In my previous post, I described how migrating to Kubernetes involves transitioning an organization’s infrastructure from a legacy service platform (LeSP) to a Kubernetes service platform (KuSP). In this post, I want to go into more details on why this migration is hard and what you can do to reduce the pain.
Unlike my previous post on why service meshes are hard, my goal here is not to dissuade you from doing the migration in the first place, but rather to make it clear that there are a lot of decisions to be made and lots of work to be done. Migrating to Kubernetes can be very valuable, but you need to be prepared!
Why it’s hard
You’re migrating a platform, not a system
The main reason that migrating to Kubernetes is hard is that you’re not just updating a single component- you’re migrating to an entirely new platform, the KuSP, that has its own set of assumptions, requirements, and interfaces.
As described in part 1, the biggest shift in going from a LeSP to a KuSP is in the use of containers. Containers require images, which means that you need new workflows for defining, building, testing, and storing these. Containers usually have a different networking setup than that of “regular” LeSP application processes, which means that your networking infrastructure (how you allocate IPs, how service discovery works, how certs are provisioned, etc.) may have to change.
Having containers and orchestrating them via Kubernetes will typically also require changes to whatever frameworks you’re using for logging, metrics, secrets, performance monitoring, deploys, and other app lifecycle tooling. Although it’s possible to keep using the LeSP equivalents for these, at a minimum the interfaces will be slightly different; logs, for instance, will be written into a different place in the file system, and in a different format, which means that whatever log collector/forwarder you’re using will need to be reconfigured.
Many of these updates aren’t scary when considered independently. However, there are a lot of them to do and there are a lot of problems that can be encountered along the way, so the whole process can take a long time from end-to-end. And, it’s hard to run any mission-critical services in the KuSP in production before you have at least some basic implementations in place for each of the core platform components.
Identity is at a different granularity
As mentioned in part 1, a KuSP typically separates machine identity from application identity. While this is nice from a security and isolation standpoint, it can be a huge pain, particularly if legacy systems have ingrained the idea that machines map 1:1 to identities.
In the AWS world, for instance, IAM roles and network interfaces (with their associated IP addresses and security group designations) are typically tied to EC2 instances. Supporting container-level roles, externally addressable IPs, security groups, etc. is possible and has been getting slightly better over time, but is not yet super easy.
If you’re running a service mesh, then you’ll need to worry about pod-level certificates and proxies. Third party frameworks like Istio can help here, but they’re non-trivial to deploy and operate.
Configuration is complex
The Kubernetes configuration for a simple, single-container application is not too terrible. However, as you add in init and sidecar containers, shared volumes, scheduling constraints, health probes, and other features that production systems might need, these configs can get pretty hairy.
This complexity leads to at least two problems when adopting Kubernetes. First, you need to figure out how to set all of the knobs that the configs expose, which can require reading a lot of documentation and going through a lot of trial and error. Second, when multiplied out across dozens (or hundreds) of apps running across different environments, manually creating and updating the corpus of Kubernetes configs for an organization can become really tedious- you need some tooling to help.
Most companies address the second issue with a combination of YAML templating (via systems like Helm) and higher-level, organization-specific config generation tools. These help, but none of the existing options here is really perfect. See this post that I wrote for the Segment engineering blog last year for more detail.
As part of the migration process, you need to evaluate the various approaches here and either adopt a third-party tool or write you own, which can be a non-trivial amount of work.
Some batteries not included
Kubernetes includes a powerful set of base API primitives and tooling. However, the pieces it includes don’t cover 100% of what you need to run Kubernetes in production. Several big chunks, most significantly service networking, are specified in high-level terms but not actually implemented.
Thankfully, there are solid, third-party solutions available for these missing pieces. As with the identity mapping issues described above, however, there may be a lot of work involved to evaluate the various options, make a decision about which ones to use, and deploy them in your clusters.
Tips for a smoother migration
Pad your estimates
Migrating to Kubernetes in any medium-to-large organization is a really big, multi-year project. If you estimate that it will only take two quarters of work from two engineers, then you’re probably a bit off the mark unless you’re at a small company.
Estimating realistically at the beginning isn’t just for the team’s sanity- it’s also really important for setting expectations among your (internal) customers, who may be eagerly awaiting Kubernetes to address the pain points they’re facing in the organization’s LeSP. If they believe that the KuSP can be flicked on with a switch and that it will magically wash away all of their infrastructure management woes, then they’re bound to be disappointed when reality sets in down the line.
Identify key customers
Rather than make Kubernetes work for everyone from the beginning, it’s helpful to initially focus on a small number of “key customers”. Ideally, these are engineering teams that are:
- Frustrated by one or more limitations in the existing LeSP
- Technically savvy when it comes to infrastructure
- Enthusiastic about Kubernetes in general
- Eager to get their hands dirty and make the project a success!
By working with just these customers, the team running the migration can focus on a limited subset of use cases and get active help on improving the associated tools and processes. Once these customers are migrated and happy with the results (hopefully), they can serve as examples of how to use the KuSP and also help evangelize the migration within the organization.
At Airbnb, we initially worked with the team that was running Superset internally. At Stripe, we partnered with the teams that were building the company’s machine learning training pipelines and using these models to detect fraud in incoming charge requests. At both companies, doing these initial migrations was critical for proving that Kubernetes could work and provide value.
Involve the entire organization
Migrating customers in a 1:1, “white glove” manner is fine, but unless you have a 30 person migration team, this high-touch approach won’t efficiently scale. Once you’re migrated the initial “key customers”, proved that Kubernetes will work, and worked out the initial bugs, you need to get help from the larger organization.
Ideally, most teams are responsible for migrating their services themselves. This frees up the Kubernetes team to answer questions, monitor the shared infrastructure, improve general usability and reliability, and focus on the hairier migrations that need more hand-holding.
Unfortunately, some teams might prioritize other things above helping out with a messy infrastructure migration that doesn’t directly benefit them. This is where management really needs to step in, set priorities, and make it clear that product features will be pushed back to make room for migration work. If management isn’t willing to this this, then that’s fine, but they’ll need to understand that the project will go on for a very, very long time without this help.
Don’t just focus on the long tail
It’s tempting when doing a Kubernetes migration to start with small, non-critical services and then gradually work up to the big, “monster” services that lots of people work on and are key for delivering the company’s products.
While it’s fine to focus on the long tail at the beginning, I think procrastinating too much on attacking whatever “the beasts” are at an organization (e.g., that old, crufty service that handles all the API requests) can really be a detriment and reduce the project’s momentum.
First, these larger services are typically where there’s the most pain and where a migration can have the biggest impact on engineer productivity. Second, the only way to really flush out the bugs and rough edges, both in terms of usability and also in terms of the performance and reliability of the technology, is to exercise the KuSP in a high-usage, high-traffic environment.
If you’re only using Kubernetes for the less critical components of your stack, then it’s hard to claim that Kubernetes (and, by extension, your team’s work) is critical to the success of your company.
Avoid scope creep
Handling just the “must haves” in a Kubernetes migration is hard enough. Resist the temptation to treat this as an opportunity to fix every other legacy platform component that’s in a non-optimal state, particularly if the migration makes these things no worse than before.
While it would be great to get Kubernetes bundled with a new networking stack, a new secrets system, a new logging system, etc. each of these extra dimensions adds more things that will be unfamiliar to users, and more things that can break in production. Focus on what is needed to migrate successfully, then handle the “nice to haves” in later phases of the project.
Migrating to Kubernetes is a big deal because it’s a complicated system, and it touches on so many different aspects of the “platform” that apps run on in an organization.
Even with strong execution, the migration can take years and require contributions from engineering teams across the company. It’s hard work, and there are definitely still some rough edges in the Kubernetes ecosystem, but I think the end result can really improve developer happiness and productivity if implemented in a sensible way. Good luck!