What it means to run a monolith on AWS Lambda
May 29, 2021
Let’s talk about the
elephant monolith in the room. It’s easy
to associate monoliths with legacy, weird, outdated, insecure code.
A beast developed for decades that nobody wants to touch because
they’ll break it and it is impossible to wrap your head around
everything. Today I want to talk about monolith, but not exactly this kind.
Maybe what I mean by monolith can also be interpreted as a Macroservice.
A single source code repository worked by a team of more than one
person that aggregate many business features sometimes not related
to one another and deployed as a single artifact. Something that
10 years from now may be seen as a legacy monstrosity by a different
team in a different era, but that today is a live system
and (pardon my biased) well maintained.
This post will detail my personal view of many things in the software development process. I am in no way looking to convince anyone of anything. This is simply how I’ve been working for the past 2 years. Feel free to agree, disagree, learn or abhor anything and everything in chunks or bulks.
Simplicity. I can’t put it into better words. I’m looking for the most efficient way for a complete journey of my job: understand, implement, test, release and guarantee continuity of a feature. For the last 50 years the industry gathered a lot of knowledge on how to do all of these things and serverless is the most recent paradigm shift. It mainly targets the “guarantee continuity” step of a software-based company. But that’s where the biggest problem in running a software for many years lies, and it often seems intuitive that if we change all of our development process to fit into serverless (namely AWS Lambda) then we’re addressing the big problem. Professionals that have been long enough in this profession knows that no new solution comes without its own set of problems and AWS can be a source of an uncountable number of problems. If you’re not careful, you’re suddenly spending all of your time fixing release pipelines, orchestrating multiple containers for local development, fighting unreliable automation tests, building resiliency against timeouts and navigating a distributed system that is just as impossible to maintain as the legacy monolith but for a whole new set of reason. The ability to utilize 50 years of monolithic development knowledge while using the (arguably) best tool for guaranteed continuity in the market is why I’m running a macroservice on AWS Lambda.
1 Team = 1 Service
After struggling a lot with microservices, I spent days analysing why I was struggling, what was bad and where I went wrong. I don’t remember where, but I do remember reading the term “distributed monolith” somewhere, which was the perfect explanation for all my suffering: multiple microservices coupled to each other that had all the downsides of monoliths and none of the benefits of microservices. I came to the conclusion that I love the idea of microservices when my responsibility ends in one side of the communication and another team’s responsibility starts on the other side. In simple words: if I have to make an API call from Service A to Service B, but I’m responsible for both services, then I’m dealing with latency, retrials, producer bugs, consumer bugs and all the distributed nature of software. All of this while sacrificing the ability to write a simple Feature Test that would otherwise cover the functionality. My conclusion is that if my team is in a position of maintaining a communication between two services, then we will actually be merging those two services into one and making a function call instead. That way we can easily write a feature test that guarantees the functionality without having to orchestrate services locally or in production.
One positive argument around microservices are it’s ability of running on hardware that better match it’s purpose. For instance, an API Controller has different hardware needs than an SQS Worker. However, this does not have to be sacrificed when working with a monolith / macroservice. We may still deploy a subset of our API endpoints into one AWS Lambda with e.g. 1536MB of RAM while another subset of API endpoints are in another Lambda with just 512MB of RAM and lastly our background worker may still be another Lambda with yet a different configuration. But for simplicity, all 3 Lambda functions have the entire codebase loaded. Yes, this mean that cold start is slightly worst for “no good reason”, meaning that AWS Lambda is downloading your entire source code repository even though a huge chunk of code will never be executed there. Personally, I never had to optimize this. I know that I could invest time into building such chunks of code in a way that Lambda 1 would only have relevant code for its own endpoints while Lambda 2 would only have another set of code. And if you’re a large enough organization, you may be forced into doing it because of the 250MB limit on AWS Lambda. I simply have not reached that problem yet. Source code also doesn’t take a lot of disk space.
The biggest advantage of this mindset is to allow for automation tests / Feature Tests to be seen as one unit of code while production infrastructure still being sharded for better performance.
Queuing System / Background Workers
AWS SQS is an amazing service for offloading work that can be done in the background. And it even abstracts the code necessary for the worker because you can subscribe Lambda to be triggered by an SQS message. Over the years I learned to identify two major types of Queueing systems: asynchronously offloading a unit of work and delegating a unit of work to another service. If you have worked with Laravel, you’re probably familiar with the first case. A Job class will represent the unit of work to be queued and serialized. This is an extremely coupled scenario where the message contains PHP/Laravel-specific information. As someone who got introduced to queues in that scenario, I used to think that queues are only a private resource and that a project should provide an HTTP API, validate the payload and then offload it into it’s own private queueing system. While studying for the AWS Developer and DevOps Professional certifications, I started to run into cloud-native scenarios where SQS is used as the communication channel between two independent services. Technically it means that validation of the payload in advance is not an option, but it’s much more scalable and allows for a more resilient distributed system. The key to a successful implementation is rather communication and upfront agreement: Who writes messages, what are the acceptable payload format and who works those messages. If an invalid message is located, it can be driven into a Dead Letter Queue. Is it invalid because of the producer? Then the producer must be fixed. Is the worker rejecting valid messages? Then the worker must be fixed.
When aligning the idea of 1 Service = 1 Team with this mindset,
queues can be used for semi-decoupled processes. Instead of
writing an SQS Message that delegates to another service, we
can write a message to an internal queue where we’re responsible
for handling it ourselves. As long as the business allows for
the fact that this makes the process asynchronous, then we’re
able to decouple the producer from the consumer without separating
the codebase. Automation tests would still be able to use some
sync driver where the job is simply dispatched synchronously
while the codebase could be seen as slightly separated. The
benefits of decoupled code is also something praised by the last
50 years of software development. In case of team growth, we can
surgically split the codebase into two distinct source code
repository and adjust the payload from an “internal job class”
into a business message. That’s when a 2nd team becomes responsible
for a specific part of the project.
The development practices
Microservices, nanoservices and truly cloud-native application brings it’s own set of challenges. I’m not against learning new technology and facing problems in different ways in an attempt to look for a better outcome. However, there is one practice that I learned in 11 years of software development that beats anything else: Automated Feature Testing. This is the one and only practice that has allowed me to evolve rapidly, deploy on fridays if I really need to, upgrade PHP, Laravel, database engine or refactor the entire codebase. Feature tests are about representing a business need in the exact way that the user will interact with that feature and perform assertions on the expected outcome. They may be seen by some people as Behavior Driven Development or Integration Testing. However, several articles that I’ve read in the past bring attention to the same problem with the existing terminology: Are integration tests suppose to integrate several classes or several services? Can behavior tests make use of mocks? These questions highlight why I like the term Feature Tests. As a team member responsible for one service, my feature tests will integrate as many classes as needed within my service. But the process will resemble a unit test because it will allow me to mock the edge of another service. For instance, if I’m suppose to write an SQS message that delegates work to a 2nd team, then my Feature Test stops at the AWS SDK right before it writes said message. On the other hand, if I’m writing a message for an internal job which I’m responsible, then I can swap SQS with a Sync Driver so that the automation test runs the entire process (end to end). My assertion is in the result of the job being worked and not on the ability to produce a request for a unit of work.
Since my responsibility is primarily Backend and DevOps work, almost all of my Laravel Feature Tests will be about interacting with the API endpoints I provide. If I ever need to make a change to how an API is called, I know that’s a breaking change. I also know that if I’m changing the assertions being performend, then that’s a behavioral change. Changing the arrangement / state of the application before the action is performed can be allowed provided that we’re able to automatically migrate the state of the database / customers so that the changes are basically not noticeable by the users. When upgrading external dependencies, Feature Tests will demonstrate whether these dependencies have breaking changes or behavioral changes that has to be mitigated by the code.
On the other side of the spectrum, there are extensive debate on how to test cloud-native applications. AWS offers DynamoDB for local development. MinIO can be used as a drop-in replacement for S3 in a lot of cases. MySQL may be used as an alternative for Aurora. But the more cloud features we make use, the less likely we’ll be able to replicate them locally. Tools like localstack make a great effort in replicating AWS APIs for local development. It’s truly a tremendous piece of technology. But I work on a small team of PHP developers with limited understanding of the entire suite of AWS offerings. And a great deal of our work can be simplified by a good set of testable APIs. APIs such as Filesystem, SQS, Database Connection. Laravel’s strategy of swapping the implementation with an environment variable so that PHPUnit can make assertion about files, messages or database changes without worrying about whether it will work or not in production. It’s definitely not a perfect system and it’s common to have a feature “broken” in its first release because environment variables are neglected; the automation test is making use of local environment variables while the deployed version make use of AWS CloudFormation definition. So if variables are not introduced into the deployment template, the feature will not work. This type of error is easily identifiable by interacting with the feature for the first time when launching it, and it rarely breaks after introducing the proper set of variables.
Part of the work that I produce involves providing public APIs to customers. These APIs are available behind an HTTP protocol, which involves a domain. A monolith is not forced to be offered behind a single domain. Laravel, for instance, can even provide routes for specific domains. With AWS Lambda, we can have multiple domains pointing to the same Lambda function. This setup provides the fragmented perception that allows for a good customer experience by using purpose-built domain name that directly map to the area of usage while source code maintenance is still kept as a single service. It also provides a good imaginary boundary around the codebase for a potential area of separation.
This one is a lost cause. A single source code repository ties the deployment process into a single unit. It’s often hard - or sometimes even impossible - to decouple things that are ready for release from things that are not ready for release if two or more major work has started in parallel and both have been merged into the staging branch for QA. Feature flags and keeping features small and releasable can mitigate, but the winning score goes to separated services on this subject. However, it’s important to pay attention to the distributed monolith situation again. I have been in situations where deployment of several services had to be orchestrated in a specific sequence to avoid disruption or downtime. This to me was a sign of over-engineered architecture that had the drawback of a monolith with a sprinkle of complication brought by separated codebase.
PHP engineers are not known for it’s extensive knowledge on Cloud Engineering. But I also have this wild guess that this isn’t limited to one language. There are a number of software engineers that will still be good / great at their jobs without being interested in infrastructure, be it servers, load balancers, cloud, serverless or anything in between. But onboarding engineers with extensive experience in cloud native applications is not something that can be taken lightly. Its hard. Even though AWS Lambda is already 7 years old it has drastically changed / evolved since its inception. It’s a new paradigm that is constantly growing and adapting. In the meantime, there’s this known and proven development practice of writing tests and shipping APIs / background workers that can cover from basic webhook functionality to an enterprise-grade online software solution. Releasing that behind AWS Lambda not only provides a cheap “hosting” solution, but it also comes with nullable maintenance overhead in terms of operating system, scalability and security patches. With millisecond billing and improved network capabilities that brought cold start down drastically, AWS Lambda saves a lot on system management and scalability tuning without affecting the hiring practices and reducing the pool of professionals available in the market.
I first started targeting AWS Lambda as a hosting solution for a monolithic application about 2 years ago and since then I adapted a lot my understanding of software development practices for the current world we live in. I do believe that in the next 10 years Serverless will become more and more mainstream and all of its weaknesses will be targeted and brought down. In the meantime we can still make use of its fantastic elasticity and capabilities by making small tweaks and adjustments to the software deployment process so that we can combine great tools and proven knowledge while creating an ecosystem that will easily scale for the needs of the business that will exist within the next 5~10 years. The next generation of developers will surely look down at all of the “legacy” I’ll be leaving behind and may or may not like to work on it at all. But they won’t be held back by security updates, major releases of external dependencies, or a constant stream of bugs that are hard to diagnose and fix while working on their own next generation of enterprise solution. If this holds true, then I will be happy with the legacy monolith I will be leaving behind.
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