I wonder what setting up a local dev instance is like for anything involving more than one or two of those.
And the thing is that nobody ever needs to run the entire stack other than end to end tests which get run in the cloud.
You just checkout the services you need and because they are designed to be isolated the dependencies will usually be automatically stubbed out. So it's just a matter of running them or chaining them together if you have a particular scenario to test.
Q2: How do you ensure the stubbed deps behave like the real thing?
Q3: how do you handle logging and metrics in an unified way across the stack? And related to this: how do you ever get to upgrade services crosscutting concerns that ideally are not invented in every service?
SRE here. Generally speaking, each API or each service will have a contract that it must adhere to depending on upstream and downstream relationships and their fail safes. Each service (or API) will then load test in isolation.
After that, if you want to be really sure about regressions (which would include fail safes) you load test the whole thing put together.
> Is the key some sort of meta tooling that understands relationships between microservices?
This is quite hard to do when you have a lot of transactions. I don't think there's commodity software that does this because you'd need to configure that software to map on keys, then map those keys to services. Generally, the easiest way is to get engineering teams to declare upstreams and downstreams.
> Q2: How do you ensure the stubbed deps behave like the real thing?
Generally, generation. Something like protobuf or Open API generation will do.
> Q3: how do you handle logging and metrics in an unified way across the stack?
You issue high level standards like, "We'll use JSON logging with UTC time formatting". At the end of the day logging is very contextual and in a service ownership model the service owners are usually the ones reading and alerting on their logs.
> And related to this: how do you ever get to upgrade services crosscutting concerns that ideally are not invented in every service?
Shared dependencies. I'm not actually sure what's a cross cutting concern; generally services that are this small should be designed to operate mostly independently. They're small, but "microservices" tend to have a lot of fail safes built in. If you're referring to how do we not write 4000 config loaders then there's usually a team that builds a very generic config loader and everyone or a majority use it.
perhaps simplest, biggest impact in my log life has been adhering these principles.
Q1: (perf) these tools exist, the buzzword phrase is "distributed tracing". The relationships are actually not explicitly defined for the tooling to work, but rather inferred. Visualize a network call as a call-stack, where each service is a level in the stack. Jaeger (a CNCF project addressing distributed tracing) was coincidentally started by Uber.
Q2 (stubs): In my experience, mocked responses get you a long, long way. Typically the API response type that you're mocking is generated from a protobuf (or thrift, OpenAPI, etc.) file. If your dependency changes that type in a way that breaks your test, the CI platform will let them know.
If it's a more subtle change (like, it used to deterministically return 18 and now it deterministically returns 20), it's really on the service owners to communicate changes and grep the code base before making the change.
Q3 (logging/metrics): Typically by using shared "logging" and "metrics" lib for each language. Every service will typically be a gRPC service and accordingly a standardized + generated-from-protobufs set of metrics to Prometheus, by default.
Q4 (how to upgrade common libraries): this is definitely a tricky one. The answer is, basically, really carefully. Typically, you'll want your infrastructure to be compatible with vX and vX+1, and give teams a deadline to cut over from logging X to X+1. The couple of weeks before that deadline usually involves a lot of cat-herding and handwringing.
I like to view k8s a lot like erlang's OTP, if something isn't right with the state of a service, I advocate calling 'exit()' and letting the restart with exponential backoff handle the transient.
But you tend to try to write your service so that it treats everything else it depends on like a vendor-provided API. Like, if you were building a Slack bot, you wouldn't ask Slack to let you pull down and run a local copy of Slack's API to test against. You'd maybe set up a test account in Slack's production system, and run your local bot against that to test it before you deploy it with credentials to run against your real slack account.
In a microservice architecture, you integrate with other internal systems in the same way.
I find the opaqueness of other services to reduce development speed quite drastically. With local code I can view both sides of the fence and easily see if I'm using it wrong or if it's a bug in my colleagues code.
Seems that if you're constantly developing against opaque services you'd end up in the same quagmire quite quickly?
But when things don't work as I expect, it's far more efficient to be able to view the code on both sides, rather than only on my own side and try to guess what the other side is doing.
Besides the usual suspect of wrong understanding on my end leading to misuse, this can also be due to lacking or wrong documentation of the other system, or bugs in the other system due to unexpected inputs or similar.
Like just a few days ago we spent an unreasonable amount of time with an API of one of our customers, where we would get empty list back for some of our queries. Turned out something in their service crashed when handed national characters, despite accepting JSON and hence UTF-8 input and nothing in the documentation about English letters only. Rather than returning 400 or 500, the service returned 200 with an empty list, leading us to assume we did something wrong.
Are you able to view the source code of your platform vendor? Everyone is at some level dependent on Black box APIs.
If you can document where with certain input you don’t get the expected output, you reach out to the team that is responsible for it whether internally or externally and they either explain it or they fix it.
This is the API service I’ve been working with over the past five+ years - three actually working at AWS (Professional Services).
https://boto3.amazonaws.com/v1/documentation/api/latest/inde...
I found a bug in one relatively new API that a service team released, I reached out to the team with a documented scenario and they fixed it.
Other times they explained what I was doing wrong. That’s what any large organization does.
I’ve worked with other vendors and internal teams plenty of times over the years.
Uber employees’ apps are special and allow us to log in as these fake users and create fake rides or deliveries, and then we can look at the traces and logs to debug and stuff.