In my application, I need to run a fargate job(Job1) which loops through a particular task and invokes multiple tasks of fargate Job(Job2). So I want to know what are the possible ways to run this whole operation as a scheduled task? I tried to create ECS cluster with 2 containers and schedule both job1, and job2 using cloud watch events to run. But i was wondering what is the use of AWS Batch? Was is it an alternative for Cloud watch events? Suggest your thoughts please
You could use AWS EventBridge for this task, it uses the same underlying API as CloudWatch Events but with some relevant architectural changes to better implement an event-driven architecture.
Here's the official documentation how to implement a schedule rule, you're looking to use a ECS Target
AWS Batch serves a different purpose than the one from your use case, as per their official documentation:
AWS Batch enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. AWS Batch dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of the batch jobs submitted.
What you're trying to do is quite simple, I recommend you keep it simple and don't try to overcomplicate it.
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I have simple workflow to design where there will be 4 batch job running one after another sequentially and each jobs is running in multi node master/slave architecture.
My question is AWS Batch can manage simple workflow using job queue and can manage multi-node parallel job as well.
Now, should I use AWS Batch or Airflow ?
With Airflow , I can use KubernetesPodOperator and job will run in Kubernetes cluster. But Airflow does not inherently support multi node parallel jobs.
Note: The batch job is written in java using Spring batch remote partitioning framework that support master/slave architecture.
AWS Batch would fit your requirements better.
Airflow is a workflow orchestration tool, it's used to host many jobs that have multiple tasks each, with each task being light on processing. Its most common use is for ETL, but in your use case you would have an entire Airflow ecosystem for just a single job, which (unless you manually broke it out to smaller tasks) would not run multi-threaded.
AWS Batch on the other hand is for batch processing, and you can more finely-tune the servers/nodes that you want your code to execute on. I think in your use case it would also work out cheaper than Airflow too.
I have a docker image. I would like to create a container periodically and execute as a job, say every 1 hour, by creating CloudWatch Rule.
As we are using AWS cloud, I am looking at the AWS Batch service. Interestingly there is also a ECS Scheduled task.
What is the difference between these 2?
Note: I have an init container - that is I have 2 docker containers to run one after another. It seems to be possible with ECS Scheduled Task. But not with Batch.
AWS Batch is for batch jobs, such as processing numerous images or videos in parallel (one container per image/video). This is mostly useful in batch-type workloads for research purposes.
AWS Batch is based on ECS (also supports EC2), and it allows you to simply to run your containers. It does not have specific use-case, it is more generic. If you don't have batch-type projects, then ECS would be probably better choice for you.
The other answers are spot on. I just wanted to add that we (AWS container team) ran a session at re:Invent last year that covered these options and provided hints about when using one over the other. The session covers the relationship between ECS, EC2 and Fargate (something that is often missed) as well as when to use "raw" ECS, Vs Step Functions Vs Batch as an entry point for running your batch jobs. This is the link to the session.
If you want to run two containers in sequence, using AWS Fargate, then you probably want to orchestrate it with AWS Step Functions. Step Functions will allow you to call arbitrary tasks in serial, and it has direct integration with AWS Fargate.
Amazon EventBridge Rule (hourly) ----- uses AWS IAM role to gain permission to trigger Step Functions
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AWS Step Functions ----- Uses AWS IAM role to gain permission to trigger Fargate
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AWS Fargate (Amazon ECS) Task Definition
AWS Batch is designed for data processing tasks that need to scale out across many nodes. If your use case is simply to spin up a couple of containers in sequence, then AWS Batch will be overkill.
CloudWatch Event Rules
FYI CloudWatch Event Rules still work, but the service has been rebranded as Amazon EventBridge. I'd recommend using the Amazon EventBridge console and APIs instead of Amazon CloudWatch Events APIs going forward.
I have been planning to migrate my Batch processing from Spring Batch to AWS Batch. Can someone give me the reasons to Choose AWS Batch over Spring Batch?
Whilst both these things will play a role in orchestrating your batch workloads a key difference is that AWS Batch will also manage the infrastructure you need to run the jobs/pipeline. AWS Batch lets you to tailor the underlying cloud instances, or specifcy a broad array of instance types that will work for you. And it'll let you make trade-offs: you can task it with managing a bag of EC2 Spot instances for you (for example), and then ask it to optimize time-to-execution over price (or prefer price to speed).
(For full disclosure, I work for the engineering team that builds AWS Batch).
I believe both work at different levels .Spring batch provide framework that reduce the boiler plate code that you need in order to write a batch job.For eg. saving the state of job in Job repository that provide restartability.
On the contrary, AWS batch is an infrastructure framework that helps in managing infra and set some environment variable that help differentiate master node from slave node.
In my opinion both can work together to write a full fledged cost effective batch job at scale on AWS cloud.
Aws Batch is full blown SaaS solution for batch processing,
It has inbuilt
Queue with priority options
Runtime, which can be self managed and auto managed
Job repo, docker images for the job definitions
Monitoring , dashboards, integration with other AWS services like SNS and from SNS to where ever you want
On the other hand, batch is a framework which would still need some of your efforts to manage it all. like employing a queue, scaling is your headache, monitoring etc
My take is , if your company or app is on AWS , go for AWS batch , you will save months of time and get to scalability to a million jobs per day in no time . If you are on-perm or private go for spring batch with some research
I have scheduled 2 cronjobs for my application.
My Application server is in an autoscaling group and I kept a minimum of 2 instances because of High availability. Everything working is fine but cron job is running multiple times because of 2 instances in autoscaling.
I could not limit the instance size to 1 because already my application in the production environment I prefer to have HA.
How should I have to limit execute cron job on a single instance? or should i have to use other services like AWS Lamda or AWS ELasticBeanstalk
Firstly you should consider whether running the crons on these instances is suitable. If you're trying to keep this highly available and it is directly interacted via customers what will the impact of the crons performance be?
Perhaps consider using a separate autoscaling group or instance with a total of 1 instances to run these crons? You could launch the instance or update the autoscaling group just before the cron needs to run and then automate the shutdown after it has completed.
Otherwise you would need to consider using a locking mechanism for your script. By using this your script write a lock to confirm that it is in process, at the beginning of the script run it would check whether there was any script lock in progress. To further prevent the chance of a collision between multiple servers consider adding jitter (random seconds of sleep) to the start of your script.
Suitable technologies for writing a lock are below:
DynamoDB using strongly consistent reads.
EFS for a Linux application, or FSX for a Windows application.
S3 using strong consistency.
Solutions suggested by Chris Williams sound reasonable if using lambda function is not an option.
One way to simulate cron job is by using CloudWatch Events (now known as EventBridge) in conjunction with AWS Lambda.
First you need to write a Lambda function with the code that needs to be executed on a schedule. Lambda supports cron expressions.
You can then use Schedule Expressions with EventBridge/CloudWatch Event in the same way as a cron tab and mention the Lambda function as target.
you can enable termination protection on of the instance. Attach necessary role & permission for system manager. once the instance is available under managed instance under system manager you can create a schedule event in cloudwatch to run ssm documents. if you are running a bash script convert that to ssm document and set this doc as targate. or you can use shellscript document for running commands
We can add tags to EC2 instances to help us better track billing usages and to manage instances.
Is there a way to achieve when deploying containers in ECS? I would like the running container to have the ability to know what tag it currently have attached.
It really depends on what you're ultimately trying to visualize after the fact. I'll share a few off-the-cuff thoughts below, and maybe you can extrapolate on these to build something that satisfies your needs.
As you probably are aware, ECS Tasks themselves don't support the notion of tags, however there are some workarounds that you could consider. For example, depending on how you're logging your application's behavior (eg. batching logs to CloudWatch Logs), you could create a Log Stream name, for each ECS Task, that contains a delimited array of tags.
As part of a POC I was building recently, I used the auto-generated computer name to dynamically create CloudWatch Log Stream names. You could easily append or prepend the tag data that you embed in your container images, and then query the tag information from the CloudWatch Log Streams later on.
Another option would be to simply log a metric to CloudWatch Metrics, based on the number of ECS Tasks running off of each unique Task Definition in ECR.
You could build a very simple Lambda function that queries your ECS Tasks, on each cluster, and writes the Task count, for each unique Task Definition, to CloudWatch Metrics on a per-minute basis. CloudWatch Event Rules allow you to trigger Lambda functions on a cron schedule, so you can customize the period to your liking.
You can use this metric data to help drive scaling decisions about the ECS Cluster, the Services and Tasks running on it, and the underlying EC2 compute instances that support the ECS Cluster.
Hope this helps.
Just found this while trying to work out the current situation. For future searchers: I believe tagging was added some time after this question, in late 2018.
I've not yet worked out if you can set this up in the Console or if it's a feature of the API only, but e.g. the Terraform AWS provider now lets you set service or task definition tags to 'bubble through' to tasks – including Fargate ones – via propagate_tags.
I've just enabled this and it works but forces a new ECS service – I guess this is related to it not being obviously editable in the Console UI.