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I've worked on a couple of extremely large micro services projects. And the thing is that nobody ever needs to run the entire stack other than end to end tests

I've worked on a couple of extremely large micro services projects. 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.

plonk 3y ago
Is there typically a “cold restart” plan to rebuild the whole infra from scratch? I’m thinking of things like circular dependencies when services boot.
- nurple 3y ago
  I've found that the eventual consistency provided by orchestrators like k8s solve this problem rather well. If the services are written using the right paradigms to handle such a situation, they will also be much more resilient to platform disruptions. 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.
cma 3y ago
Nobody needs to if they can't possibly.
x86x87 3y ago
Question for you: how does performance measurement and optimization work in that environment? Is the key some sort of meta tooling that understands relationships between microservices? How would you express such relationships in the first place? 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?
- joshmanders 3y ago
  Not OP, but I worked on a large microservices based system at a leading financial institute and when we needed to work on a single service, we had docker compose files that pulled the images needed for the dependency services of that service for us to develop what we needed. They all just ran in our local docker. If we wanted we could have a massive compose file with all services in it, but typically we only needed the iam service and a few other small ones depending on what services we were working on.
- eddtries 3y ago
  Q2 - techniques like contract testing help here, beyond simple stubs. Also, mocked services maintained by the services original devs you can work against help
- oooyay 3y ago
  > how does performance measurement and optimization work in that environment? 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.

plonk 3y ago

Is there typically a “cold restart” plan to rebuild the whole infra from scratch? I’m thinking of things like circular dependencies when services boot.

nurple 3y ago
I've found that the eventual consistency provided by orchestrators like k8s solve this problem rather well. If the services are written using the right paradigms to handle such a situation, they will also be much more resilient to platform disruptions. 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.

cma 3y ago

Nobody needs to if they can't possibly.

x86x87 3y ago

Question for you: how does performance measurement and optimization work in that environment? Is the key some sort of meta tooling that understands relationships between microservices? How would you express such relationships in the first place? 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?

joshmanders 3y ago
Not OP, but I worked on a large microservices based system at a leading financial institute and when we needed to work on a single service, we had docker compose files that pulled the images needed for the dependency services of that service for us to develop what we needed. They all just ran in our local docker. If we wanted we could have a massive compose file with all services in it, but typically we only needed the iam service and a few other small ones depending on what services we were working on.
eddtries 3y ago
Q2 - techniques like contract testing help here, beyond simple stubs. Also, mocked services maintained by the services original devs you can work against help
oooyay 3y ago
> how does performance measurement and optimization work in that environment? 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.