open up a job identify and see the completely different job runs. There’s one particular person job run that we want to examine the metrics on, so we select the job identify and select View metrics for job run id: <my job run id>. It will take us to the Glue Metrics sheet and routinely filter for the job run ID we need to view.

glue information sheet

The Glue Metrics sheet is constructed to replicate the documentation we offer in AWS Glue useful resource monitoring. This documentation helps clarify every visible within the dashboard. You should utilize the Glue Metrics sheet to view aggregated metrics throughout all jobs, a single job, or all the way down to the job run ID.

To populate the Glue Metrics sheet, your AWS Glue jobs have to be enabled to seize metrics in CloudWatch.

glue metrics sheet

Arrange alerts

Establishing alerts on measures can also be simple to do in QuickSight. To take action, select (right-click) one of many tracked measures on both worksheet and select Create Alarm. It will carry you to the configuration web page to arrange the metric you’d wish to be alerted on.

quicksight alarm

The dashboard is designed to provide the freedom to change it and make your personal visualizations with the metadata and metrics which can be supplied to you. If you would like much more perception into price, think about deploying the CUDOS dashboard as properly!

Clear up

When you now not want the dashboard, delete the CDK app:

Conclusion

On this put up, we talked concerning the significance of getting observability of your AWS Glue jobs and supplied an AWS CDK app that deploys a QuickSight dashboard for you. We hope this helps you optimize your AWS Glue atmosphere utilizing the insights the dashboard gives. To find out about event-based alerting in your AWS Glue for Apache Spark and Ray jobs, check with Automate alerting and reporting for AWS Glue job useful resource utilization.


Concerning the authors

Michael Hamilton is a Sr Analytics Options Architect specializing in serving to enterprise prospects within the south east modernize and simplify their analytics workloads on AWS. He enjoys mountain biking and spending time together with his spouse and three youngsters when not working.

Cody Penta is a Options Architect at Amazon Internet Providers and is predicated out of Charlotte, NC. He has a spotlight in safety and CDK, and enjoys fixing the actually troublesome issues within the expertise world. Off the clock, he loves stress-free within the mountains, coding private tasks, and gaming.

Angus Ferguson is a Options Architect at AWS who’s captivated with assembly prospects the world over, serving to them clear up their technical challenges. Angus makes a speciality of Knowledge & Analytics with a deal with prospects within the monetary companies trade.

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