We're trying to move to AWS and to use DynamoDB. It'd be nice to keep everything under DynamoDB so there aren't extraneous types of databases, but aside from half complete research projects I'm not really finding anything to use for a scheduler. There's going to be dynamically set schedules in the range of thousands+, possibly with many running at the same time. For languages, Java or at least JVM would be awesome.
Does anyone know a good Scheduler for DynamoDB or other AWS technology?
---Addendum
When I say scheduler I'm thinking of something all purpose like quartz. I want to set a cron and it runs at that time with the code I give it. This isn't doing some AWS task, this is a task internal to our product. SWF's cron runs inside the VM, so I'm worried what happens when the VM is down. Data Pipeline seems a bit too much. I've been looking into making a dynamodb job store for quartz, consistent read might get around the transaction and consistency issues, but I'm hesitant, might be biting off a lot with a lot of hard to notice problems.
Have you looked at AWS Simple Workflow? You would use the AWS Flow Framework to program against the service, and they have a well documented Java API with lots of samples. They support continuous workflows with timers which you can use to run periodic code (see code example here). I'm using SWF and the Flow Framework for Ruby to run async code that gets kicked off from my main app, and it's been working great.
Another new option for you is to look at AWS Lambda. You can attach your Lambda function code directly to a DynamoDB table update event, and Lambda will spin up and shut down the compute resources for you, without you having to manage a server to run your code. Also, recently, AWS launched the ability to call the Lambda function directly -- e.g. you could have an external timer or other code that triggers the function on a specific schedule.
Lastly, this SO thread may have other options for you to consider.
Another option is to use AWS Lambda Scheduled Functions (newly announced on October 8th 2015 at AWS re:Invent).
Here is a relevant snippet from the blog (source):
Scheduled Functions (Cron)
You can now invoke a Lambda function on a regular, scheduled basis. You can specify a fixed rate (number of minutes, hours, or days between invocations) or you can specify a Cron-like expression:
Related
I have a set of calculations that needs to run in a batch, and the workload is easily parallelized across machines. The work to be done is already done within a Docker container. I'm trying to understand the easiest way for me to run this workload in a highly parallel way on AWS. However, in trying to figure out where to begin I'm having trouble finding the right entrypoint. I read about AWS Batch and AWS Fargate, but each time I try to go down one of those paths to learn about them in more detail, more AWS services start popping up (Lamdas, Step Functions, ECS, AutoScaling groups), with each article having a different combination. Furthermore, I start thinking about the problem as a Batch vs Fargate problem, and then I find another article that talks about Batch + Fargate, or X + ECS + ....
I'm having trouble finding the appropriate introduction to the choices so I can get started with setting something up and getting some experience. Any pointers on which direction I might go or some resources for me to look at?
AWS containers services team member here. Your question triggers all my button cause I have been working on a deliverable to address some of this confusion ("where do I start with xyz?"). I can try to answer your question briefly here but if you want to read more (perhaps way more than you'd need feel free to contact me offline (mreferre at amazon dot com will work).
First and foremost it's not a Vs but it's an AND. Think of all these products you mention being distributed at different layers of the stack (this is a draft visual in the deliverable):
Fargate represents capacity (where your container is running), ECS represents a core containers orchestrator and Batch is one of the provisioners on top of the container orchestrator. Lambda is something separate and that live on its own. The options for your specific use case seem to be:
Lambda
ECS/Fargate
Batch/ECS/Fargate
Step Functions/ECS/Fargate (this one is outside of analysis and you don't see it in my visual - wondering if I should add it).
As others have hinted you probably want to use Lambda if your model is event-driven (e.g. if you want to fire up a dedicated function for every event like a new file uploaded to S3).
You probably do not want to use a naked ECS/Fargate solution because it would require more work to deal with the triggering and the scheduling of your batch jobs.
You probably want to use either Batch or Step Functions to schedule jobs on ECS/Fargate. I'd argue SF is good if you have basic workflows that you need to deal with and Batch if you need to manage complex jobs at scale. Perhaps this 35 mins presentation that I did last year can provide a bit more background on these Batch Vs SF differences.
Let me know if you have any additional questions because this discussion is super useful for the positioning I am trying to build.
I am writing a syncing/ETL app inside AWS. It works as follows:
The source of the data is outside of AWS
Whenever new data is changed/added AWS is alerted via API Gateway (REST)
The REST API triggers a lambda function that does ETL and stores the data in CSV format to S3
This works fine for small tables. However, we are dealing with larger amount of data lately and I have to switch to Fargate (EKS/ECS) instead of lambda. As you can imagine these will be long running jobs and not cheap to perform. Usually when the data is changed in it changes multiple times within a period of 5 minutes, say for example 3 times. So REST API gets a ping 3 times in a row and triggers the ETL jobs 3 times as well. This is very inefficient as you can imagine.
I came up with idea that every time that REST API is triggered lets wait for 5 minutes if the API has not been invoked during the waiting period do ETL otherwise do nothing. I think I can do the waiting using Step Functions. However I cannot find a suitable way to store hash/id of the latest ping to API to one single variable. I thought maybe I can store the hash to an S3 object and after 5 minutes check to see if it is the same as the variable in my step function, but apparently ordinality is not guaranteed. I looked into SQS but the fact that is a FIFO is not very convenient and way more than what I actually need. I am pretty sure that other people have had a similar issue and there must a standard solution for this problem. I could not find any by googling and hence my plea here
Thanks
From what I understand, Amazon DynamoDB is the store you are looking for to save the state of your job.
Also, please note that SQS is not FIFO by default. Using SQS won't prevent you from storing your job state.
What I would do:
Trigger a job and store the state in DynamoDB. Do not further launch job until the job state is done.
Orchestrate the ETL from Step Functions (including the 5 minutes wait)
You can also expire your jobs so DynamoDB will automatically clean them up with time.
We are trying the lambda for our ETL job which is written in Clojure.
Our architecture is the scheduler will trigger the parent lambda, then the parent lambda trigger 100 child lambda and counter lambda. The child lambdas after completion of their work it will write the data to s3 . The counter lambda will check the number of files in the S3 , if it is 100 then it will combine all the files and save it to S3, otherwise it will span a new counter lambda and die.
All the positive scenario is working fine, but if any child fails then the counter lambda will end up in the indefinite loop, because there wont be 100 files.
If there any proper way of spanning child lambda, monitor it and if it fails need to restart or retry that alone ?
Is there any good Clojure lambda framework ?
Process monitoring is not built into any lambda clojure libraries that I know of, so for this case I'd recommend taking a page out of the erlang metaphorical play book (supervisor trees) and say that to have a dependable distributed system every actor needs a monitor so a decent approach would be to have a watcher for each lambda task. This can really simplify the error handling cases along the "let it crash" philosophy.
So this would leave you with this list of lambdas:
counters:
a watcher/restarter for the counter (you kind of already have this)
workers x100
supervisors x100
Each supervisor only checks for the presence of one particular file and restarts one particular lambda if it does not exist. this gets much easier if your process is idempotent, so you don't have to worry too much if a file is produced twice, though it's not too hard to check if the lambda a supervisor is watching is still running using the aws api. this supervisor can be started by the thing it's supervising or by the thing that starts the rest of the system, whatever is easier for your codebase. You likely don't need to explicitly start the workers, the supervisor can do that.
The important part is to add cloudwatch or whatever your favourite eventing system is (mine is riemann) so you can add alerts to know when you need to watch the watchers.
There is easy way out there in AWS is called AWS Step Functions. Step Functions provides a graphical console to arrange and visualize the components of your application as a series of steps. Define steps using the AWS Step Functions console or API, a fluent Java API, or AWS CloudFormation templates.
Step makes it simple to orchestrate AWS Lambda functions. Irrespective of language of function, it manages all the lambdas.
Step is good for following use cases
Run sequence functions
Run functions in parallel
Select functions based on data
Retry the functions
try/catch/finally for functions
Running the code for hours
I have an application on an AWS EC2 instance that runs once daily. The application fetches some files from a web service, parses the files line by line, updates a database, updates S3 files based on changes in the database, sends notification emails to customers as well as a few other tasks.
This is a series of logical tasks that must take place in sequence, although some of the tasks can be thought of as sub-tasks that can be executed in parallel. All tasks are a combination of Perl scripts and Java programs, with a single Perl script acting as the manager that executes each in turn. Some tasks can take as long as 45 minutes to complete, and the whole process can take up to 3 hours in total.
I'd like to make this whole process serverless. My initial idea was to use AWS Lambda, whereby each task would execute as a Lambda function, until I discovered Lambda functions impose a 5 minute execution timeout. It seems like the AWS Step Functions service is actually a better fit for my use case, but my understanding is that this service is backed by Lambda, so the tasks will still have the 5 min execution limitation.
(I'm also aware that I would have to re-write my Perl scripts to a language supported by Lambda).
I assume that I can work around the execution time limit by refactoring my code into smaller functions that will guarantee to complete in under 5 minutes. In my particular situation though, this seems inefficient.
Currently the database update task processes lines from a file one at a time. For this to work with Lambda, a Lambda function would need to handle only a single line from the file (or a very small number of lines) in order to guarantee not spilling over 5 minutes execution time. This would involve opening and closing a connection with the database on every invocation of the Lambda function. Also, each line processed should result in an entry written to a file, to be stored in S3. Right now, I just keep a file handle in memory and write the file to S3 when all lines are processed, but with Lambda I would need to keep reading the file, updating it and writing it back to S3.
What I'm asking is:
Is my use case a bad fit for AWS Lambda and/or AWS Step Functions?
Have I misunderstood how these services work?
Is there another AWS service that would be a better fit for my use case?
After further research, I think AWS Batch might be a good idea.
What you want are called Activity Workers. Tl;dr: You register "activities" and each gets an ARN. Then you can put that ARN in the resource field of Task states and then you run some code (the "worker") somewhere (in a Lambda, on EC2, in your basement, wherever) that polls for tasks identified by that ARN, then calls back to report success or failure. Activity Workers can run for up to a year.
Step-by-step details at the AWS docs
In response to RTF's comment, here's a deeper dive: Suppose you have code to color turtles in color_turtles.pl. So what you do is call the CreateActivity API - see http://docs.aws.amazon.com/step-functions/latest/apireference/API_CreateActivity.html - giving the name "ColorTurtles" and it'll give you back an ARN, a string beginning arn:aws... Then in your state machine you make a Task state with that ARN as the value of the resource field. Then you add code to color_turtles.pl to poll the service with http://docs.aws.amazon.com/step-functions/latest/apireference/API_GetActivityTask.html - whenever a machine you're running gets to that task, it'll go look for activity workers polling. It'll give your polling worker the input for the task, then you process the input and generate some output, and call SendTaskSuccess or SendTaskFailure. All these are just REST HTTP calls, so you can run them anywhere and I mean anywhere; in a Lambda, on an EC2 instance, or on some computer anywhere on the Internet.
So to answer your questions:
1) Yeah, if you've got something that'll run for around 45 minutes, whilst you could engineer it with Lambda/Step functions you're probably better off getting a EC2 micro instance.
2)Nope you've pretty much got it.
3) As above you want to go with EC2 for this, there's a good article on using Data Pipelines to start / stop an EC2 instance here that way by starting instance only when you need it the cost(if any) is negligible.
I have jobs that run in this fashion normally you can get away with with a t2.micro instance which is free tier eligible.
You can also run your perl scripts on an EC2 instance so no need to rewrite them!
I will start with that it seems you are looking for workflow solutions on AWS. SWF and Step functions are the two most popular ones. Steps function is more recent offering and encouraged by AWS more than SWF.
SWF has native capability to handle long-running tasks, the downside is that you have to provide your own execution environment for deciders (can't use lambda).
With step functions, you can do this in two different ways. One of the approaches is suggested by Tim in his answer. There is an alternative way to achieve the same which is using job poller in step functions. Job pollers have the ability to call (poll) your resource and find out if the task is done and if not you can send execution in wait mode for the specified time. As mentioned above maximum execution time allowed currently for any workflow is 1 year. In case you have tasks which may take longer than 1 year, you can't use step functions in its current form.
I'd like to be able to create a "job" that will execute in an arbitrary time from now... Let's say 1 year from now. I'm trying to come up with a stable, distributed system that doesn't rely on me maintaining a server and scheduling code. (Obviously, I'll have to maintain the servers to execute the job).
I realize I can poll simpleDB every few seconds and check to see if there's anything that needs to be executed, but this seems very inefficient. Ideally I could create an Amazon SNS topic that would fire off at the appropriate time, but I don't think it's possible.
Alternatively, I could create a message in the Amazon SQS that would not be visible for 1 year. After 1 year, it becomes visible and my polling code picks up on it and executes it.
It would seem this is a topic like Singletons or Inversion Control that Phd's have discussed and come up with best practices for. I can't find the articles if there any.
Any ideas?
Cheers!
The easiest way for most people to do this would be to run at least an EC2 server with a cron job on the EC2 server to trigger an action. However, the cost of running an EC2 server 24 hours a day for a year just to trigger an action would be around $170 at the cheapest (8G t1.micro with Heavy Utilization Reserved Instance). Plus, you have to monitor that server and recover from failures.
I have sketched out a different approach to running jobs on a schedule that uses AWS resources completely. It's a bit more work, but does not have the expense or maintenance issues with running an EC2 instance.
You can set up an Auto Scaling schedule (cron format) to start an instance at some point in the future, or on a recurring schedule (e.g., nightly). When you set this up, you specify the job to be run in a user-data script for the launch configuration.
I've written out sample commands in the following article, along with special settings you need to take care of for this to work with Auto Scaling:
Running EC2 Instances on a Recurring Schedule with Auto Scaling
http://alestic.com/2011/11/ec2-schedule-instance
With this approach, you only pay for the EC2 instance hours when the job is actually running and the server can shut itself down afterwards.
This wouldn't be a reasonable way to schedule tens of thousands of emails with an individual timer for each, but it can make a lot of sense for large, infrequent jobs (a few times a day to once per year).
I think it really depends on what kind of job you want to execute in 1 year and if that value (1 year) is actually hypothetical. There are many ways to schedule a task, windows and linux both offer a service to schedule tasks. Windows being Task Scheduler, linux being crontab. In addition to those operating system specific solutions you can use Maintenance tasks on MSSQL server and I'm sure many of the larger db's have similar features.
Without knowing more about what you plan on doing its kind of hard to suggest any more alternatives since I think many of the other solutions would be specific to the technologies and platforms you plan on using. If you want to provide some more insight on what you're going to be doing with these tasks then I'd be more than happy to expand my answer to be more helpful.