I have a use case where I have a AWS Step function where is each task is a Lambda. One of the lambda expects a particular version file to be present in an S3 location. The particular version of file is uploaded by an external service. The only way to know if version recently uploaded is the one we are interested in, is by looking for a data attribute inside the file.
If data attribute is missing, then I am not interested in that version and in that case I want to wait for an hour and re-execute the same lambda to check if newer version uploaded is the version we are interested in, until either we find the correct version or exhaust retries.
If at any point within retry limit I find the data attribute, the next task should be executed.
Any advice is much appreciated on how to tackle this.
Use a choice state after your lambda. If the lambda output indicates the version wasn't found, then redirect to a wait state set for 1 hour which feeds back into the lambda. If the lambda output indicates the version was found, then continue with processing.
Hope this helps!
Related
I am interested in doing automated real-time data processing on AWS using Lambda and I am not certain about how I can trigger my Lambda function. My data processing code involves taking multiple files and concatenating them into a single data frame after performing calculations on each file. Since files are uploaded simultaneously onto S3 and files are dependent on each other, I would like the Lambda to be only triggered when all files are uploaded.
Current Approaches/Attempts:
-I am considering an S3 trigger, but my concern is that an S3 Trigger will result in an error in the case where a single file upload triggers the Lambda to start. An alternate option would be adding a wait time but that is not preferred to limit the computation resources used.
-A scheduled trigger using Cloudwatch/EventBridge, but this would not be real-time processing.
-SNS trigger, but I am not certain if the message can be automated without knowing the completion in file uploads.
Any suggestion is appreciated! Thank you!
If you really cannot do it with a scheduled function, the best option is to trigger a Lambda function when an object is created.
The tricky bit is that it will fire your function on each object upload. So you either can identify the "last part", e.g., based on some meta data, or you will need to store and track the state of all uploads, e.g. in a DynamoDB, and do the actual processing only when a batch is complete.
Best, Stefan
Your file coming in parts might be named as -
filename_part1.ext
filename_part2.ext
If any of your systems is generating those files, then use the system to generate a final dummy blank file name as -
filename.final
Since in your S3 event trigger you can use a suffix to generate an event, use .final extension to invoke lambda, and process records.
In an alternative approach, if you do not have access to the server putting objects to your s3 bucket, then with each PUT operation in your s3 bucket, invoke the lambda and insert an entry in dynamoDB.
You need to put a unique entry per file (not file parts) in dynamo with -
filename and last_part_recieved_time
The last_part_recieved_time keeps getting updated till you keep getting the file parts.
Now, this table can be looked up by a cron lambda invocation which checks if the time skew (time difference between SYSTIME of lambda invocation and dynamoDB entry - last_part_recieved_time) is enough to process the records.
I will still prefer to go with the first approach as the second one still has a chance for error.
Since you want this to be as real time as possible, perhaps you could just perform your logic every single time a file is uploaded, updating the version of the output as new files are added, and iterating through an S3 prefix per grouping of files, like in this other SO answer.
In terms of the architecture, you could add in an SQS queue or two to make this more resilient. An S3 Put Event can trigger an SQS message, which can trigger a Lambda function, and you can have error handling logic in the Lambda function that puts that event in a secondary queue with a visibility timeout (sort of like a backoff strategy) or back in the same queue for retries.
I'm currently struggling to configure automated input to my AWS StepFunctions state machine. Basically, I am trying to set up a state machine that is notified whenever an object create event takes place in a certain S3 bucket. When that happens, the input is passed to a choice state which checks the file size. If the file is small enough, it invokes a Lambda function to process the file contents. If the file is too large, it invokes a Lambda to split up the file into files of manageable size, and then invokes the other Lambda to process the contents of those files. The problem with this is that I cannot figure out a way to pass the file size in as input to the state machine.
I am generally aware of how input is passed to StepFunctions, and I know that S3 Lambda triggers contain file size as a parameter, but I still haven't been able to figure out a practical way of passing file size as an input parameter to a StepFunctions state machine.
I would greatly appreciate any help on this issue and am happy to clarify or answer any questions that you have to the best of my ability. Thank you!
Currently S3 events can't triggers Step Function directly, so one option would be to create a S3 event that triggers a lambda. The lambda works as a proxy and passes the file info to the step function and kicks it off, also you can select data you want and only pass selective data to Step Functions.
The other option is to configure a state machine as a target for a CloudWatch Events rule. This will start an execution when files are added to an Amazon S3 bucket.
The first option is more flexible.
I have written a cloud storage trigger based cloud function. I have 10-15 files landing at 5 secs interval in cloud bucket which loads data into a bigquery table(truncate and load).
While there are 10 files in the bucket I want cloud function to process them in sequential manner i.e 1 file at a time as all the files accesses the same table for operation.
Currently cloud function is getting triggered for multiple files at a time and it fails in BIgquery operation as multiple files trying to access the same table.
Is there any way to configure this in cloud function??
Thanks in Advance!
You can achieve this by using pubsub, and the max instance param on Cloud Function.
Firstly, use the notification capability of Google Cloud Storage and sink the event into a PubSub topic.
Now you will receive a message every time that a event occur on the bucket. If you want to filter on file creation only (object finalize) you can apply a filter on the subscription. I wrote an article on this
Then, create an HTTP functions (http function is required if you want to apply a filter) with the max instance set to 1. Like this, only 1 function can be executed in the same time. So, no concurrency!
Finally, create a PubSub subscription on the topic, with a filter or not, to call your function in HTTP.
EDIT
Thanks to your code, I understood what happens. In fact, BigQuery is a declarative system. When you perform a request or a load job, a job is created and it works in background.
In python, you can explicitly wait the end on the job, but, with pandas, I didn't find how!!
I just found a Google Cloud page to explain how to migrate from pandas to BigQuery client library. As you can see, there is a line at the end
# Wait for the load job to complete.
job.result()
than wait the end of the job.
You did it well in the _insert_into_bigquery_dwh function but it's not the case in the staging _insert_into_bigquery_staging one. This can lead to 2 issues:
The dwh function work on the old data because the staging isn't yet finish when you trigger this job
If the staging take, let's say, 10 seconds and run in "background" (you don't wait the end explicitly in your code) and the dwh take 1 seconds, the next file is processed at the end of the dwh function, even if the staging one continue to run in background. And that leads to your issue.
The architecture you describe isn't the same as the one from the documentation you linked. Note that in the flow diagram and the code samples the storage events triggers the cloud function which will stream the data directly to the destination table. Since BigQuery allow for multiple streaming insert jobs several functions could be executed at the same time without problems. In your use case the intermediate table used to load with write-truncate for data cleaning makes a big difference because each execution needs the previous one to finish thus requiring a sequential processing approach.
I would like to point out that PubSub doesn't allow to configure the rate at which messages are sent, if 10 messages arrive to the topic they all will be sent to the subscriber, even if processed one at a time. Limiting the function to one instance may lead to overhead for the above reason and could increase latency as well. That said, since the expected workload is 15-30 files a day the above maybe isn't a big concern.
If you'd like to have parallel executions you may try creating a new table for each message and set a short expiration deadline for it using table.expires(exp_datetime) setter method so that multiple executions don't conflict with each other. Here is the related library reference. Otherwise the great answer from Guillaume would completely get the job done.
I'm experimenting with dynamo db and lambda and am having trouble with the following flow:
Lambda A is triggered by a put to S3 event. It takes the object, an audio file, calculates its duration and writes a record in dynamoDB for each 30 second segment.
Lambda B is triggered by dynamoDB, downloads the file from S3 and operates on the 30 second record defined in the dynamo row.
My trouble is that when I run this flow, function A writes all of the rows required to dynamo, by function B
Does not seem to be triggered for each row in dynamo
Times out after 5 minutes.
Configuration
Function B is set with the highest memory and 5 minute expiration
The trigger is set with a batch size of 1 and starting position latest
Things I've confirmed
When function B is triggered, the download from S3 happens fast. This does not seem to be the blocker
When I trigger function B with a test event it executes perfectly.
When I look at the cloudwatch metrics, function B has a nearly 100% error rate in invocation. I can't tell if this means he function was invoked and had an error or could not be invoked at all.
Has anyone had similar issues? Any idea what to check next?
Thanks
I had the same problem, the solution was to create a VERSION from the Lambda and NOT to use the $LATEST Version, but a 'fixed' one.
It is not possible to use the latest ever-changing version to build a trigger upon.
Place to do that:
Lambda / Functions / YourLambdaName / Qualifiers Dropdown on the page / Switch versions/aliases / Version Tab -> check that you have a version
If not -> Actions / Publish new version
Check for DynamoDB "Stream" is it is enabled on the table.
Checkout this
5 min timeout is default for lambda, you can find this mentioned in forums.
I am seeking advice on what's the best way to design this -
Use Case
I want to put multiple files into S3. Once all files are successfully saved, I want to trigger a lambda function to do some other work.
Naive Approach
The way I am approaching this is by saving a record in Dynamo that contains a unique identifier and the total number of records I will be uploading along with the keys that should exist in S3.
A basic implementation would be to take my existing lambda function which is invoked anytime my S3 bucket is written into, and have it check manually whether all the other files been saved.
The Lambda function would know (look in Dynamo to determine what we're looking for) and query S3 to see if the other files are in. If so, use SNS to trigger my other lambda that will do the other work.
Edit: Another approach is have my client program that puts the files in S3 be responsible for directly invoking the other lambda function, since technically it knows when all the files have been uploaded. The issue with this approach is that I do not want this to be the responsibility of the client program... I want the client program to not care. As soon as it has uploaded the files, it should be able to just exit out.
Thoughts
I don't think this is a good idea. Mainly because Lambda functions should be lightweight, and polling the database from within the Lambda function to get the S3 keys of all the uploaded files and then checking in S3 if they are there - doing this each time seems ghetto and very repetitive.
What's the better approach? I was thinking something like using SWF but am not sure if that's overkill for my solution or if it will even let me do what I want. The documentation doesn't show real "examples" either. It's just a discussion without much of a step by step guide (perhaps I'm looking in the wrong spot).
Edit In response to mbaird's suggestions below-
Option 1 (SNS) This is what I will go with. It's simple and doesn't really violate the Single Responsibility Principal. That is, the client uploads the files and sends a notification (via SNS) that its work is done.
Option 2 (Dynamo streams) So this is essentially another "implementation" of Option 1. The client makes a service call, which in this case, results in a table update vs. a SNS notification (Option 1). This update would trigger the Lambda function, as opposed to notification. Not a bad solution, but I prefer using SNS for communication rather than relying on a database's capability (in this case Dynamo streams) to call a Lambda function.
In any case, I'm using AWS technologies and have coupling with their offering (Lambda functions, SNS, etc.) but I feel relying on something like Dynamo streams is making it an even tighter coupling. Not really a huge concern for my use case but still feels dirty ;D
Option 3 with S3 triggers My concern here is the possibility of race conditions. For example, if multiple files are being uploaded by the client simultaneously (think of several async uploads fired off at once with varying file sizes), what if two files happen to finish uploading at around the same time, and two or more Lambda functions (or whatever implementations we use) query Dynamo and gets back N as the completed uploads (instead of N and N+1)? Now even though the final result should be N+2, each one would add 1 to N. Nooooooooooo!
So Option 1 wins.
If you don't want the client program responsible for invoking the Lambda function directly, then would it be OK if it did something a bit more generic?
Option 1: (SNS) What if it simply notified an SNS topic that it had completed a batch of S3 uploads? You could subscribe your Lambda function to that SNS topic.
Option 2: (DynamoDB Streams) What if it simply updated the DynamoDB record with something like an attribute record.allFilesUploaded = true. You could have your Lambda function trigger off the DynamoDB stream. Since you are already creating a DynamoDB record via the client, this seems like a very simple way to mark the batch of uploads as complete without having to code in knowledge about what needs to happen next. The Lambda function could then check the "allFilesUploaded" attribute instead of having to go to S3 for a file listing every time it is called.
Alternatively, don't insert the DynamoDB record until all files have finished uploading, then your Lambda function could just trigger off new records being created.
Option 3: (continuing to use S3 triggers) If the client program can't be changed from how it works today, then instead of listing all the S3 files and comparing them to the list in DynamoDB each time a new file appears, simply update the DynamoDB record via an atomic counter. Then compare the result value against the size of the file list. Once the values are the same you know all the files have been uploaded. The down side to this is that you need to provision enough capacity on your DynamoDB table to handle all the updates, which is going to increase your costs.
Also, I agree with you that SWF is overkill for this task.