In the I discussed the pros & cons of following the (SRP) when moving to the serverless paradigm. last post Single Responsibility Principle One of the questions that popped up on both Twitter and Medium is . It is a FAQ whenever I speak at user groups or conferences about AWS Lambda, alongside “how do you deal with shared code?” “how do you deal with shared infrastructure that doesn’t clearly belong to any one particular service?” So here are my thoughts on these two questions. Again, I’m not looking to convince you one way or the other and I don’t believe there’s a “right” answer that’d work for everyone. This is simply me talking out loud and sharing my internal thought process with you and hopefully get you asking the same questions of your own architecture. As ever, if you disagree with my assessment or find flaws in my thinking, please let me know via the comments section below. As you build out your system with all these little Lambda functions, there are no doubt going to be business logic, or utility code that you want to share and reuse amongst your Lambda functions. When you have a group of functions that are highly cohesive and are organised into the same repo — like the functions that we created to implement the — then sharing code is easy, you just do it via a module inside the repo. timeline feature in the Yubl app The functions in this diagram form a cohesive unit that work together to implement a feature. But to share code more generally between functions across the service boundary, it can be done through shared libraries, perhaps published as private NPM packages so they’re only available to your team. Or, you can share business logic by encapsulating them into a service, and there are couple of considerations you should make in choosing which approach to use. The serverless architecture that powered Yubl. Shared library vs Service visibility When you depend on a shared library, that dependency is declared explicitly, in the case of Node.js, this dependency is declared in the . package.json When you depend on a service, that dependency is often not declared at all, and may be discovered only through logging, and perhaps explicit attempts at tracing, maybe using the service. AWS X-Ray deployment When it comes to deploying updates to these shared code, with shared library, you can publish a new version but you still have to rely on the consumers of your shared library to update. Whereas with a service, you as the owner of the service has the power to decide when to deploy the update, and you can even use techniques such as canary deployment or feature flags to roll out updates in a controlled, and safe manner. versioning With libraries, you will have multiple active versions at the same time (as discussed above) depending on the upgrade and deployment schedule of the consumers. In fact, there’s really no way to avoid it entirely, not even with the best efforts at a coordinated update, there will be a period of time where there are multiple versions active at once. With services, you have a lot more control, and you might choose to run multiple versions at the same time. This can be done via canary deployment or to run multiple versions side-by-side by maybe putting the version of the API in the URL, as people often do. There are multiple ways to version an API, but I don’t find any of them to be satisfactory. did a on this topic and went through several of these approaches and why they’re all bad, so check out his talk if you want to learn more about the perils of API versioning. Sebastien Lambla good talk backward compatibility With a shared library, you can communicate backward compatibility of updates using — where a MAJOR version update signifies a breaking change. If you follow semantic versioning with your releases then backward compatibility can be broken in a controlled, well communicated manner. semantic versioning Most package managers by allowing the user to decide whether automatic updates should increment to the next MAJOR or MINOR version. support semantic versioning With a service, if you roll out a breaking change then it’ll break anyone that depends on your service. This is where it ties back to versioning again, and as I already said, none of the approaches that are commonly practiced feel satisfactory to me. I have had this discussion with my teams many times in the past, and they always ended with the decision to “always maintain backward compatibility” as a general rule, unless the circumstances dictate that we have to break the rule and do . something special isolation With a shared library, you generally expose more than you need, especially if it’s for internal use. And even if you have taken the care to consider what should be part of the library’s public API, there’s always a way for the consumer to get at those internal APIs via reflection. With a service, you are much more considerate towards what to expose via the service’s public API. You have to, because anything you share via the service’s public API is an explicit design decision that requires effort. The internal workings of that service is also hidden from the consumer, and there’s no direct (and easy) way for the consumers to access them so there’s less risk of the consumers of our shared code accidentally depending on internal implementation details. The worst thing that can happen here is if the consumers of your API start to depend on those (accidentally leaked) implementation details as features… failure When a library fails, your code fails, and it’s often loud & clear and you get the stack trace of what went wrong. With a service, it may fail, or maybe it just didn’t respond in time before you stop waiting for the response. As a consumer you often can’t distinguish between a service being down, from it being slow. When that happens, retries can become tricky as well if the actions you’re trying to perform would modify state and that the action is . not idempotent Partial failures are also very difficult to deal with, and often requires elaborate patterns like the in order to rollback state changes that have already been introduced in the transaction. Saga pattern latency Finally, and this is perhaps the most obvious, that calling a service introduces network latency, which is significantly higher than calling a method or function in a library. Managing shared infrastructure Another question that I get a lot is . “how do you manage shared AWS resources like DynamoDB tables and Kinesis streams?” If you’re using the framework then you can manage these directly in your files, and add them as additional resources like below. Serverless serverless.yml CloudFormation This is actually the approach I took in my video course , but that is because the demo app I’m leading the students to build is a project with well defined start and end state. Production-Ready Serverless But what works in a project like that won’t necessary work when you’re . In the context of building a product, there are some problems with this approach. building a product that will evolve continuously over time can delete user data "sls remove" Since these resources are tied to the stack for the (as in, the framework) project, if you ever decide to delete the functions using the command then you’ll delete those resources too, along with any user data that you have in those resources. CloudFormation Serverless sls remove Even if you don’t intentionally run against production, the thought that someone might one day accidentally do it is worrisome enough. sls remove It’s one thing losing the functions if that happens, and experience a down time in the system, it’s quite another to lose all the production user data along with the functions and potentially find yourself in a situation that you can’t easily recover from… You can — and you should — lock down IAM permissions so that developers can’t accidentally delete these resources in production, which goes a long way to mitigate against these accidents. You should also leverage the new that DynamoDB offers. backup capability For Kinesis streams, you should back up the source events in S3 using Kinesis Firehose. That way, you don’t even have to write any backup code yourself! But even with all the backup options available, I still feel uneasy at the thought of tying these resources that store user data with the creation and deletion of the compute layer (i.e. the Lambda functions) that utilises them. ownership is often not clear cut The second problem with managing shared infrastructure in the is that, what do you do when the ownership of these resources is not clear cut? serverless.yml By virtue of being resources, it’s not always clear which project should be responsible for managing these resources in its . shared serverless.yml Kinesis streams for example, can consume events from Lambda functions, applications running in EC2 or in your own datacenter. And since it uses a polling model, you can process Kinesis events using Lambda functions, or consumer applications running on EC2 and your own data centres. They (Kinesis streams) exist as a way for you to notify others of events that has occurred in your system, and modern distributed systems are heterogeneous by design to allow for greater flexibility and the ability to choose the right tradeoff in different circumstances. Even without the complex case of multi-producer and multi-consumer Kinesis streams, the basic question of is often enough to stop us in our tracks as there doesn’t (at least not to me) seem to be a clear winner here. “should the consumer or the producer own the stream” Manage shared AWS resources separately One of the better ways I have seen — and have adopted myself — is to manage these shared AWS resources in a separate repository using either or templates depending on the expertise available in the team. Terraform CloudFormation This seems to be the approach that many companies have adopted once their serverless architecture matured to the point where shared infrastructure starts to pop up. But, on its own, it’s probably not even a way as it introduces other problems around your workflow. good For example. if those shared resources are managed by a separate infrastructure team, then it can create bottlenecks and frictions between your development and infrastructure teams. That said, by comparison I still think it’s better than managing those shared AWS resources with your for the reasons I mentioned. serverless.yml If you know of other ways to manage shared infrastructure, then by all means let me know in the comments, or you can get in touch with me via twitter. Hi, my name is . I’m an and the author of . I have run production workload at scale in AWS for nearly 10 years and I have been an architect or principal engineer with a variety of industries ranging from banking, e-commerce, sports streaming to mobile gaming. I currently work as an independent consultant focused on AWS and serverless. Yan Cui AWS Serverless Hero Production-Ready Serverless You can contact me via , and . Email Twitter LinkedIn Check out my new course, . Complete Guide to AWS Step Functions In this course, we’ll cover everything you need to know to use AWS Step Functions service effectively. Including basic concepts, HTTP and event triggers, activities, design patterns and best practices. Get your copy . here Come learn about operational for AWS Lambda: CI/CD, testing & debugging functions locally, logging, monitoring, distributed tracing, canary deployments, config management, authentication & authorization, VPC, security, error handling, and more. BEST PRACTICES You can also get off the face price with the code . 40% ytcui Get your copy . here

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AWS Lambda — how best to manage shared code and shared infrastructure

AWS Lambda — how best to manage shared code and shared infrastructure

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