I am looking for some high level guidance on an architecture. I have a provider writing "transactions" to a Kinesis pipe (about 1MM/day). I need to pull those transactions off, one at a time, validating data, hitting other SOAP or Rest services for additional information, applying some business logic, and writing the results to S3.
One approach that has been proposed is use Spark job that runs forever, pulling data and processing it within the Spark environment. The benefits were enumerated as shareable cached data, availability of SQL, and in-house knowledge of Spark.
My thought was to have a series of Lambda functions that would process the data. As I understand it, I can have a Lambda watching the Kinesis pipe for new data. I want to run the pulled data through a bunch of small steps (lambdas), each one doing a single step in the process. This seems like an ideal use of Step Functions. With regards to caches, if any are needed, I thought that Redis on ElastiCache could be used.
Can this be done using a combination of Lambda and Step Functions (using lambdas)? If it can be done, is it the best approach? What other alternatives should I consider?
This can be achieved using a combination of Lambda and Step Functions. As you described, the lambda would monitor the stream and kick off a new execution of a state machine, passing the transaction data to it as an input. You can see more documentation around kinesis with lambda here: http://docs.aws.amazon.com/lambda/latest/dg/with-kinesis.html.
The state machine would then pass the data from one Lambda function to the next where the data will be processed and written to S3. You need to contact AWS for an increase on the default 2 per second StartExecution API limit to support 1MM/day.
Hope this helps!
Related
I know there's a question with the same title but my question is a little different: I got a Lambda API - saveInputAPI() to save the value into a specified field. Users can invoke this API with different parameter, for example:
saveInput({"adressType",1}); //adressType is a DB field.
or
saveInput({"name","test"}) //name is a DB field.
And of course, this hosts on AWS so I'm also using API Gateway as well. But the problem is sometimes, an error like this happened:
As you can see. API No. 19 was invoked first but ended up finishing later
(10:10:16:828) -> (10:10:18:060)
While API No.18 was invoked later but finished sooner...
(10:10:17:611) -> (10:10:17:861)
This leads to a lot of problems in my project. And sometimes, the delay between 2 API was up to 10 seconds. The front project acts independently so users don't know what happens behind. They think they have set addressType to 1 but in reality, the addressType is still 2. Since this project is large and I cannot change this kind of [using only 1 API to update DB value] design. Is there any way for me to fix this problem ?? Really appreciate any idea. Thanks
If updates to Database can't be skipped if last updated timestamp is more recent than the source event timestamp, we need to decouple Api Gateway and Lambda.
Api Gateway writes to SQS FIFO Queue.
Lambda to consume SQS and process the request.
This will ensure older event is processed first.
Amazon Lambda is asynchronous by design. That means that trying to make it synchronous and predictable is kind of waste.
If your concern is avoiding "old" data (in a sense of scheduling) overwrite "fresh" data, then you might consider timestamping each data and then applying constraints like "if you want to overwrite target data, then your source timestamp have to be in the future compared to timestamp of the targeted data"
I use AWS Simple Email Services (SES) for email. I've configured SES to save incoming email to an S3 bucket, which triggers an AWS Lambda function. This function reads the new object and forwards the object contents to an alternate email address.
I'd like to log some basic info. from my AWS Lambda function during invocation -- who the email is from, to whom it was sent, if it contained any links, etc.
Ideally I'd save this info. to a database, but since AWS Lambda functions are costly (relatively so to other AWS ops.), I'd like to do this as efficiently as possible.
I was thinking I could issue an HTTPS GET request to a private endpoint with a query-string containing the info. I want logged. Since I could fire my request async. at the outset and continue processing, I thought this might be a cheap and efficient approach.
Is this a good method? Are there any alternatives?
My Lambda function fires irregularly so despite Lambda functions being kept alive for 10 minutes or so post-firing, it seems a database connection is likely slow and costly since AWS charges per 100ms of usage.
Since I could conceivable get thousands of emails/month, ensuring my Lambda function is efficient is paramount to cost. I maintain 100s of domain names so my numbers aren't exaggerated. Thanks in advance.
I do not think that thousands per emails per month should be a problem, these cloud services have been developed with scalability in mind and can go way beyond the numbers you are suggesting.
In terms of persisting, I cannot really understand - lack of logs, metrics - why your db connection would be slow. From the moment you use AWS, it will use its own internal infrastructure so speeds will be high and not something you should be worrying about.
I am not an expert on billing but from what you are describing, it seems like using lambdas + S3 + dynamoDB is highly optimised for your use case.
From the type of data you are describing (email data) it doesn't seem that you would have neither a memory issue (lambdas have mem constraints which can be a pain) or an IO bottleneck. If you can share more details on your memory used during invocation and the time taken that would be great. Also how much data you store on each lambda invocation.
I think you could store jsonified strings of your email data in dynamodb easily, it should be pretty seamless and not that costly.
Have not used (SES) but you could put a trigger on DynamoDB whenever you store a record, in case you want to follow up with another lambda.
You could combine S3 + dynamoDB. When you store a record, simply upload a file containing the record to a new S3 key and update the row in DynamoDB with a pointer to the new S3 object
DynamoDB + S3
You can now persist data using AWS EFS.
I've built a bit of a pipeline of AWS Lambda functions using the Serverless framework. There are currently five steps/functions, and I need them to run in order and each run exactly once. Roughly, the functions are:
Trigger function by an HTTP request, respond with an ID.
Access and API to get the URL of a resource to download.
Download that resource and upload a copy to S3.
Alter that resource and upload the altered copy to S3.
Submit the altered resource to a different API.
The specifics aren't important, but the question is: What's the best event/trigger to use to move along down this line of functions? The first one is triggered by an HTTP call, but the first one needs to trigger the second somehow, then the second triggers the third, and so on.
I wrote all the code using AWS SNS, but now that I've deployed it to staging I see that SNS often triggers more than once. I could add a bunch of code to detect this, but I'd rather not. And the problem is also compounding -- if the second function gets triggered twice, it sends two SNS notifications to trigger step three. If either of those notifications gets doubled... it's not unreasonable that the last function could be called ten times instead of once.
So what's my best option here? Trigger the chain through HTTP? Kinesis maybe? I have never worked with a trigger other than HTTP or SNS, so I'm not really sure what my options are, and which options are guaranteed to only trigger the function once.
AWS Step Functions seems pretty well targeted at this use-case of tying together separate AWS operations into a coherent workflow with well-defined error handling.
Not sure if the pricing will work for you (can be pricey for millions+ operations) but it may be worth looking at.
Also not sure about performance overhead or other limitations, so YMMV.
You can simply trigger the next lambda asynchronously in your lambda function after you complete the required processing in that step.
So, the first lambda is triggered by an HTTP call and in that lambda execution, after you finish processing this step, just launch the next lambda function asynchronously instead of sending the trigger through SNS or Kinesis. Repeat this process in each of your steps. This would guarantee single time execution of all the steps by lambda.
Eventful Lambda triggers (SNS, S3, CloudWatch, ...) generally guarantee at-least-once invocation, not exactly-once. As you noted you'd have to handle deduplication manually by, for example, keeping track of event IDs in DynamoDB (using strongly consistent reads!), or by implementing idempotent Lambdas, meaning functions that have no additional effects even when invoked several times with the same input. In your example step 4 is essentially idempotent providing that the function doesn't have any side effects apart from storing the altered copy, and that the new copy overwrites any previously stored copies with the same event ID.
One service that does guarantee exactly-once delivery out of the box is SQS FIFO. This service unfortunately cannot be used to trigger Lambdas directly so you'd have to set up a scheduled Lambda to poll the FIFO queue periodically (as per this answer). In your case you could handle step 5 with this arrangement, since I'm assuming you don't want to submit the same resource to the target API several times.
So in summary here's how I'd go about it:
Lambda A, invoked via HTTP, responds with ID and proceeds to asynchronously fetch resource from the API and store it to S3
Lambda B, invoked by S3 upload event, downloads the uploaded resource, alters it, stores the altered copy to S3 and finally pushes a message into the FIFO SQS queue using the altered resource's filename as the distinct deduplication ID
Lambda C, invoked by CloudWatch scheduler, polls the FIFO SQS queue and upon a new message fetches the specified altered resource from S3 and submits it to the other API
With this arrangement even if Lambda B is occasionally executed twice or more by the same S3 upload event there's no harm done since the FIFO SQS queue handles deduplication for you before the flow reaches Lambda C.
AWS Step function is meant for you: https://docs.aws.amazon.com/step-functions/latest/dg/welcome.html
You will execute the steps you want based on previous executions outputs.
Each task/step just need to output a json correctly in the wanted "state".
https://docs.aws.amazon.com/step-functions/latest/dg/concepts-states.html
Based on the state, your workflow will move on. You can create your workflow easily and trigger lambdas, or ECS tasks.
ECS tasks are your own "lambda" environment, running without the constraints of the AWS Lambda environment.
With ECS tasks you can run on Bare metal, on your own EC2 machine, or in ECS Docker containers on ECS and thus have unlimited resources extensible limits.
As compared to Lambda where the limits are pretty strict: 500Mb of disk, execution limited in time, etc.
How come I find so little examples of the KCL being used with AWS Lambda.
https://docs.aws.amazon.com/streams/latest/dev/developing-consumers-with-kcl.html
It does provide a fine implementation for keeping track of your position on the stream (checkpointing).
I want to use the KCL as a consumer. My set-up is a stream with multiple shards. On each shard a Lambda is consuming. I want to use the KCL in the Lambda's to track the position of the iterator on the shard.
Why can't I find anyone who use the KCL with Lambda.
What is the issue here?
Since you can directly consume from Kinesis in your lambdas (using Kinesis as event source) it doesn't make any sense to use KCL within lambda. The event source framework that AWS has built must be using something like KCL to bring lambdas up in response to kinesis events.
It would be super weird to bring up a lambda, initialize KCL in the handler and wait for events during the lambda runtime. Lambda will go down in 5 mins and you'll again do the same thing. Doing this from an EC2 instance makes sense but then you're reimplementing the Lambda - Kinesis integration by yourself. That is what Lambda is, behind the scene.
I do not work for AWS, so obviously I do not know the exact reason why there is no documentation, but here are my thoughts.
First of all, to run the KCL, you need to have the JVM running. This means you can only do this in a lambda using Java because (to my knowledge at this point) there is no way to pull in other sdk, runtimes, etc into a lambda. You chose one runtime at setup. So already they would only be creating documentation for just java lambdas.
Now for the more technical reason. You need to think about what a lambda is doing, and then what the KCL is doing.
Let's start with the Lambda. Lambdas are by design, ephemeral. They can (and will) spin up and go down consistently throughout the day. Of course, you could set up a warming scheme so the lambdas stay up, but they will still have the ephemeral nature to them and this is completely out of your control. In other words, AWS controls when and if a lambda stays active and the exact methods for this is not published. So you can only try to keep things warmed.
What does a KCL do?
Connects to the stream
Enumerates the shards
Coordinates shard associations with other workers (if any)
Instantiates a record processor for every shard it manages
Pulls data records from the stream
Pushes the records to the corresponding record processor
Checkpoints processed records
Balances shard-worker associations when the worker instance count changes
Balances shard-worker associations when shards are split or merged
After reading through this list, lets now go back to the ephemeral nature of lambdas. This means that every single time a lambda comes up or goes down, all of this work needs to happen. This includes a complete rebalance between the shards and workers, pulling data records from the streams, setting checkpoints, etc. You would also need to make sure that you don't ever have more lambdas spun up than the number of shards as they would be worthless (never used in the best case or registered as workers in the worst case potentially causing lost messages. Think what would happen in this scenario with a rebalance.)
OK, technically could you pull it off? If you used Java and you did everything in your power to keep your lambdas warm, it could technically be possible. But back to your question. Why is there no documentation? I never want to say 'never', but generally speaking, Lambdas, with their ephemeral nature, are just not the best use case for the KCL. And if you don't go deep into the weeds on how the KCL works, you'll probably miss something, causing rebalancing issues and potentially causing messages to get lost.
If there is anything inaccurate here please let me know so I can update. Thanks and I hope this helps somebody.
I am using the Titan/DynamoDB library to use AWS DynamoDB as a backend for my Titan DB graphs. My app is very read-heavy and I noticed Titan is mostly executing query requests against DynamoDB. I am using transaction- and instance-local caches and indexes to reduce my DynamoDB read units and the overall latency. I would like to introduce a cache layer that is consistent for all my EC2 instances: A read/write-through cache between DynamoDB and my application to store query results, vertices, and edges.
I see two solutions to this:
Implicit caching done directly by the Titan/DynamoDB library. Classes like the ParallelScanner could be changed to read from AWS ElastiCache first. The change would have to be applied to read & write operations to ensure consistency.
Explicit caching done by the application before even invoking the Titan/Gremlin API.
The first option seems to be the more fine-grained, cross-cutting, and generic.
Does something like this already exist? Maybe for other storage backends?
Is there a reason why this does not exist already? Graph DB applications seem to be very read-intensive so cross-instance caching seems like a pretty significant feature to speedup queries.
First, ParallelScanner is not the only thing you would need to change. Most importantly, all the changes you need to make are in DynamoDBDelegate (that is the only class that makes low level DynamoDB API calls).
Regarding implicit caching, you could add a caching layer on top of DynamoDB. For example, you could implement a cache using API Gateway on top of DynamoDB, or you could use Elasticache. Either way, you need to figure out a way to invalidate Query/Scan pages. Inserting/deleting items will cause page boundaries to change so it requires some thought.
Explicit caching may be easier to do than implicit caching. The level of abstraction is higher, so based on your incoming writes it may be easier for you to decide at the application level whether a traversal that is cached needs to be invalidated. If you treat your graph application as another service, you could cache the results at the service level.
Something in between may also be possible (but requires some work). You could continue to use your vertex/database caches as provided by Titan, and use a low value for TTL that is consistent with how frequently you write columns. Or, you could take your caching approach a step further and do the following.
Enable DynamoDB Stream on edgestore.
Use a Lambda function to stream the edgestore updates to a Kinesis Stream.
Consume the Kinesis Stream with edgestore updates in the same JVM as the Gremlin Server on each of your Gremlin Server instances. You would need to instrument the database level cache in Titan to consume the Kinesis stream and invalidate the cached columns as appropriate, in each Titan instance.