I have a lambda function that I'm calling using boto3. There is a high chance that there will be many concurrent executions and I know that Lambda throttles you if you make too many requests. I am doing this in an synchronous manner, so there are no retries. I want to make sure I know when this will happen, so that I can push requests onto a queue, and try them again at a later time.
Boto3 will return an error if there are too many requests, but I would rather not use try and catch for this. From the boto3 docs:
For example, Lambda returns TooManyRequestsException if executing the function would cause you to exceed a concurrency limit at either the account level (ConcurrentInvocationLimitExceeded ) or function level (ReservedFunctionConcurrentInvocationLimitExceeded ).
Does anyone know of a way to check if the function is available for execution before hand?
Thanks.
Does anyone know of a way to check if the function is available for execution before hand?
No, there isn't a way unless you maintain a counter yourself, which would also be a rough estimate.
Use a try catch statement as this is where it is meant to be used at a code level, use asynchronous invocation or retry your synchronous invocation using exponential backoff (increasing the duration between retries every time).
Related
I want to terminate and exit running cloud function. Function was triggered by Firestore event. What are some ways to do this?
There are some reasons why you want a Cloud Function to terminate itself, for example, to avoid an infinite loop or infinite retries.
To avoid infinite retry loops, set an end condition. You can do this by including a well-defined end condition, before the function begins processing.
A simple yet effective approach is to discard events with timestamps older than a certain time. This helps to avoid excessive executions when failures are either persistent or longer-lived than expected.
Events are delivered at least once, but a single event may result in multiple function invocations. Avoid depending on exactly-once mechanics and write idempotent functions.
Note that updating the function-triggering Firestore document may create subsequent update events, which may cascade into an infinite loop within your function. To solve this problem, use trigger types that ignore updates (such as document.create), or configure your function to only write to Firestore if the underlying value has changed.
Also, note the limitations for Firestore triggers for Cloud Functions.
You might also want to check this example about Cloud Function Termination.
Do not manually exit a Function; it can cause unexpected behavior.
I have a system the receives records from Kinesis stream, Lambda is consuming the stream and invokes one function per shard, this function takes a batch of records and invokes an Async Express Step Function to process each record. The Step Function contains a Task relies on a third party. I have the timeout for this task set but this still can cause high number of concurrent step functions to start executing, when the task is taking longer, as the step functions are not completing quickly enough, causing throttling on Lambda executions further down the line.
To mitigate the issue I am thinking of implementing a "Semaphore" for concurrent Express function executions. There isn't too much out there in terms of similar approach, I found this article but the approach of checking how many active executions there are at a time would only work with Standard Step Function. If it would work with Express I can imagine I could throw error in the function that receives Kinesis record if the arbitrary Step Function execution limit is exceeded, causing Kinesis+Lambda to retry until capacity is available. But as I am using Express workflow, calling ListExecutions is not really an option.
Is there a solution for limiting number of parallel Async Express Step Function executions out there or do you see how I could alternatively implement the "Semaphore" approach?
Have you considered triggering on step function per lambda invoke and using a map state to do the multiple records per batch? The map state allows you to limit the number of concurrent executions. This doesn’t address multiple executions of the step function, and could lead to issues with timeouts if you are pushing the boundary of the five minute limits for express functions.
I think if you find that you need to throttle something across partitions you are going to be in a world of complex solutions. One could imagine a two phase commit system of tracking concurrent executions and handling timeouts, but these solutions are often more complicated than they are worth.
Perhaps the solution is to make adjustments downstream to reduce the concurrency there? If you end up with other lambdas being invoked too many times at once you can put SQS in front of them and enable batching as well as manage throttling there. In general you should use something like SQS to trigger lambdas at the point where high concurrency is a problem, and less so at points that feed into it. In other words if your current step functions can handle the high concurrency you should let them, and anything has issues as a result of it should be managed at that point.
I understand that lambdas re-use the execution context to avoid bootstrapping and latency cost on each invocation. As documented here.
If the lambda is used frequently (at least once a minute) is there a specific duration before the execution context is flushed and re-cached? Or is it kept in cache indefinitely?
I don't know any hard limits Lambda kills a function if it's called regularly, though you shouldn't rely on it. A function can be killed any time and for no particular reason.
If lambda function stays inactive for 15 minutes , then the execution context is flushed and re-cached , however you can keep it warm by invoking it every 14th minute with the help of cloudwatch events or any relevant trigger.
I have an AWS Lambda function which is internally calling multiple Lambda functions based on input. It can call multiple internal functions or same function multiple times which is completely based on input payload.
I am using 'Invoke' to call internal functions. It's all working as required. Sometimes internal functions are never called and caller lambda times out after the configured time out seconds. Sometimes it starts working after redeployment or increase in memory/time out and redeploy.
Has anyone faced this and is there any solution ? As it fails some times, not sure of what is the cause. Please suggest.
I have a large file being uploaded on S3, and for each line in the file I need to make a long running rest API call. I'm trying to figure out the best way to break up the work. My current flow idea is
Lambda (break up file by line) -> SNS (notification per line) -> Lambda (separate per line/notification)
This seems like it is a common use case, but I can't find many references to it, am I missing something? Is there a better option to break up my work and get it done in a reasonable amount of time?
The Best way is going to be subjective. The method you are using currently, Lambda->SNS->Lambda, is one possible method. As JohnAllen pointed out, you could simply do Lambda->Lambda.
Your scenario reminds me of this project, which has a single Lambda function adding items to a Kinesis stream, which then triggers many parallel Lambda functions.
I think Lambda->Kinesis->Lambda might be a better fit for your use case than Lambda->SNS->Lambda if you are generating a very large number of Lambda tasks. I would be worried that the SNS implementation would run up against the maximum number of concurrent Lambda functions, while the Kinesis implementation would queue them up and handle that gracefully.