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SRE Doesn’t Scale

We encounter a lot of organizations talking about or attempting to implement SRE as part of our consulting at Real Kinetic. We’ve even discussed and debated ourselves, ad nauseam, how we can apply it at our own product company, Witful. There’s a brief, unassuming section in the SRE book tucked away towards the tail end of chapter 32, “The Evolving SRE Engagement Model.” Between the SLIs and SLOs, the error budgets, alerting, and strategies for handling change management, it’s probably one of the most overlooked parts of the book. It’s also, in my opinion, one of the most important.

Chapter 32 starts by discussing the “classic” SRE model and then, towards the end, how Google has been evolving beyond this model. “External Factors Affecting SRE”, under the “Evolving Services Development: Frameworks and SRE Platform” heading, is the section I’m referring to specifically. This part of the book details challenges and approaches for scaling the SRE model described in the preceding chapters. This section describes Google’s own shift towards the industry trend of microservices, the difficulties that have resulted, and what it means for SRE. Google implements a robust site reliability program which employs a small army of SREs who support some of the company’s most critical systems and engage with engineering teams to improve the reliability of their products and services. The model described in the book has proven to be highly effective for Google but is also quite resource-intensive. Microservices only serve to multiply this problem. The organizations we see attempting to adopt microservices along with SRE, particularly those who are doing it as a part of a move to cloud, frequently underestimate just how much it’s about to ruin their day in terms of thinking about software development and operations.

It is not going from a monolith to a handful of microservices. It ends up being hundreds of services or more, even for the smaller companies. This happens every single time. And that move to microservices—in combination with cloud—unleashes a whole new level of autonomy and empowerment for developers who, often coming from a more restrictive ops-controlled environment on prem, introduce all sorts of new programming languages, compute platforms, databases, and other technologies. The move to microservices and cloud is nothing short of a Cambrian Explosion for just about every organization that attempts it. I have never seen this not play out to some degree, and it tends to be highly disruptive. Some groups handle it well—others do not. Usually, however, this brings an organization’s delivery to a grinding halt as they try to get a handle on the situation. In some cases, I’ve seen it take a year or more for a company to actually start delivering products in the cloud after declaring they are “all in” on it. And that’s just the process of starting to deliver, not actually delivering them.

How does this relate to SRE? In the book, Google says a result of moving towards microservices is that both the number of requests for SRE support and the cardinality of services to support have increased dramatically. Because each service has a base fixed operational cost, even simple services demand more staffing. Additionally, microservices almost always imply an expectation of lower lead time for deployment. This is invariably one of the reasons we see organizations adopting them in the first place. This reduced lead time was not possible with the Production Readiness Review model they describe earlier in chapter 32 because it had a lead time of months. For many of the organizations we work with, a lead time of months to deliver new products and capabilities to their customers is simply not viable. It would be like rewinding the clock to when they were still operating on prem and completely defeat the purpose of microservices and cloud.

But here’s the key excerpt from the book: “Hiring experienced, qualified SREs is difficult and costly. Despite enormous effort from the recruiting organization, there are never enough SREs to support all the services that need their expertise.” The authors conclude, “the SRE organization is responsible for serving the needs of the large and growing number of development teams that do not already enjoy direct SRE support. This mandate calls for extending the SRE support model far beyond the original concept and engagement model.”

Even Google, who has infinite money and an endless recruiting pipeline, says the SRE model—as it is often described by the people we encounter referencing the book—does not scale with microservices. Instead, they go on to describe a more tractable, framework-oriented model to address this through things like codified best practices, reusable solutions, standardization of tools and patterns, and, more generally, what I describe as the “productization” of infrastructure and operations.

Google enforces standards and opinions around things like programming languages, instrumentation and metrics, logging, and control systems surrounding traffic and load management. The alternative to this is the Cambrian Explosion I described earlier. The authors enumerate the benefits of this approach such as significantly lower operational overhead, universal support by design, faster and lower overhead SRE engagements, and a new engagement model based on shared responsibility rather than either full SRE support or no SRE support. As the authors put it, “This model represents a significant departure from the way service management was originally conceived in two major ways: it entails a new relationship model for the interaction between SRE and development teams, and a new staffing model for SRE-supported service management.”

For some reason, this little detail gets lost and, consequently, we see groups attempting to throw people at the problem, such as embedding an SRE on each team. In practice, this usually means two things: 1) hiring a whole bunch of SREs—which even Google admits to being difficult and costly—and 2) this person typically just becomes the “whipping boy” for the team. More often than not, this individual is some poor ops person who gets labeled “SRE.”

With microservices, which again almost always hit you with a near-exponential growth rate once you adopt them, you simply cannot expect to have a handful of individuals who are tasked with understanding the entirety of a microservice-based platform and be responsible for it. SRE does not mean developers get to just go back to thinking about code and features. Microservices necessitate developers having skin in the game, and even Google has talked about the challenges of scaling a traditional SRE model and why a different tack is needed.

“The constant growth in the number of services at Google means that most of these services can neither warrant SRE engagement nor be maintained by SREs. Regardless, services that don’t receive full SRE support can be built to use production features that are developed and maintained by SREs. This practice effectively breaks the SRE staffing barrier. Enabling SRE-supported production standards and tools for all teams improves the overall service quality across Google.”

My advice is to stop thinking about SRE as an implementation specifically and instead think about the problems it’s solving a bit more abstractly. It’s unlikely your organization has Google-level resources, so you need to consider the constraints. You need to think about the roles and responsibilities of developers as well as your ops folks. They will change significantly with microservices and cloud out of necessity. You’ll need to think about how to scale DevOps within your organization and, as part of that, what “DevOps” actually means to your organization. In fact, many groups are probably better off simply removing “SRE” and “DevOps” from their vocabulary altogether because they often end up being distracting buzzwords. For most mid-to-large-sized companies, some sort of framework- and platform- oriented model is usually needed, similar to what Google describes.

I’ve seen it over and over. This hits companies like a ton of bricks. It requires looking at some hard org problems. A lot of self-reflection that many companies find uncomfortable or just difficult to do. But it has to be done. It’s also an important piece of context when applying the SRE book. Don’t skip over chapter 32. It might just be the most important part of the book.

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Structuring a Cloud Infrastructure Organization

Real Kinetic often works with companies just beginning their cloud journey. Many come from a conventional on-prem IT organization, which typically looks like separate development and IT operations groups. One of the main challenges we help these clients with is how to structure their engineering organizations effectively as they make this transition. While we approach this problem holistically, it can generally be looked at as two components: product development and infrastructure. One might wonder if this is still the case with the shift to DevOps and cloud, but as we’ll see, these two groups still play important and distinct roles.

We help clients understand and embrace the notion of a product mindset as it relates to software development. This is a fundamental shift from how many of these companies have traditionally developed software, in which development was viewed as an IT partner beholden to the business. This transformation is something I’ve discussed at length and will not be the subject of this conversation. Rather, I want to spend some time talking about the other side of the coin: operations.

Operations in the Cloud

While I’ve talked about operations in the context of cloud before, it’s only been in broad strokes and not from a concrete, organizational perspective. Those discussions don’t really get to the heart of the matter and the question that so many IT leaders ask: what does an operations organization look like in the cloud?

This, of course, is a highly subjective question to which there is no “right” answer. This is doubly so considering that every company and culture is different. I can only humbly offer my opinion and answer with what I’ve seen work in the context of particular companies with particular cultures. Bear this in mind as you think about your own company. More often than not, the cultural transformation is more arduous than the technology transformation.

I should also caveat that—outside of being a strategic instrument—Real Kinetic is not in the business of simply helping companies lift-and-shift to the cloud. When we do, it’s always with the intention of modernizing and adapting to more cloud-native architectures. Consequently, our clients are not usually looking to merely replicate their current org structure in the cloud. Instead, they’re looking to tailor it appropriately.

Defining Lines of Responsibility

What should developers need to understand and be responsible for? There tend to be two schools of thought at two different extremes when it comes to this depending on peoples’ backgrounds and experiences. Oftentimes, developers will want more control over infrastructure and operations, having come from the constraints of a more siloed organization. On the flip side, operations folks and managers will likely be more in favor of having a separate group retain control over production environments and infrastructure for various reasons—efficiency, stability, and security to name a few. Not to mention, there are a lot of operational concerns that many developers are likely not even aware of—the sort of unsung, unglamorous bits of running software.

Ironically, both models can be used as an argument for “DevOps.” There are also cases to be made for either. The developer argument is better delivery velocity and innovation at a team level. The operations argument is better stability, risk management, and cost control. There’s also likely more potential for better consistency and throughput at an organization level.

The answer, unsurprisingly, is a combination of both.

There is an inherent tension between empowering developers and running an efficient organization. We want to give developers the flexibility and autonomy they need to develop good solutions and innovate. At the same time, we also need to realize the operational efficiencies that common solutions and standardization provide in order to benefit from economies of scale. Should every developer be a generalist or should there be specialists?

Real Kinetic helps clients adopt a model we refer to as “Developer Enablement.” The idea of Developer Enablement is shifting the focus of ops teams from being “masters” of production to “enablers” of production by applying a product lens to operations. In practical terms, this means less running production workloads on behalf of developers and more providing tools and products that allow developers to run workloads themselves. It also means thinking of operations less as a task-driven service model and more as a strategic enabler. However, Developer Enablement is not about giving full autonomy to developers to do as they please, it’s about providing the abstractions they need to be successful on the platform while realizing the operational efficiencies possible in a larger organization. This means providing common tooling, products, and patterns. These are developed in partnership with product teams so that they meet the needs of the organization. Some companies might refer to this as a “platform” team, though I think this has a slightly different meaning. So how does this map to an actual organization?

Mapping Out an Engineering Organization

First, let’s mentally model our engineering organization as two groups: Product Development and Infrastructure and Reliability. The first is charged with developing products for end users and customers. This is the stuff that makes the business money. The second is responsible for supporting the first. This is where the notion of “developer enablement” comes into play. And while this group isn’t necessarily doing work that is directly strategic to the business, it is work that is critical to providing efficiencies and keeping the lights on just the same. This would traditionally be referred to as Operations.

As mentioned above, the focus of this discussion is the green box. And as you might infer from the name, this group is itself composed of two subgroups. Infrastructure is about enabling product teams, and Reliability is about providing a first line of defense when it comes to triaging production incidents. This latter subgroup is, in and of itself, its own post and worthy of a separate discussion, so we’ll set that aside for another day. We are really focused on what a cloud infrastructure organization might look like. Let’s drill down on that piece of the green box.

An Infrastructure Organization Model

When thinking about organization structure, I find that it helps to consider layers of operational concern while mapping the ownership of those concerns. The below diagram is an example of this. Note that these do not necessarily map to specific team boundaries. Some areas may have overlap, and responsibilities may also shift over time. This is mostly an exercise to identify key organizational needs and concerns.

We like to model the infrastructure organization as three teams: Developer Productivity, Infrastructure Engineering, and Cloud Engineering. Each team has its own charter and mission, but they are all in support of the overarching objective of enabling product development efficiently and at scale. In some cases, these teams consist of just a handful of engineers, and in other cases, they consist of dozens or hundreds of engineers depending on the size of the organization and its needs. These team sizes also change as the priorities and needs of the company evolve over time.

Developer Productivity

Developer Productivity is tasked with getting ideas from an engineer’s brain to a deployable artifact as efficiently as possible. This involves building or providing solutions for things like CI/CD, artifact repositories, documentation portals, developer onboarding, and general developer tooling. This team is primarily an engineering spend multiplier. Often a small Developer Productivity team can create a great deal of leverage by providing these different tools and products to the organization. Their core mandate is reducing friction in the delivery process.

Infrastructure Engineering

The Infrastructure Engineering team is responsible for making the process of getting a deployable artifact to production and managing it as painless as possible for product teams. Often this looks like providing an “opinionated platform” on top of the cloud provider. Completely opening up a platform such as AWS for developers to freely use can be problematic for larger organizations because of cost and time inefficiencies. It also makes security and compliance teams’ jobs much more difficult. Therefore, this group must walk the fine line between providing developers with enough flexibility to be productive and move fast while ensuring aggregate efficiencies to maintain organization-wide throughput as well as manage costs and risk. This can look like providing a Kubernetes cluster as a service with opinions around components like load balancing, logging, monitoring, deployments, and intra-service communication patterns. Infrastructure Engineering should also provide tooling for teams to manage production services in a way that meets the organization’s regulatory requirements.

The question of ownership is important. In some organizations, the Infrastructure Engineering team may own and operate infrastructure services, such as common compute clusters, databases, or message queues. In others, they might simply provide opinionated guard rails around these things. Most commonly, it is a combination of both. Without this, it’s easy to end up with every team running their own unique messaging system, database, cache, or other piece of infrastructure. You’ll have lots of architecture astronauts on your hands, and they will need to be able to answer questions around things like high availability and disaster recovery. This leads to significant inefficiencies and operational issues. Even if there isn’t shared infrastructure, it’s valuable to have an opinionated set of technologies to consolidate institutional knowledge, tooling, patterns, and practices. This doesn’t have to act as a hard-and-fast rule, but it means teams should be able to make a good case for operating outside of the guard rails provided.

This model is different from traditional operations in that it takes a product-mindset approach to providing solutions to internal customers. This means it’s important that the group is able to understand and empathize with the product teams they serve in order to identify areas for improvement. It also means productizing and automating traditional operations tasks while encouraging good patterns and practices. This is a radical departure from the way in which most operations teams normally operate. It’s closer to how a product development team should work.

This group should also own standards around things like logging and instrumentation. These standards allow the team to develop tools and services that deal with this data across the entire organization. I’ve talked about this notion with the Observability Pipeline.

Cloud Engineering

Cloud Engineering might be closest to what most would consider a conventional operations team. In fact, we used to refer to this group as Cloud Operations but have since moved away from that vernacular due to the connotation the word “operations” carries. This group is responsible for handling common low-level concerns, underlying subsystems management, and realizing efficiencies at an aggregate level. Let’s break down what that means in practice by looking at some examples. We’ll continue using AWS to demonstrate, but the same applies across any cloud provider.

One of the low-level concerns this group is responsible for is AMI and base container image maintenance. This might be the AMIs used for Kubernetes nodes and the base images used by application pods running in the cluster. These are critical components as they directly relate to the organization’s security and compliance posture. They are also pieces most developers in a large organization are not well-equipped to—or interested in—dealing with. Patch management is a fundamental concern that often takes a back seat to feature development. Other examples of this include network configuration, certificate management, logging agents, intrusion detection, and SIEM. These are all important aspects of keeping the lights on and the company’s name out of the news headlines. Having a group that specializes in these shared operational concerns is vital.

In terms of realizing efficiencies, this mostly consists of managing AWS accounts, organization policies (another important security facet), and billing. This group owns cloud spend across the organization and, as a result, is able to monitor cumulative usage and identify areas for optimization. This might look like implementing resource-tagging policies, managing Reserved Instances, or negotiating with AWS on committed spend agreements. Spend is one of the reasons large companies standardize on a single cloud platform, so it’s essential to have good visibility and ownership over this. Note that this team is not responsible for the spend itself, rather they are responsible for visibility into the spend and cost allocations to hold teams accountable.

The unfortunate reality is that if the Cloud Engineering team does their job well, no one really thinks about them. That’s just the nature of this kind of work, but it has a massive impact on the company’s bottom line.

Summary

Depending on the company culture, words like “standards” and “opinionated” might be considered taboo. These can be especially unsettling for developers who have worked in rigid or siloed environments. However, it doesn’t have to be all or nothing. These opinions are more meant to serve as a beaten path which makes it easier and faster for teams to deliver products and focus on business value. In fact, opinionation will accelerate cloud adoption for many organizations, enable creativity on the value rather than solution architecture, and improve efficiency and consistency at a number of levels like skills, knowledge, operations, and security. The key is in understanding how to balance this with flexibility so as to not overly constrain developers.

We like taking a product approach to operations because it moves away from the “ticket-driven” and gatekeeper model that plagues so many organizations. By thinking like a product team, infrastructure and operations groups are better able to serve developers. They are also better able to scale—something that is consistently difficult for more interrupt-driven ops teams who so often find themselves becoming the bottleneck.

Notice that I’ve entirely sidestepped terms like “DevOps” and “SRE” in this discussion. That is intentional as these concepts frequently serve as a distraction for companies who are just beginning their journey to the cloud. There are ideas encapsulated by these philosophies which provide important direction and practices, but it’s imperative to not get too caught up in the dogma. Otherwise, it’s easy to spin your wheels and chase things that, at least early on, are not particularly meaningful. It’s more impactful to focus on fundamentals and finding some success early on versus trying to approach things as town planners.

Moreover, for many companies, the organization model I walked through above was the result of evolving and adapting as needs changed and less of a wholesale reorg. In the spirit of product mindset, we encourage starting small and iterating as opposed to boiling the ocean. The model above can hopefully act as a framework to help you identify needs and areas of ownership within your own organization. Keep in mind that these areas of responsibility might shift over time as capabilities are implemented and added.

Lastly, do not mistake this framework as something that might preclude exploration, learning, and innovation on the part of development teams. Again, opinionation and standards are not binding but rather act as a path of least resistance to facilitate efficiency. It’s important teams have a safe playground for exploratory work. Ideally, new ideas and discoveries that are shown to add value can be standardized over time and become part of that beaten path. This way we can make them more repeatable and scale their benefits rather than keeping them as one-off solutions.

How has your organization approached cloud development? What’s worked? What hasn’t? I’d love to hear from you.

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We suck at meetings

I’ve worked as a software engineer, manager, consultant, and business owner. All of these jobs have involved meetings. What those meetings look like has varied greatly.

As an engineer, meetings typically entailed technical conversations with peers, one-on-ones with managers, and planning meetings or demos with stakeholders.

As a manager, these looked more like quarterly goal-setting with engineering leadership, one-on-ones with direct reports, and decision-making discussions with the team.

As a consultant, my day often consists of talking to clients to provide input and guidance, communicating with partners to develop leads and strategize on accounts, and meeting with sales prospects to land new deals.

As a business owner, I am in conversations with attorneys and accountants regarding legal and financial matters, with advisors and brokers for things like employee benefits and health insurance, and with my co-owner Robert to discuss items relating to business operations.

What I’ve come to realize is this: we suck at meetings. We’re really bad at them. After starting my first job out of college, I quickly discovered that everyone’s just winging it when it comes to meetings. We’re winging it in a way the likes of which Dilbert himself would envy. We’re so bad at it that it’s become a meme in the corporate world. Whether it’s joking about your lack of productivity due to the number of meetings you have or that one meeting that could have been an email, we’ve basically come to terms with the fact that most meetings are just not very good.

And who’s to blame? There’s no science to meetings. It’s not something they teach you in school. Everyone just shows up and sort of finds a system that works—or doesn’t work—for them. What’s most shocking to me, however, is that meetings are one of the most expensive things a business can do—like billions-of-dollars expensive. If you’re going to pay a bunch of people a lot of money to talk to other people who you’re similarly paying a lot of money, you probably want that talking to be worthwhile, right? And yet here we are, jumping from one meeting to the next, unable to even process what was said in the last one. It’s become an inside joke that every company is in on.

But meetings are also important. They’re where collaboration happens, where ideas are born, where decisions are made. Is being “good at meetings” a legitimate hiring criteria? Should it be?

From all of the meetings I’ve had across these different jobs, I’ve learned that the biggest difference throughout is that of the role played in the meeting. In some cases, it’s The Spectator—there mostly to listen and maybe ask questions. In other cases, it’s playing the role of The Advisor—actively participating in the meeting but mostly in the form of offering advice and guidance. Sometimes it’s The Facilitator, who helps move the agenda along, captures notes, and keeps track of action items or decisions. It might be the Decision Maker, who’s there to decide which way to go and be the tie breaker.

Whatever the role, I’ve consistently struggled with how to insert the most value into meetings and extract the most value out of them. This is doubly so when your job revolves around people, which I didn’t recognize until I became a manager and, later, consultant. In these roles, your calendar is usually stacked with meetings, often with different groups of people across many different contexts. A software engineer’s work happens outside of meetings, but for a manager or consultant, it revolves around what gets done during and after meetings. This is true of a lot of other roles as well.

I’ve always had a vague sense for how to do meetings effectively—have a clear purpose or desired outcome, gather necessary context and background information, include an agenda, invite only the people you need, be present and engaged in the discussion, document the action items and decisions, follow up. The problem is I’ve never had a system for doing it that wasn’t just ad hoc and scattered. Also, most of these things happen outside of the conference room or Zoom call, and who has the time to do all of that when your schedule looks like a Dilbert calendar? All of it culminates in a feeling of severe meeting fatigue.

That’s when it occurred to us: what if meetings could be good? Shortly after starting Real Kinetic, we began to explore this question, but the idea had been rattling around our heads long before that. And so we started to develop a solution, first by building a prototype on nights and weekends, then later by investing in it as a full-fledged product. We call it Witful—a note-taking app that connects to your calendar. It’s deceptively simple, but its mission is not: make meetings suck less.

Most calendar and note-taking apps focus on time. After all, what’s the first thing we do when we create a meeting? We schedule it. When it comes to meetings, time is important for logistical purposes—it’s how we know when we need to be somewhere. But the real value of meetings is not time, it’s the people and discussion, decisions, and action items that result. This is what Witful emphasizes by creating a network of all these relationships. It’s less an extension of your notebook and calendar and—forgive the cliche—more like an extension of your brain. It’s a more natural way to organize the information around your work.

We’re still early on this journey, but the product is evolving quickly. We’ve also been clear from the start: Witful isn’t for everyone. If your day is not run by your calendar, it might not be for you. If your role doesn’t center around managing people or maintaining relationships, it might not be for you. Our focus right now is to make you better at meetings. We want to give you the tools and resources you need to conquer your calendar and look good doing it. We use Witful every day to make our consulting work more manageable at Real Kinetic. And while we’re focused on empowering the individual today, our eyes are set towards making teams better at meetings too.

We don’t want to change the way people work, we want to help them do their best work. We want to make meetings suck less. Come join us.

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Getting big wins with small teams on tight deadlines

Part of what we do at Real Kinetic is give companies confidence to ship software in the cloud. Many of our clients are large organizations that have been around for a long time but who don’t always have much experience when it comes to cloud. Others are startups and mid-sized companies who may have some experience, but might just want another set of eyes or are looking to mature some of their practices. Whatever the case, one of the things we frequently talk to our clients about is the value of both serverless and managed services. We have found that these are critical to getting big wins with small teams on tight deadlines in the cloud. Serverless in particular has been key to helping clients get some big wins in ways others didn’t think possible.

We often get pulled into a company to help them develop and launch new products in the cloud. These are typically high-profile projects with tight deadlines. These deadlines are almost always in terms of months, usually less than six. As a result, many of the executives and managers we talk to in these situations are skeptical of their team’s ability to execute on these types of timeframes. Whether it’s lack of cloud experience, operations and security concerns, compliance issues, staffing constraints, or some combination thereof, there’s always a reason as to why it can’t be done.

And then, some months later, it gets done.

Mental Model of the Cloud

The skepticism is valid. Often people’s mental model of the cloud is something like this:

A subset of typical cloud infrastructure concerns

More often than not, this is what cloud infrastructure looks like. In addition to what’s shown, there are other concerns. These include things like managing backups and disaster recovery, multi-zone or regional deployments, VM images, and reserved instances. It can be deceiving because simply getting an app running in this environment isn’t terribly difficult, and most engineers will tell you that—these are the “day-one” costs. But engineers don’t tend to be the best at giving estimates while still undervaluing their own time. The minds of most seasoned managers, however, will usually go to the “day-two” costs—what are the ongoing maintenance and operations costs, the security and compliance considerations, and the staffing requirements? This is why we consistently see so much skepticism. If this is also your initial foray into the cloud, that’s a lot of uncertainty! A manager’s job, after all, is to reduce uncertainty.

We’ve been there. We’ve also had to manage those day-two costs. I’ve personally gone through the phases of building a complex piece of software in the cloud, having to maintain one, having to manage a team responsible for one, and having to help a team go through the same process as an outside consultant. Getting that perspective has helped me develop an appreciation for what it really means to ship software. It’s why we like to take a different tack at Real Kinetic when it comes to cloud.

We are big on picking a cloud platform and going all-in on it. Whether it’s AWS, GCP, or Azure—pick your platform, embrace its capabilities, and move on. That doesn’t mean there isn’t room to use multiple clouds. Some platforms are better than others in different areas, such as data analytics or machine learning, so it’s wise to leverage the strengths of each platform where it makes sense. This is especially true for larger organizations who will inevitably span multiple clouds. What we mean by going “all-in” on a platform, particularly as it relates to application development, is sidestepping the trap that so many organizations fall into—hedging their bets. For a variety of reasons, many companies will take a half measure when adopting a cloud platform by avoiding things like managed services and serverless. Vendor lock-in is usually at the top of their list of concerns. Instead, they end up with something akin to the diagram above, and in doing so, lose out on the differentiated benefits of the platform. They also incur significantly more day-two costs.

The Value and Cost of Serverless

We spend a lot of time talking to our clients about this trade-off. With managers, it usually resonates when we ask if they want their people focusing on shipping business value or doing commodity work. With engineers, architects, or operations folks, it can be more contentious. On more than a few occasions, we’ve talked clients out of using Kubernetes for things that were well-suited to serverless platforms. Serverless is not the right fit for everything, but the reality is many of the workloads we encounter are primarily CRUD-based microservices. These can be a good fit for platforms like AWS Lambda, Google App Engine, or Google Cloud Run. The organizations we’ve seen that have adopted these services for the correct use cases have found reduced operations investment, increased focus on shipping things that matter to the business, accelerated delivery of new products, and better cost efficiency in terms of infrastructure utilization.

If vendor lock-in is your concern, it’s important to understand both the constraints and the trade-offs. Not all serverless platforms are created equal. Some are highly opinionated, others are not. In the early days, Google App Engine was highly opinionated, requiring you to use its own APIs to build your application. This meant moving an application built on App Engine was no small feat. Today, that is no longer the case; the new App Engine runtimes allow you to run just about any application. Cloud Run, a serverless container platform, allows you to deploy a container that can run anywhere. The costs are even less. On the other hand, using a serverless database like Cloud Firestore or DynamoDB requires using a proprietary API, but APIs can be abstracted.

In order to decide if the trade-off makes sense, you need to determine three things:

  1. What is the honest likelihood you’ll need to move in the future?
  2. What are the switching costs—the amount of time and effort needed to move?
  3. What is the value you get using the solution?

These are not always easy things to determine, but the general rule is this: if the value you’re getting offsets the switching costs times the probability of switching—and it often does—then it’s not worth trying to hedge your bet. There can be a lot of hidden considerations, namely operations and development overhead and opportunity costs. It can be easy to forget about these when making a decision. In practice, vendor lock-in tends to be less about code portability and more about capability lock-in—think things like user management, Identity and Access Management, data management, cloud-specific features and services, and so forth. These are what make switching hard, not code.

Another concern we commonly hear with serverless is cost. In our experience, however, this is rarely an issue for appropriate use cases. While serverless can be more expensive in terms of cloud spend for some situations, this cost is normally offset by the reduced engineering and ongoing operations costs. Using serverless and managed services for the right things can be quite cost-effective. This may not always hold true, such as for large organizations who can negotiate with providers for committed cloud spend, but for many cases it makes sense.

Serverless isn’t just about compute. While people typically associate serverless with things like Lambda or Cloud Functions, it actually extends far beyond this. For example, in addition to its serverless compute offerings (Cloud Run, Cloud Functions, and App Engine), GCP has serverless storage (Cloud Storage, Firestore, and Datastore), serverless integration components (Cloud Tasks, Pub/Sub, and Scheduler), and serverless data and machine learning services (BigQuery, AutoML, and Dataflow). While each of these services individually offers a lot of value, it’s not until we start to compose them together in different ways where we really see the value of serverless appear.

Serverless vs. Managed Services

Some might consider the services I mentioned above “managed services”, so let me clarify that. We generally talk about “serverless” being the idea that the cloud provider fully manages and maintains the server infrastructure. This means the notion of “managed services” and “serverless” are closely related, but they are also distinct.

A serverless product is also managed, but not all managed services are serverless. That is to say, serverless is a subset of managed services.

Serverless means you stop thinking about the concept of servers in your architecture. This broadly encompasses words like “servers”, “instances”, “nodes”, and “clusters.” Continuing with our GCP example, these words would be associated with products like GKE, Dataproc, Bigtable, Cloud SQL, and Spanner. These services are decidedly not serverless because they entail some degree of managing and configuring servers or clusters, even though they are managed services.

Instead, you start thinking in terms of APIs and services. This would be things like Cloud Functions, Dataflow, BigQuery, Cloud Run, and Firestore. These have no servers or clusters. They are simply APIs that you interact with to build your applications. They are more specialized managed services.

Why does this distinction matter? It matters because of the ramifications it has for where we invest our time. Managing servers and clusters is going to involve a lot more operations effort, even if the base infrastructure is managed by the cloud provider. Much of this work can be considered “commodity.” It is not work that differentiates the business. This is the trade-off of getting more control—we take on more responsibility. In rough terms, the managed services that live outside of the serverless circle are going to be more in the direction of “DevOps”, meaning they will involve more operations overhead. The managed services inside the serverless circle are going to be more in the direction of “NoOps”. There is still work involved in using them, but the line of responsibility has moved upwards with the cloud provider responsible for more. We get less control over the infrastructure, but that means we can focus more on the business outcomes we develop on top of that infrastructure.

In fairness, it’s not always a black-and-white determination. Things can get a little blurry since serverless might still provide some degree of control over runtime parameters like memory or CPU, but this tends to be limited in comparison to managing a full server. There might also be some notion of “instances”, as in the case of App Engine, but that notion is much more abstract. Finally, some services appear to straddle the line between managed service and serverless. App Engine Flex, for instance, allows you to SSH into its VMs, but you have no real control over them. It’s a heavily sandboxed environment.

Why Serverless?

Serverless enables focusing on business outcomes. By leveraging serverless offerings across cloud platforms, we’ve seen product launches go from years to months (and often single-digit months). We’ve seen release cycles go from weeks to hours. We’ve seen development team sizes go from double digits to a few people. We’ve seen ops teams go from dozens of people to just one or two. It’s allowed these people to focus on more differentiated work. It’s given small teams of people a significant amount of leverage.

It’s no secret. Serverless is how we’ve helped many of our clients at Real Kinetic get big wins with small teams on tight deadlines. It’s not always the right fit and there are always trade-offs to consider. But if you’re not at least considering serverless—and more broadly, managed services—then you’re not getting the value you should be getting out of your cloud platform. Keep in mind that it doesn’t have to be all or nothing. Find the places where you can leverage serverless in combination with managed services or more traditional infrastructure. You too will be surprising and impressing your managers and leadership.

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Continuous Deployment for AWS Glue

AWS Glue is a managed service for building ETL (Extract-Transform-Load) jobs. It’s a useful tool for implementing analytics pipelines in AWS without having to manage server infrastructure. Jobs are implemented using Apache Spark and, with the help of Development Endpoints, can be built using Jupyter notebooks. This makes it reasonably easy to write ETL processes in an interactive, iterative fashion. Once finished, the Jupyter notebook is converted into a Python script, uploaded to S3, and then run as a Glue job.

There are a number of steps involved in doing this, so it can be worthwhile to automate the process into a CI/CD pipeline. In this post, I’ll show you how you can build an automated pipeline using GitHub Actions to do continuous deployment of Glue jobs built on PySpark and Jupyter notebooks. The full code for this demo is available on GitHub.

The Abstract Workflow

First, I’m going to assume you already have a notebook for which you’d like to set up continuous deployment. If you don’t, you can take a look at my example, but keep in mind you’ll need to have the appropriate data sources and connections set up in Glue for it to work. This post won’t be focusing on the ETL script itself but rather the build and deployment pipeline for it.

I recommend treating your Jupyter notebooks as the “source code” for your ETL jobs and treating the resulting Python script as the “build artifact.” Though this can present challenges for diffing, I find providing the notebook from which the code was derived makes the development process easier, particularly when collaborating with other developers. Additionally, GitHub has good support for rendering Jupyter notebooks, and there is tooling available for diffing notebooks, such as nbdime.

With that in mind, the general flow of our deployment pipeline looks something like this:

  1. Upon new commits to master, generate a Python script from the Jupyter notebook.
  2. Copy the generated Python script to an S3 bucket.
  3. Update a Glue job to use the new script.

You might choose to run some unit or integration tests for your script as well, but I’ve omitted this for brevity.

The Implementation

As I mentioned earlier, I’m going to use GitHub Actions to implement my CI/CD pipeline, but you could just as well use another tool or service to implement it. Actions makes it easy to automate workflows and it’s built right into GitHub. If you’re already familiar with it, some of this will be review.

In our notebook repository, we’ll create a .github/workflows directory. This is where GitHub Actions looks for workflows to run. Inside that directory, we’ll create a main.yml file for defining our CI/CD workflow.

First, we need to give our workflow a name. Our pipeline will simply consist of two jobs, one for producing the Python script and another for deploying it, so I’ll name the workflow “build-and-deploy.”

name: build-and-deploy

Next, we’ll configure when the workflow runs. This could be on push to a branch, when a pull request is created, on release, or a number of other events. In our case, we’ll just run it on pushes to the master branch.

on:
  push:
    branches: [ master ]

Now we’re ready to define our “build” job. We will use a tool called nbconvert to convert our .ipynb notebook file into an executable Python script. This means our build job will have some setup. Specifically, we’ll need to install Python and then install nbconvert using Python’s pip. Before we define our job, we need to add the “jobs” section to our workflow file:

# A workflow run is made up of one or more jobs that can run
# sequentially or in parallel.
jobs:

Here we define the jobs that we want our workflow to run as well as their order. Our build job looks like the following:

build:
  runs-on: ubuntu-latest

  steps:
    # Checks-out your repository under $GITHUB_WORKSPACE, so your
    # job can access it
    - uses: actions/checkout@v2
        
    - name: Set up Python 3.8
      uses: actions/setup-python@v2
      with:
        python-version: '3.8'
          
    - name: Install nbconvert
      run: |
        python -m pip install --upgrade pip
        pip install nbconvert

    - name: Convert notebook
      run: jupyter nbconvert --to python traffic.ipynb

    - name: Upload python script
      uses: actions/upload-artifact@v2
      with:
        name: traffic.py
        path: traffic.py

The “runs-on” directive determines the base container image used to run our job. In this case, we’re using “ubuntu-latest.” The available base images to use are listed here, or you can create your own self-hosted runners with Docker. After that, we define the steps to run in our job. This consists of first checking out the code in our repository and setting up Python using built-in actions.

Once Python is set up, we pip install nbconvert. We then use nbconvert, which works as a subcommand of Jupyter, to convert our notebook file to a Python file. Note that you’ll need to specify the correct .ipynb file in your repository—mine is called traffic.ipynb. The file produced by nbconvert will have the same name as the notebook file but with the .py extension.

Finally, we upload the generated Python file so that it can be shared between jobs and stored once the workflow completes. This is necessary because we’ll need to access the script from our “deploy” job. It’s also useful because the artifact is now available to view and download from the workflow run, including historical runs.

Now that we have our Python script generated, we need to implement a job to deploy it to AWS. This happens in two steps: upload the script to an S3 bucket and update a Glue job to use the new script. To do this, we’ll need to install the AWS CLI tool and configure credentials in our job. Here is the full deploy job definition, which I’ll talk through below:

deploy:
  needs: build
  runs-on: ubuntu-latest

  steps:
    - name: Download python script from build
      uses: actions/download-artifact@v2
      with:
        name: traffic.py
          
    - name: Install AWS CLI
      run: |
        curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip"
        unzip awscliv2.zip
        sudo ./aws/install
          
    - name: Set up AWS credentials
      shell: bash
      env:
        AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
        AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
      run: |
        mkdir -p ~/.aws
        touch ~/.aws/credentials
        echo "[default]
        aws_access_key_id = $AWS_ACCESS_KEY_ID
        aws_secret_access_key = $AWS_SECRET_ACCESS_KEY" > ~/.aws/credentials
          
    - name: Upload to S3
      run: aws s3 cp traffic.py s3://${{secrets.S3_BUCKET}}/traffic_${GITHUB_SHA}.py --region us-east-1
      
    - name: Update Glue job
      run: |
        aws glue update-job --job-name "Traffic ETL" --job-update \
"Role=AWSGlueServiceRole-TrafficCrawler,Command={Name=glueetl,ScriptLocation=s3://${{secrets.S3_BUCKET}}/traffic_${GITHUB_SHA}.py},Connections={Connections=redshift}" \
--region us-east-1
      
    - name: Cleanup
      run: rm -rf ~/.aws

We use “needs: build” to specify that this job depends on the “build” job. This determines the order in which jobs are run. The first step is to download the Python script we generated in the previous job.

Next, we install the AWS CLI using the steps recommended by Amazon. The AWS CLI relies on credentials in order to make API calls, so we need to set those up. For this, we use GitHub’s encrypted secrets which allow you to store sensitive information within your repository or organization. This prevents our credentials from leaking into code or workflow logs. In particular, we’ll use an AWS access key to authenticate the CLI. In our notebook repository, we’ll create two new secrets, AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY, which contain the respective access key tokens. Our workflow then injects these into an ~/.aws/credentials file, which is where the AWS CLI looks for credentials.

With our credentials set up, we can now use the CLI to make API calls to AWS. The first thing we need to do is copy the Python script to an S3 bucket. In the workflow above, I’ve parameterized this using a secret called S3_BUCKET, but you could also just hardcode this or parameterize it using a configuration file. This bucket acts as a staging directory for our Glue scripts. You’ll also notice that I append the Git commit SHA to the name of the file uploaded to S3. This way, you’ll know exactly what version of the code the script contains and the bucket will retain a history of each script. This is useful when you need to debug a job or revert to a previous version.

Once the script is uploaded, we need to update the Glue job. This requires the job to be already bootstrapped in Glue, but you could modify the workflow to update the job or create it if it doesn’t yet exist. For simplicity, we’ll just assume the job is already created. Our update command specifies the name of the job to update and a long –job-update string argument that looks like the following:

Role=AWSGlueServiceRole-TrafficCrawler,Command={Name=glueetl,ScriptLocation=s3://${{secrets.S3_BUCKET}}/traffic_${GITHUB_SHA}.py},Connections={Connections=redshift}

This configures a few different settings on the job, two of which are required. “Role” sets the IAM role associated with the job. This is important since it determines what resources your Glue job can access. “Command” sets the job command to execute, which is basically whether it’s a Spark ETL job (“glueetl”), Spark Streaming job (“gluestreaming”), or a Python shell job (“pythonshell”). Since we are running a PySpark job, we set the command name to “glueetl” and then specify the script location, which is the path to our newly uploaded script. Lastly, we set a connection used by the job. This isn’t a required parameter but is important if your job accesses any Glue data catalog connections. In my case, that’s a Redshift database connection I’ve created in Glue, so update this accordingly for your job. The Glue update-job command is definitely the most unwieldy part of our workflow, so refer to the documentation for more details.

The last step is to remove the stored credentials file that we created. This step isn’t strictly necessary since the job container is destroyed once the workflow is complete, but in my opinion is a good security hygiene practice.

Now, all that’s left to do is see if it works. To do this, simply commit the workflow file which should kick off the GitHub Action. In the Actions tab of your repository, you should see a running workflow. Upon completion, the build job output should look something like this:

And the deploy output should look something like this:

At this point, you should see your Python script in the S3 bucket you configured, and your Glue job should be pointing to the new script. You’ve successfully deployed your Glue job and have automated the process so that each new commit will deploy a new version! If you wanted, you could also extend this workflow to start the new job or create a separate workflow that runs on a set schedule, e.g. to kick off a nightly batch ETL process.

Hopefully you’ve found this useful for automating your own processes around AWS Glue or Jupyter notebooks. GitHub Actions provides a convenient and integrated solution for implementing CI/CD pipelines. With it, we can build a nice development workflow for getting Glue ETL code to production with continuous deployment.