I am creating an AWS step function where one of the step, let's call it step X, starts a variable number of lambdas. Since these lambda functions are long (they take between 1 and 10 minutes each to complete), I don't want to wait for them in step X. I would be spending money just for waiting. I therefore start them with InvocationType=Event so that they all run asynchronously and in parallel.
Once step X is done starting all these lambdas, I want my step function to wait for all these asynchronous functions to complete. So, a little like described here, I would create some kind of while loop in my step function. This loop would wait until all my asynchronous invocations have completed.
So the problem is: is it possible to query for the status of an AWS lambda that was started with InvocationType=Event?
If it is not possible, I would need my lambdas to persist their status somewhere so that I can poll this status. I would like to avoid this strategy since it does not cover problems that occur outside of my lambda (ex: out of memory, throttling exceptions, etc.)
An asynchronously invoked Lambda is a "fire and forget" use case. There's no straightforward way to get its result. I'm afraid you'll have to write your own job synchronization logic.
instead of polling,(which again is expensive), you can provide a callback, for the lambda to post back asynchronously. once you get all positives for all lambdas, then continue the process.
Since the question was initially posted, AWS added the support for dynamic parallelism in workflows. The need to manually start lambda functions and poll for their completion from within a step function is therefore now an anti-pattern.
Related
I need to orchestrate multiple jobs in parallel (each using Lambdas in AWS), ensure all finish by retrying individual jobs as needed, and then update a state only when all jobs have completed successfully. It will look like this:
This image was taken from the Step Functions documentation. I'm considering whether Step Functions might be an answer.
Any thoughts on how this might look using Lambda (with or without Step Functions)? I'm guessing deadletter queues might be involved to facilitate retries? My biggest unknown is how to update the final state only after all jobs complete and considering whether retries may have occurred.
You are correctly, using AWS Step Functions do resolve your problem.
But, as you are looking for other approaches using pure lambda you will need a state persistence, as the lambda doesn't have that over different functions.
Create a data structure that will be checked at the end of each lambda execution, e.g boolean attributes that corresponds to each process that have to be executed
At the end of each process (lambda execution), change the attribute related to that lambda process to true, than verify if all the attributes are true, if yes you can invoke the lambda responsible to the next step of your pipeline.
If you need retry when errors came up, implement a DLQ and you can have more control of it.
I have one use case where I am supposed to execute a piece of code based on idle time of a given lambda function, I mean if given function has been idle for say 5 mins, my piece of code should run.
Is there any way to check the lambda state/status?
I assume you are looking to avoid lambda cold starts, please leverage Provisioned Concurrency which will have lambda running up with the amount of concurrency setup
https://aws.amazon.com/blogs/aws/new-provisioned-concurrency-for-lambda-functions/
If you did not mean this, then I assume idleness as "no requests processed" by lambda, if yes, then use cloudwatch metric/alarm to monitor # of invocations over a timeframe and then do whatever in its action
I am implementing a solution that involves SQS that triggers a Lambda funcion, that uses a 3rd party API to perform some operations.
That 3rd party API has a limit of requests per second, so I would like to limit the amount of SQS messages processed by my Lambda funtion to a similar rate.
Is there any way to limit the number of messages visibles per second on the SQS or the number of invocations per second of a Lambda function?
[edited]
After some insights given in the comments about AWS Kinesis:
There is no lean solution by handling Kinesis parameters Batch Window, Batch size and payload size, due to the behaviour of Kinesis has that triggers the lambda execution if ANY of the thresholds and reached:
* Given N = the max number of request per second I can execute over the 3rd party api.
* Configuring a Batch Window = 1 second and a Batch Size of N, back presurre should trigger the execution with more than N_MAX requests.
* Configuring a Batch Windows = 1 secnd and a Batch Size of MAX_ALLOWED_VALUE, will be under performant and also does not guarantee executing less than N execution per second.
The simplest solution I have found is creating a Lambda with a fixed execution rate of 1 second, that reads a fixed number of messages N from SQS/Kinesis, and write those in another SQS/Kinesis, having those another Lambda as endpoint.
This is a difficult situation.
Amazon SQS can trigger multiple AWS Lambda functions in parallel, so there is not central oversight of how fast requests can be made to the 3rd-party API.
From Managing concurrency for a Lambda function - AWS Lambda:
To ensure that a function can always reach a certain level of concurrency, you can configure the function with reserved concurrency. When a function has reserved concurrency, no other function can use that concurrency. Reserved concurrency also limits the maximum concurrency for the function, and applies to the function as a whole, including versions and aliases.
Therefore, concurrency can be used to limit the number of simultaneous Lambda functions executing, but this does not necessarily map to "x API calls per second". That would depend upon how long the Lambda function takes to execute (eg 2 seconds) and how many API calls it makes in that time (eg 2 API calls).
It might be necessary to introduce delays either within the Lambda function (not great because you are still paying for the function to run while waiting), or outside the Lambda function (by triggering the Lambda functions in a different way, or even doing the processing outside of Lambda).
The easiest (but not efficient) method might be:
Set a concurrency of 1
Have the Lambda function retry the API call if it is rejected
Thanks to #John Rotenstein gave a comprehensive and detailed answer about SQS part.
If your design is limited to a single consumer than you may replace sqs with kinesis streams. By replacing it, you may use batch window option of kinesis to limit the requests made by consumer. Batch window option is used to reduce the number of invocations
Lambda reads records from a stream at a fixed cadence (e.g. once per second for Kinesis data streams) and invokes a function with a batch of records. Batch Window allows you to wait as long as 300s to build a batch before invoking a function. Now, a function is invoked when one of the following conditions is met: the payload size reaches 6MB, the Batch Window reaches its maximum value, or the Batch Size reaches its maximum value. With Batch Window, you can increase the average number of records passed to the function with each invocation. This is helpful when you want to reduce the number of invocations and optimize cost.
On my project there is REST API which implemented on AWS API Gateway and AWS Lambda. As AWS Lambda functions are serverless and stateless while we make a call to it, AWS starts a container with code of the Lambda function which process our call. According AWS documentation after finishing of lambda function execution AWS don't stop the container and we are able to process next call in that container. Such approach improves performance of the service - only in time of first call AWS spend time to start container (cold start of Lambda function) and all next calls are executed faster because their use the same container (warm starts).
As a next step for improving the performance we created cron job which calls periodically our Lambda function (we use Cloudwatch rules for that). Such approach allow to keep Lambda function "warm" allowing to avoid stopping and restarting of containers. I.e. when the real user will call our REST API, Lambda will not spent time to start a new container.
But we faced with the issue - such approach allow to keep warm only one container of Lambda function while the actual number of parallel calls from different users can be much larger (in our case that's hundreds and sometimes even thousands of users). Is there any way to implement warm up functionality for Lambda function which could warm not only single container, but some desired number of them?
I understand that such approach can affect cost of Lambda function's using and possibly, at all it will be better to use good old application server, but comparison of these approaches and their costs will be the next steps, I think, and in current moment I would like just to find the way to warm desired count of Lambda function containers.
This can be long but bear with me as this would probably give you workaround and may be would make you understand better How Lambda Works ?
Alternatively You can Skip to Bottom "The Workaround" if you are not interested in reading.
For folks who are not aware about cold starts please read this blog post to better understand it. To describe this in short:
Cold Starts
When a function is executed for the first time or after having the
functions code or resource configuration updated, a container will be
spun up to execute this function. All the code and libraries will be
loaded into the container for it to be able to execute. The code will
then run, starting with the initialisation code. The initialisation
code is the code written outside the handler. This code is only run
when the container is created for the first time. Finally, the Lambda
handler is executed. This set-up process is what is considered a cold
start.
For performance, Lambda has the ability to re-use containers created
by previous invocations. This will avoid the initialisation of a new
container and loading of code. Only the handler code will be
executed. However, you cannot depend on a container from a previous
invocation to be reused. if you haven’t changed the code and not too
much time has gone by, Lambda may reuse the previous container.
If you change the code, resource configuration or some time has
passed since the previous invocation, a new container will be
initialized and you will experience a cold start.
Now Consider these scenarios for better understanding:
Consider the Lambda function, in the example, is invoked for the first time. Lambda will create a container, load the code into the container and run the initialisation code. The function handler will then be executed. This invocation will have experienced a cold start. As mentioned in the comments, the function takes 15 seconds to complete. After a minute, the function is invoked again. Lambda will most likely re-use the container from the previous invocation. This invocation will not experience a cold start.
Now consider the second scenario, where the second invocation is executed 5 seconds after the first invocation. Since the previous function takes 15 seconds to complete and has not finished executing, the new invocation will have to create a new container for this function to execute. Therefore this invocation will experience a cold start.
Now to Come up First Part of Problem that you have solved :
Regarding preventing cold starts, this is a possibility, however, it is not guaranteed, the common workaround will only keep warm one container of the Lambda function. To do, you would run a CloudWatch event using a schedule event (cron expression) that will invoke your Lambda function every couple of minutes to keep it warm.
The Workaround:
For your use-case, your Lambda function will be invoked very frequently with a very high concurrency rate. To avoid as many cold starts as possible, you will need to keep warm as many containers as you expect your highest concurrency to reach. To do this you will need to invoke the functions with a delay to allow the concurrency of this function to build and reach the desired amount of concurrent executions. This will force Lambda to spin up the number of containers you desire. This, as a result, can bring up costs and will not guarantee to avoid cold starts.
That being said, here is a break down on how you can keep multiple containers for your function warm at one time:
You should have a CloudWatch Events Rule that is triggered on a schedule. This schedule can be a fixed rate or a cron expression. for example, You can set this rule to trigger every 5 minutes. You will then specify a Lambda function (Controller function) as the target of this rule.
Your Controller Lambda function will then invoke the Lambda function (Function that you want to be kept warm) for as many concurrent running containers as you desire.
There are a few things to consider here:
You will have to build concurrency because if the first invocation
is finished before another invocation starts then this invocation
may reuse the previous invocations container and not create a new
one. To do this you will need to add some sort of delay on the
Lambda function if the function is invoked by the controller
function. This can be done by passing in a specific payload to
the function with these invocations. The lambda function that you
want to be kept warm will then check if this payload exists. If
it does then the function will wait (to build concurrent
invocations), if it does not then the function can execute as
expected.
You will also need to ensure you are not getting throttled on the Invoke Lambda API call if you are calling it repeatedly. Your
Lambda
function should be written to handle this throttling if it occurs
and consider adding a delay between API calls to avoid throttling.
At the End this solution can reduce cold starts but it will increase costs and will not guarantee that cold starts will occur as they are inevitable when working with Lambda.If your application needs faster response times then what occurs with a Lambda cold start, I would recommend looking into having your server on a EC2 instance.
We are using java (spring boot) lambdas and have come to pretty much an identical solution as Kush Vyas's answer above which works very well.
We did find during load testing, however, that a legitimate user request would often occur during the period that the "Controller function" was executing, again causing the inevitable cold start...
So, now in our "Controller function", we have our regular number of X concurrent warm-up requests, however every 5th execution of the function we call our target lambda an additional 2 times. Theory being that we will end up with X+2 lambdas staying warm, but for 4 out of 5 warm up calls there will still be 2 redundant lambdas that can service user requests.
It did reduce our number of cold starts even further (but obviously still not completely) and we are still playing with concurrency/frequency of warm-ups/sleep-time combinations to find optimum solution for us - these values will always likely be dependent on load requirements for a specific situation.
AWS just announced this:
https://aws.amazon.com/about-aws/whats-new/2019/12/aws-lambda-announces-provisioned-concurrency/
Be aware though that it is not free and for our simple use case of keeping 10 lambda instances warm it seems our daily cost would increase from $0.06 to $4
If you use the serverless framework with AWS Lambda, you can use this plugin to keep all your lambdas warm with a certain level of concurrency.
I'd like to share small but useful tip which we use to reduce 'observed by user' delay related to cold starts. In our case the Lambda function handles HTTP requests from front-end via AWS API Gateway, in particular executes search functionality when user type something in the input field. Usually user start to type with some delay after UI is rendered, so we have some time to execute ping call to our Lambda function for warming it up. And when user will make requests to the back-end, most likely the Lambda will be ready for work.
Actually such approach do nothing for fixing the issue with cold starts on the back-end side and you will need to look for other options how to fix it, but it can be an user experience improvement without much efforts (something like hotfix).
One thing you should remember - if your service is public and you care about Google Insights score you should be careful implementing such approach.
I'm trying to use boost::asio for the first time to write a process that connects to N servers reads data from them.
My question regards the way in which asynchronicity works. My design goal is to connect to all servers in parallel, and also read data from every server in parallel. This should be done with async_connect and async_read, and calling io_service::run() N times, then reading the results. And the question is: is it enough to call io_service::run() from a single thread, sequentially, N times, in order to achieve parallelism?
Note that this is a matter of the implementation of asio: specifically, when calling connect_async and write_async, does the call signal the OS to begin connecting/reading before returning, or does it simply delegate a synchronous connect/read task to the worker thread and returns immediately? - case in which calling io_service::run() from a single thread means serial execution of tasks.
My guess is the former, of course, but I need someone to please confirm. I find it off that the documentation for async stuff (http://think-async.com/Asio/boost_asio_1_3_1/doc/html/boost_asio/overview/core/basics.html) doesn't mention when the async_xxx calls return, which would clarify my question.
The heart of asio is an event loop, which begins with the call to io_service::run(), which is a blocking call. When you call async_connect, you queue up the connect operation in the io_services event queue. To achieve parallelism, you must create a thread pool and have each thread call run() on the same io_service instance.