Guide Consumption and Management. New Discovery and Applications

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For instance, if using a BaaS database in this kind of system, all your client apps perhaps web, native iOS, and native Android now need to be able to communicate with your vendor database, and will need to understand how to map from your database schema to application logic. With a full BaaS architecture there is no opportunity to optimize your server design for client performance. Such a pattern is not available for full BaaS.

Both this and the previous drawback exist for full BaaS architectures where all custom logic is in the client and the only backend services are vendor supplied. A mitigation of both of these is to embrace FaaS, or some other kind of lightweight server-side pattern, to move certain logic to the server. I said earlier:. FaaS functions have significant restrictions when it comes to local.. You should not assume that state from one invocation of a function will be available to another invocation of the same function. The reason for this assumption is that with FaaS we typically have no control over when the host containers for our functions start and stop.

I also said earlier that the alternative to local state was to follow factor number 6 of the Twelve-Factor app, which is to embrace this very constraint:. Twelve-factor processes are stateless and share-nothing. Any data that needs to persist must be stored in a stateful backing service, typically a database. Heroku recommends this way of thinking, but you can bend the rules when running on their PaaS since you have control of when Heroku Dynos are started and stopped. The quote above refers to using a database, and in many cases a fast NoSQL database, out-of-process cache e.

But these are all a lot slower than in-memory or on-machine persistence. Another concern in this regard is in-memory caches. Many apps that are reading from a large data set stored externally will keep an in-memory cache of part of that data set. Alternatively you may be reading from an HTTP service that specifies cache headers, in which case your in-memory HTTP client can provide a local cache.

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FaaS does allow some use of local cache, and this may be useful assuming your functions are used frequently enough. For some caches this may be sufficient. However this requires extra work, and may be prohibitively slow depending on your use case. The previously described drawbacks are likely always going to exist with Serverless. The remaining drawbacks, however, come down purely to the current state of the art. In fact this list has shrunk since the first version of this article. When I wrote the first version of this article AWS offered very little in the way of configuration for Lambda functions.

AWS Lambda limits how many concurrent executions of your Lambda functions you can be running at a given time. Say that this limit is one thousand; that means that at any time you are allowed to be executing one thousand function instances. The problem here is that this limit is across an entire AWS account.

Some organizations use the same AWS account for both production and testing. Even if you use different AWS accounts for production and development, one overloaded production lambda e. Amazon provides some protection here, by way of reserved concurrency. However, reserved concurrency is not turned on by default for an account, and it needs careful management.

Earlier in the article I mentioned that AWS Lambda functions are aborted if they run for longer than five minutes. This has been consistent now for a couple of years, and AWS has shown no signs of changing it. I talked about cold starts earlier, and mentioned my article on the subject. Continued improvements are expected in this area.

Integration testing Serverless apps, on the other hand, is hard. So should your integration tests use the external systems too? If yes, then how amenable are those systems to testing scenarios? Can you easily tear up and tear down state? Can your vendor give you a different billing strategy for load testing?

If you want to stub those external systems for integration testing does the vendor provide a local stub simulation? If so, how good is the fidelity of the stub? And remember those cross-account execution limits I mentioned a couple of sections ago when running integration tests in the cloud? You probably want to at least isolate such tests from your production cloud accounts, and likely use even more fine-grained accounts than that.

Part of the reason that considering integration tests is a big deal is that our units of integration with Serverless FaaS i. Relying on cloud-based testing environments rather than running everything locally on my laptop has been quite a shock to me. But times change, and the capabilities we get from the cloud are similar to what engineers at Google and the like have had for over a decade.

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Debugging with FaaS is an interesting area. Microsoft, as I mentioned earlier, provides excellent debugging support for functions run locally, yet triggered by remote events. Amazon offers something similar, but not yet triggered by production events. Debugging functions actually running in a production cloud environment is a different story.

Lambda at least has no support for that yet, though it would be great to see such a capability. This is an area under active improvement. Many usages of Serverless are inherently event driven, and here the consumer of an event typically self registers to some extent. We may even use further layers in front of the API gateway e.


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  • Monitoring is a tricky area for FaaS because of the ephemeral nature of containers. Still, whatever they—and you—can ultimately do depends on the fundamental data the vendor gives you. This may be fine in some cases, but for AWS Lambda, at least, it is very basic. What we really need in this area are open APIs and the ability for third-party services to help out more. While the link refers to API gateways in general e. This logic is typically hard to test, version control, and, sometimes, define. My guidance is to use enhanced API gateway functionality judiciously, and only if it really is saving you effort in the long run, including in how it is deployed, monitored, and tested.

    Much of that has been made more simple with Lambda proxy integration , but you still need to understand some occasionally tricky nuances. Those elements themselves are made easier using open-source projects like the Serverless Framework and Claudia. The danger here is getting lulled into a false sense of security. Maybe you have your app up and running but it unexpectedly appears on Hacker News, and suddenly you have 10 times the amount of traffic to deal with and oops! The fix here is education.

    Teams using Serverless systems need to consider operational activities early, and it is on vendors and the community to provide the teaching to help them understand what this means. Areas like preemptive load testing, and chaos engineering , will also help teams teach themselves.

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    Serverless is still a fairly new world. The most important developments of Serverless are going to be to mitigate the inherent drawbacks and remove, or at least improve, the implementation drawbacks. Such concepts are even more useful in Serverless systems where so many individually deployed components make up a system—atomic release of Lambda functions at a time is simply not possible. Distributed monitoring is probably the area in need of the most significant improvement.

    Microsoft Azure Functions supports this, but Lambda does not. Being able to breakpoint a remotely running function is a very powerful capability. For instance, organizations need to be able to see when certain service instances are no longer used for security purposes, if nothing else , they need better grouping and visibility of cross-service costs especially for autonomous teams that have cost responsibilities , and more. One workaround for high-throughput applications will likely be for vendors to keep function instances alive for longer between events, and let regular in-process caching approaches do their job.

    A better solution could be very low-latency access to out-of-process data, like being able to query a Redis database with very low network overhead. Certain drawbacks to Serverless FaaS right now come down to the way platforms are implemented. Execution duration, startup latency, and cross-function limits are three obvious ones. These will likely either be fixed by new solutions or given workarounds with possible extra costs.

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    For instance, I imagine that startup latency could be mitigated by allowing a customer to request that two instances of a FaaS function are always available at low latency, with the customer paying for this availability. Many vendor-specific inherent drawbacks with Serverless are being mitigated through education. Everyone using such platforms needs to think actively about what it means to have so much of their ecosystems hosted by one or many application vendors.

    Another area for education is technical operations. Many teams now have fewer sysadmins than they used to, and Serverless is going to accelerate this change. These activities may not come naturally to many developers and technical leads, so education and close collaboration with operations folk is of utmost importance. Finally, on the subject of mitigation: vendors are going to have to be even more clear in the expectations we can have of their platforms as we rely on them for more of our hosting capabilities. Our understanding of how and when to use Serverless architectures is still in its infancy.

    Right now teams are throwing all kinds of ideas at Serverless platforms and seeing what sticks. Thank goodness for pioneers! For instance, how big can FaaS functions get before they get unwieldy? Assuming we can atomically deploy a group of FaaS functions, what are good ways of creating such groupings? One particularly interesting area of active discussion in Serverless application architecture is how it interacts with event-thinking.

    What are good ways of introducing BaaS into an existing ecosystem? And, for the reverse, what are the warning signs that a fully or mostly BaaS system needs to start embracing or using more custom server-side code? One of the standard examples for FaaS is media conversion, e.

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    How do we logically aggregate logging for a hybrid architecture of FaaS, BaaS, and traditional servers? How do we most effectively debug FaaS functions? A lot of the answers to these questions—and the emerging patterns—are coming from the cloud vendors themselves, and I expect activity to grow in this area. In the Pet Store example that I gave earlier we saw that the single Pet Store server was broken up into several server-side components and some logic that moved all the way up to the client—but fundamentally this was still an architecture focused either on the client, or on remote services in known locations.

    With Lambda Edge a Lambda function is now globally distributed—a single upload activity by an engineer will mean that function is deployed to over data centers across the globe. This is not a design that we are accustomed to, and comes with a raft of both constraints and capabilities. We in fact now see a spectrum of locality of components, spreading out from the human user. For starters, it encompasses two different but overlapping areas:. BaaS and FaaS are related in their operational attributes e. There is similar linking of the two areas from smaller companies too. Auth0 started with a BaaS product that implemented many facets of user management, and subsequently created the companion FaaS service Webtask.

    The company have taken this idea even further with Extend , which enables other SaaS and BaaS companies to easily add a FaaS capability to existing products so they can create a unified Serverless product. A good example is a typical ecommerce app—dare I say an online pet store? Traditionally, the architecture will look something like the diagram below. With this architecture the client can be relatively unintelligent, with much of the logic in the system—authentication, page navigation, searching, transactions—implemented by the server application. This is a massively simplified view, but even here we see a number of significant changes:.

    If we choose to use AWS Lambda as our FaaS platform we can port the search code from the original Pet Store server to the new Pet Store Search function without a complete rewrite, since Lambda supports Java and Javascript—our original implementation languages. Stepping back a little, this example demonstrates another very important point about Serverless architectures. In the original version, all flow, control, and security was managed by the central server application.

    In the Serverless version there is no central arbiter of these concerns. Instead we see a preference for choreography over orchestration , with each component playing a more architecturally aware role—an idea also common in a microservices approach. There are many benefits to such an approach. Of course, such a design is a trade-off: it requires better distributed monitoring more on this later , and we rely more significantly on the security capabilities of the underlying platform. More fundamentally, there are a greater number of moving pieces to get our heads around than there are with the monolithic application we had originally.

    Whether the benefits of flexibility and cost are worth the added complexity of multiple backend components is very context dependent. Think about an online advertisement system: when a user clicks on an ad you want to very quickly redirect them to the target of that ad. At the same time, you need to collect the fact that the click has happened so that you can charge the advertiser.

    This example is not hypothetical—my former team at Intent Media had exactly this need, which they implemented in a Serverless way. Traditionally, the architecture may look as below. Can you see the difference?

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    The change in architecture is much smaller here compared to our first example—this is why asynchronous message processing is a very popular use case for Serverless technologies. This function runs within the event-driven context the vendor provides. Note that the cloud platform vendor supplies both the message broker and the FaaS environment—the two systems are closely tied to each other.

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    The FaaS environment may also process several messages in parallel by instantiating multiple copies of the function code. Depending on how we wrote the original process this may be a new concept we need to consider. We've mentioned FaaS a lot already, but it's time to dig into what it really means. To do this let's look at the opening description for Amazon's FaaS product: Lambda. AWS Lambda lets you run code without provisioning or managing servers. With Lambda, you can run code for virtually any type of application or backend service 2 - all with zero administration.

    Just upload your code and Lambda takes care of everything required to run 3 and scale 4 your code with high availability. You can set up your code to automatically trigger from other AWS services 5 or call it directly from any web or mobile app 6. FaaS functions have significant architectural restrictions though, especially when it comes to state and execution duration. The rest of the code e. Say that we were having a good day and customers were clicking on ten times as many ads as usual. For the traditional architecture, would our click-processing application be able to handle this?

    For example, did we develop our application to be able to handle multiple messages at a time? If we did, would one running instance of the application be enough to process the load? If we are able to run multiple processes, is autoscaling automatic or do we need to reconfigure that manually? With a FaaS approach all of these questions are already answered—you need to write the function ahead of time to assume horizontal-scaled parallelism, but from that point on the FaaS provider automatically handles all scaling needs. You do have such storage available, but you have no guarantee that such state is persisted across multiple invocations, and, more strongly, you should not assume that state from one invocation of a function will be available to another invocation of the same function.

    For FaaS functions that are naturally stateless—i. FaaS functions are typically limited in how long each invocation is allowed to run. Microsoft Azure and Google Cloud Functions have similar limits. This means that certain classes of long-lived tasks are not suited to FaaS functions without re-architecture—you may need to create several different coordinated FaaS functions, whereas in a traditional environment you may have one long-duration task performing both coordination and execution.

    It takes some time for a FaaS platform to initialize an instance of a function before each event. This startup latency can vary significantly, even for one specific function, depending on a large number of factors, and may range anywhere from a few milliseconds to several seconds. Equally as variable as cold-start duration is cold-start frequency. Are cold starts a concern? It depends on the style and traffic shape of your application. My former team at Intent Media has an asynchronous message-processing Lambda app implemented in Java typically the language with the slowest startup time which processes hundreds of millions of messages per day, and they have no concerns with startup latency for this component.

    Whether or not you think your app may have problems like this, you should test performance with production-like load. For much more detail on cold starts, please see my article on the subject. In a Serverless architecture such handlers are often FaaS functions. When an API gateway receives a request, it finds the routing configuration matching the request, and, in the case of a FaaS-backed route, will call the relevant FaaS function with a representation of the original request.

    The FaaS function will execute its logic and return a result to the API gateway, which in turn will transform this result into an HTTP response that it passes back to the original caller. Beyond purely routing requests, API gateways may also perform authentication, input validation, response code mapping, and more. If your spidey senses are tingling as you consider whether this is actually such a good idea, hold that thought!

    We'll consider this further later. Such tools have improved significantly since then. The comment above about maturity of tooling also applies to Serverless FaaS in general. First of all is Auth0 Webtask which places significant priority on developer UX in its tooling. Second is Microsoft, with their Azure Functions product. Microsoft has always put Visual Studio, with its tight feedback loops, at the forefront of its developer products, and Azure Functions is no exception.

    The ability it offers to debug functions locally, given an input from a cloud-triggered event, is quite special. An area that still needs significant improvement is monitoring. I discuss that later on. The majority of Serverless applications make use of such services, but there are open-source projects in this world, too. It also provides an amount of cross-vendor tooling abstraction, which some users find valuable.

    Examples of similar tools include Claudia and Zappa. Another example is Apex , which is particularly interesting since it allows you to develop Lambda functions in languages other than those directly supported by Amazon. One of the main benefits of proprietary FaaS is not having to be concerned about the underlying compute infrastructure machines, VMs, even containers. But what if you want to be concerned about such things?

    Many other self-hosted FaaS implementations make use of an underlying container platform, frequently Kubernetes, which makes a lot of sense for many reasons. So far in this article I've described Serverless as being the union of two ideas: Backend as a Service and Functions as a Service. I've also dug into the capabilities of the latter. For more precision about what I see as the key attributes of a Serverless service and why I consider even older services like S3 to be Serverless , I refer you to another article of mine: Defining Serverless.

    Before we start looking at the very important area of benefits and drawbacks, I'd like to spend one more quick moment on definition. For a brief answer I refer to Adrian Cockcroft. In other words, most PaaS applications are not geared towards bringing entire applications up and down in response to an event, whereas FaaS platforms do exactly this. The key operational difference between FaaS and PaaS is scaling. Generally with a PaaS you still need to think about how to scale—for example, with Heroku, how many Dynos do you want to run? With a FaaS application this is completely transparent.

    Given this benefit, why would you still use a PaaS? There are several reasons, but tooling is probably the biggest. One of the reasons to use Serverless FaaS is to avoid having to manage application processes at the operating-system level.

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    Another popular abstraction of processes are containers, with Docker being the most visible example of such a technology. Container hosting systems such as Mesos and Kubernetes , which abstract individual applications from OS-level deployment, are increasingly popular. Given the momentum around containers, is it still worth considering Serverless FaaS? Principally the argument I made for PaaS still holds with containers - for Serverless FaaS scaling is automatically managed, transparent, and fine grained , and this is tied in with the automatic resource provisioning and allocation I mentioned earlier.

    Container platforms have traditionally still needed you to manage the size and shape of your clusters. As we see the gap of management and scaling between Serverless FaaS and hosted containers narrow, the choice between them may just come down to style and type of application. For example, it may be that FaaS is seen as a better choice for an event-driven style with few event types per application component, and containers are seen as a better choice for synchronous-request—driven components with many entry points.

    I expect in a fairly short period of time that many applications and teams will use both architectural approaches, and it will be fascinating to see patterns of such use emerge. It also means—at least—monitoring, deployment, security, networking, support, and often some amount of production debugging and system scaling. In some ways Ops is harder in a Serverless world because a lot of this is so new. Charity Majors gave a great talk on this subject at the first Serverlessconf.

    You can also read her two write-ups on it: WTF is operations? There are many lessons that come from using stored procedures that are worth reviewing in the context of FaaS and seeing whether they apply. Consider that stored procedures:. So far I've mostly tried to stick to just defining and explaining what Serverless architectures have come to mean. Now I'm going to discuss some of the benefits and drawbacks to such a way of designing and deploying applications.

    You should definitely not take any decision to use Serverless without significant consideration and weighing of pros and cons. Serverless is, at its most simple, an outsourcing solution.