Interrupting a Spot Instance

STOP Please note: That this workshop has been deprecated. For the latest and updated version featuring the newest features, please access the Workshop at the following link: Cost efficient Spark applications on Amazon EMR. This workshop remains here for reference to those who have used this workshop before, or those who want to reference this workshop for earlier version.

In this section, you’re going to launch a Spot Interruption using FIS and then verify that the capacity has been replenished and EMR was able to continue running the Spark job. This will help you to confirm the low impact of your workloads when implementing Spot effectively. Moreover, you can discover hidden weaknesses, and make your workloads fault-tolerant and resilient.

Launch the Spark Application

The Spark job could take around seven to eight minutes to finish. However, when you arrive to this part of the workshop, either the job is about to finish or has finished already. So, here are the commands you need to run to re-launch the Spark job in EMR.

First, you need to empty the results folder in the S3 bucket. Run the following command (replace the bucket name with yours):

aws s3 rm --recursive s3://$S3_BUCKET/resultsspot/

Get the Cluster ID by running the following commands:

export EMRClusterName="emr-spot-workshop";
export EMRClusterID=$(aws emr list-clusters --active | jq -c '.Clusters[] | select( .Name == '\"$EMRClusterName\"' )' | jq -r '.Id');
aws emr describe-cluster --cluster-id $EMRClusterID | jq -r '.Cluster.Status';

If you got an error about not getting the a proper value for --cluster-id, you may have picked a different name for the EMR Cluster. Make sure the EMRClusterName enviroment variable matches with your EMR cluster name and run the above commands again.

Then, launch a new job using the initial Spark application but store the results at a different location:

aws emr add-steps --cluster-id $EMRClusterID --steps Type=CUSTOM_JAR,Name="Spark application",Jar="command-runner.jar",ActionOnFailure=CONTINUE,Args=[spark-submit,--deploy-mode,cluster,--executor-memory,18G,--executor-cores,4,s3://$S3_BUCKET/,s3://$S3_BUCKET/resultsspot/]

Now go ahead an run the Spot interruption experiment before the jobs completes.

EMR might not have task nodes running at the moment because of managed scaling. If that’s the case, wait around two minutes (or less) until you have at least three task instances in a RUNNING state to interrupt them while the Spark job is running.

Launch the Spot Interruption Experiment

After creating the experiment template in FIS, you can start a new experiment to interrupt three (unless you changed the template) Spot instances. Run the following command:

FIS_EXP_TEMP_ID=$(aws cloudformation describe-stacks --stack-name $FIS_EXP_NAME --query "Stacks[0].Outputs[?OutputKey=='FISExperimentID'].OutputValue" --output text)
FIS_EXP_ID=$(aws fis start-experiment --experiment-template-id $FIS_EXP_TEMP_ID --no-cli-pager --query "" --output text)

Wait around 30 seconds, and you should see that the experiment completes. Run the following command to confirm:

aws fis get-experiment --id $FIS_EXP_ID --no-cli-pager

At this point, FIS has triggered a Spot interruption notice, and in two minutes the instances will be terminated.

Go to CloudWatch Logs group /aws/events/spotinterruptions to see which instances are being interrupted.

You should see a log message like this one:


Verify the actions taken by EMR instance fleet

You are running EMR instance fleets with managed cluster scaling, that constantly monitors key metrics and automatically increases or decreases the number of instances or units in your cluster based on workload. EMR instance fleet can launch replacement instances, if you managed to start the FIS experiment within first minute of Spark job and Spark job runs for additional 4 to 5 minutes.

You can run the following command to see the list of instances with the date and time when they were launched.

aws ec2 describe-instances --filters\
 | jq '.Reservations[].Instances[] | "Instance with ID:\(.InstanceId) launched at \(.LaunchTime)"'

You should see a list of instances with the date and time when they were launched. If managed scaling launched the replacements then you would see new instances with launch time different from the others.

"Instance with ID:i-06a82769173489f32 launched at 2022-04-06T14:02:49+00:00"
"Instance with ID:i-06c97b509c5e274e0 launched at 2022-04-06T14:02:49+00:00"
"Instance with ID:i-002b073c6479a5aba launched at 2022-04-06T14:02:49+00:00"
"Instance with ID:i-0e96071afef3fc145 launched at 2022-04-06T14:02:49+00:00"
"Instance with ID:i-04c6ced30794e965b launched at 2022-04-06T14:02:49+00:00"
"Instance with ID:i-0136152e14053af81 launched at 2022-04-06T14:11:25+00:00"
"Instance with ID:i-0ff78141712e93850 launched at 2022-04-06T14:11:25+00:00"
"Instance with ID:i-08818dc9ba688c3da launched at 2022-04-06T14:11:25+00:00"

Verify that the Spark application completed successfully

Follow the same steps from “Examining the cluster” to launch the Spark History Server and explore the details of the recent Spark job submission. Here’s the command you need to run:

ssh -i ~/environment/emr-workshop-key-pair.pem -N -L 8080:$EMRClusterDNS:18080 hadoop@$EMRClusterDNS

Now click on the Preview Running Application under the Preview menu at the top. You’ll see a browser window opening with in the Cloud9 environment with a refused connection error page. Click on the button next to Browser (arrow inside a box) to open web UI in a dedicated browser page.

In the home screen, click on the latest App ID (if it’s empty, wait for the job to finish) to see the execution details. You should see something like this:


Notice how two minutes around after the job started, three executors were removed (each executor is a Spot instance). If your job runs long enough then you can see new executors being launched to catch-up on completing the job. In this example the job took around eight minutes to finish. If you don’t see executors being added, you could re-launch the Spark job and start the FIS experiment as soon as the spark job starts.

QUESTION: As a result of Spot interruptions you might see different results. For example, all stages of your Spark jobs passed without any error, or a single stage was failed and then re-tried. Do you know why this happens and what actions are taken by EMR on the instances that were interrupted? Click to expand the answer.
If you want to run the Spot interruptions experiment again using a script, then expand this section.