I am using Amazon MWAA.
I have created a DAG with multiple Glue jobs. Few of the glue jobs are failing in first try but succeeding in 2nd/3rd retries. But MWAA capturing state of first try and making my DAG failed.
Is there any way that MWAA wait for the successful retry.
I am using AwsGlueJobOperator.
AwsGlueJobOperator(
task_id=......
job_name=.....
retry_limit=2
)
Related
I have set some cron jobs in an ec2 instance. I want to be notified whenever a cron job fails in an ec2. In my cron.log, I don't see any error or alert even if the cron fails to execute. How can I capture the failed crons and send cloud watch alarm which can be picked up by SNS.
Thank you.
When using AWS Data Pipleine with EMR there appears to be a notable delay when executing multiple steps. Cluster idles whilst waiting for next job. Is there any way to decrease the Task Runner polling interval of the built in TaskRuner.jar application that is used with this AWS service?
I use AWS IoT to manage Things.
I have a Dynamic Thing Group with Continuous IoT Job attached, so every Thing eventually (based on conditions) can be added to this Group and a thing will be notified on a Job to be executed. This one works perfect.
Now I need to track Job SUCCEEDED event for every Thing (Job execution). How can I do this using AWS IoT services?
I was trying to do this using AWS IoT Rules with the following SQL expression:
SELECT * FROM '$aws/events/jobExecution/my-continuous-job-id/succeeded'
but without success, no events were observed. However, at the same time I can see that Job was successfully executed as in Thing as in the AWS IoT Web Console.
After some research I found the answer. So to handle job execution events one should activate this feature in AWS IoT Core explicitly which is not obvious from the first glance:
AWS Web Console:
AWS IoT -> Settings -> Manage Events -> Check "Job execution: success, failed, rejected, canceled, removed"
AWS CLI:
aws iot update-event-configurations --event-configurations "{\"JOB_EXECUTION\":{\"Enabled\": true}}"
Docs: https://docs.aws.amazon.com/iot/latest/developerguide/iot-events.html
My case is the following. I want to launch a cluster during working hours and terminate it after 18:00 and weekends. The clusters will be used for a datascience project. Years ago we would use a boring crontab for this, but these days i prefer to do this with a lambda function.
In boto3 i can launch a cluster (thanks to Jose Quinteiro) and this post describes it very well How to launch and configure an EMR cluster using boto
How can i terminate a cluster in boto3 in the same lambda function as where i start it?
Using AWS CloudWatch event/rule and AWS Lambda function to check for Idle EMR clusters, you complete your goal. You achieve visibility on the AWS Console level and can easily enable and disable it.
Keeping in mind the need for this, I have developed a small framework to achieve that using the 2nd solution mentioned above. This framework is an AWS based solution using AWS CloudWatch and AWS Lambda using a Python script that is using Boto3 to terminate AWS EMR clusters that have been idle for a specified period of time.
You specify the maximum idle time threshold and AWS CloudWatch event/rule triggers an AWS Lambda function that queries all AWS EMR clusters in WAITING state and for each, compares the current time with AWS EMR cluster's ready time in case of no EMR steps added so far or compares the current time with AWS EMR cluster's last step's end time. If the threshold has been compromised, the AWS EMR will be terminated after removing termination protection if enabled. If not, it will skip that AWS EMR cluster.
AWS CloudWatch event/rule will decide how often AWS Lambda function should check for idle AWS EMR clusters.
You can disable the AWS CloudWatch event/rule at any time to disable this framework in a single click without deleting its AWS CloudFormation stack.
AWS Lambda function is using Python 3.7 as its runtime environment.
In your case, while creating the stack, you can specify your required Cron expression and maximum idle EMR cluster threshold in minutes to achieve this.
You can get the code and use it from GitHub here: https://github.com/abdullahkhawer/auto-terminate-idle-emr
Any contributions, improvements and suggestions to this solution will be highly appreciated. :)
You can terminate the cluster using boto3 by using
emr_client = boto3.client('emr')
emr_client.terminate_job_flows(JobFlowIds=[#replace it with cluster Id you want it to close ])
You could create a scheduled event in cloudwatch that triggers the lambda you are using.
Scheduled events use Cron expressions so you will be able to apply the same logic. Once your function is triggered you will need to determine that it is a shutdown trigger from the event input.
I currently have a task at hand to Terminate a long-running EMR cluster after a set period of time (based on some metric). Google Dataproc has this capability in something called "Cluster Scheduled Deletion" Listed here: Cluster Scheduled Deletion
Is this something that is possible on EMR natively? Maybe using Cloudwatch metrics? Or can I write a long-running jar which will sit on the EMR Master node and just poll yarn for some idle time metric and then shut down the cluster after a set period of time?
Edit: For more clarification. I would like some functionality wherein the cluster is terminated based on idle for some x amount of time. e.g. If the cluster has been up for a while but no jobs have been run for say 1 hour and the cluster is just sitting there doing nothing, then I'd like the ability to terminate the cluster.
The easiest method would be used to Amazon EMR Metrics and Dimensions for Amazon CloudWatch. There is an isIdle boolean that "indicates that a cluster is no longer performing work".
You could create a CloudWatch Alarm that says if it is True for more than x minutes, then trigger the alarm. This would send a message to Amazon SNS, which can trigger a Lambda function to shutdown the cluster.
Components:
Amazon CloudWatch Alarm
Amazon SNS queue
AWS Lambda function
Update: This apparently isn't suitable (see comments below).
An alternate method would be:
Use Amazon CloudWatch Events to schedule a Lambda function every x seconds
The Lambda function looks for any clusters with a particular tag that indicates how long to wait until shutdown (eg 40 minutes). If the tag is not present, the cluster remains untouched.
The Lambda function queries the cluster state (somehow -- probably via a Hadoop API call), then:
If the cluster is idle and there is no Idle Since tag, add an Idle Since tag with the current timestamp
If the cluster is idle and it been more than x minutes since the timestamp in the Idle Since tag, terminate the cluster.
If the cluster is not idle, remove the Idle Since tag (if present)
Keeping in mind the clarification that you have provided in your question, there could be 3 possible ways to do that.
1) Using AWS CloudWatch metric isIdle of an EMR cluster. This metric tracks whether a cluster is live, but not currently running tasks. You can set an alarm to fire when the cluster has been idle for a given period of time, such as thirty minutes.
Reference: https://docs.aws.amazon.com/emr/latest/ManagementGuide/UsingEMR_ViewingMetrics.html
2) [Recommended] Using AWS CloudWatch event/rule and AWS Lambda function to check for Idle EMR clusters. You can achieve visibility on the AWS Console level and can easily enable and disable it.
[Recommended] Solution using 2nd Approach
Keeping in mind the need for this, I have developed a small framework to achieve that using the 2nd solution mentioned above. This framework is an AWS based solution using AWS CloudWatch and AWS Lambda using a Python script that is using Boto3 to terminate AWS EMR clusters that have been idle for a specified period of time.
You specify the maximum idle time threshold and AWS CloudWatch event/rule triggers an AWS Lambda function that queries all AWS EMR clusters in WAITING state and for each, compares the current time with AWS EMR cluster's ready time in case of no EMR steps added so far or compares the current time with AWS EMR cluster's last step's end time. If the threshold has been compromised, the AWS EMR will be terminated after removing termination protection if enabled. If not, it will skip that AWS EMR cluster.
AWS CloudWatch event/rule will decide how often AWS Lambda function should check for idle AWS EMR clusters.
You can disable the AWS CloudWatch event/rule at any time to disable this framework in a single click without deleting its AWS CloudFormation stack.
AWS Lambda function is using Python 3.7 as its runtime environment.
You can get the code and use it from GitHub here: https://github.com/abdullahkhawer/auto-terminate-idle-emr
Note: Any contributions, improvements, and suggestions to this solution that I developed will be highly appreciated.
3) Some other custom solution based on a Shell that runs against a CRON job on an EMR cluster's master node but you will lose its visibility on the AWS Console level and you may require SSH access as well.
I had to do a similar implementation and just considering Cluster Elapsed time was not solving our problem.
so we came up with a approach to hit the Hadoop API, you can find them here
https://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/ResourceManagerRest.html#Cluster_Scheduler_API
So here is what we did,
Ask the user who brings up a cluster to add a Tag like "AutoShutDown":"True:BufferMinutes", here "AutoShutDown" is the key and "True:BufferMinutes" is the value of the Tag
Here BufferMinutes is the time in minutes (30, 60 etc.)
create a Lambda to hit the hadoop api of all those clusters configured with step 1 (if the user does not add the Tag then the cluster is untouched) and fetch the end time of the last job that was completed (only if all jobs are either completed / terminated), if any job is still running then do nothing and exit.
now
datetime_difference = (current_time - lastFinished)
if(datetime_difference > requested_time)
{
terminate_cluster
}
Create a cloud watch trigger and add the lambda created as target to it, schedule the trigger to run as required.
Note: Lambda is written in python, so boto3 is used and client will be "emr" same like what abdullahkhawer mentioned in his solution above.
This implementation gives flexibility to the user to choose and reduces a great deal of burden on dev-ops.