A lot of this first law was specifically coupled to how these systems often hid that distributed objects were distributed. In the past 10 years, async has become far more common place, and it makes the distributed boundary much less like a secret special anomaly that you wouldn't otherwise deal with and far more like just another type of async code.
I still thoroughly want to see capnproto or capnweb emerge the third party handoff, so we can do distributed systems where we tell microservice-b to use the results from microservice-a to run it's compute, without needing to proxy those results through ourself. Oh to dream.
AI has also changed the dynamics around this. Splitting things into smaller components now has a dev advantage because the AI program better with smaller scope
I dream of a SQL like engine for distributed systems where you can declaratively say "svc A uses the results of B & C where C depends on D."
Then the engine would find the best way to resolve the graph and fetch the results. You could still add your imperative logic on top of the fetched results, but you don't concern yourself with the minutiae of resilience patterns and how to traverse the dependency graph.
I think SQL alone is great if you didn't drink the microservice kool-aid. You can model dependencies between pieces of data, and the engine will enforce them (and the resulting correct code will probably be faster than what you could do otherwise).
Then you can run A,B,C and D from a consistent snapshot of data and get correct results.
The only thing microservices allow you to do if scale stateless compute, which is (architecturally) trivial to scale without microservices.
I do not believe there has been any serious server app that has had a better solution to data consistency than SQL.
All these 'webscale' solutions I've seen basically throw out all the consistency guarantees of SQL for speed. But once you need to make sure that different pieces of data are actually consistent, then you're basicallly forced to reimplement transactions, joins, locks etc.
You could build something like this using Mangle datalog. The go implementation supports extension predicates that you can use for "federated querying", with filter pushdown. Or you could model your dependency graph and then query the paths and do something custom.
You could also build a fancier federated querying system that combines the two, taking a Mangle query and the analyzing and rewriting it. For that you're on your own though - I prefer developers hand-crafting something that fits their needs to a big convoluted framework that tries to be all things to all people.
99% of systems out there are not truly microservices but SOA(fat services). A microservice is something that send emails, transforms images, encodes video and so on. Most real services are 100x bigger than that.
Secondly, if you are not doing event sourcing from the get go, doing distributed system is stupid beyond imagination.
When you do event sourcing, you can do CQRS and therefore have zero need for some humongous database that scales ad infinitum and costs and arm and a leg.
Generally, the microservices that I've seen work well are the type of things that you could decide to "buy" in the build vs buy debate - like you say, stuff that are either "fire and forget" or stuff where you only care about a fixed output produced, not the guts of how it's done.
Anything that depends on your core business logic within the service (if customer type X, do custom process Y) is probably not going to be a clean fit for microservices as you'd think, especially with an emergent design.
I once participated in implementing a system as a monolith, and later on handled the rewrite to microservices, to 'future-proof' the system.
The nice thing is that I have the Jira tickets for both projects, and I have actual hard proof, the microservice version absolutely didn't go smoother or take less time or dev hours.
You can really match up a lot of it feature-by-feature and it'll be plainly visible that the microservice version of the feature took longer and had more bugs.
And Imo this is the best case scenario for microservices. The 'good thing' about microservices, is once you have the interfaces, you can start coding. This makes these projects look more productive at least initially.
But the issue is that, more often than not, the quality of the specs are not great to awful, I've seen projects where basically Team A and Team B coded their service against a wildly different interface, and it was only found in the final stretch that these two parts do not meet.
"The consequence of this difference is that your guidelines for APIs are different. In process calls can be fine-grained, if you want 100 product prices and availabilities, you can happily make 100 calls to your product price function and another 100 for the availabilities."
While this is true, in fact for efficiency reasons it's often better to treat even local dispatch like it's "network" -- chasing pointers and doing things one at a time in a loop is far less efficient on a modern architecture than doing things in bulk and vectorized.
Non uniform memory hierarchies, caches, branch predictors, SIMD, and now GPUs, etc. all tend to reward working with data in batches.
If I were to think of a "pure" model of computation that unified remote and local it would be to treat the entire machine in terms of the relational data model, not objects. To treat all data manipulation and decisions like a query.
And to ideally in fact have the same concept of a query optimizer / planner that a DBMS has, which is able to make decisions on how to proceed based on the cost of the storage model, the indexes, etc. because it has a bigger picture of what the programmer is trying to accomplish.
Its a weird notion of a distributed object, even in 2014, I think i would never consider calling the methods of a distributed object directly with something linke RPC but instead replicate the objects with a replication protocol and then use the replicas locally.
I'm always going to say that if you have third-party integrations where you call out to other organizations' services, that will be the thing that breaks down the most. You have to armor the heck out of it and plan for contingencies, and yes, that includes when third party is <Famous Company Where Surely Nothing Ever Goes Wrong>.
Microservices are just a slightly more reliable version of that, since you can hassle the author as coworker instead of via harried FCWSNEGW support mouse.
If you've ever used NFS you've used one of the oldest distributed object systems still in use, developed by Sun Microsystems. Variously called Sun RPC, Remote Procedure Calls, or Open Network Computing Remote Procedure Call
If you can't design and implement a clean, well-structured monolith using proper modularization and libraries, you will almost certainly fail to build a clean and maintainable microservices system.
The most unmaintainable system I've ever encountered in my long career was a 30+ year-old microservices architecture consisting of over 100 processes. Even the simplest use cases had devolved into a nightmare of timing issues, fragile dependencies, and unnecessary complexity.
Yes let me repeat that it was a 30+ years old microservices architecture. There is nothing new about microservices.
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[ 3.8 ms ] story [ 47.2 ms ] threadI still thoroughly want to see capnproto or capnweb emerge the third party handoff, so we can do distributed systems where we tell microservice-b to use the results from microservice-a to run it's compute, without needing to proxy those results through ourself. Oh to dream.
100% true in retrospect.
Which is not to say it isn't valid.
Then the engine would find the best way to resolve the graph and fetch the results. You could still add your imperative logic on top of the fetched results, but you don't concern yourself with the minutiae of resilience patterns and how to traverse the dependency graph.
Then you can run A,B,C and D from a consistent snapshot of data and get correct results.
The only thing microservices allow you to do if scale stateless compute, which is (architecturally) trivial to scale without microservices.
I do not believe there has been any serious server app that has had a better solution to data consistency than SQL.
All these 'webscale' solutions I've seen basically throw out all the consistency guarantees of SQL for speed. But once you need to make sure that different pieces of data are actually consistent, then you're basicallly forced to reimplement transactions, joins, locks etc.
You could also build a fancier federated querying system that combines the two, taking a Mangle query and the analyzing and rewriting it. For that you're on your own though - I prefer developers hand-crafting something that fits their needs to a big convoluted framework that tries to be all things to all people.
Secondly, if you are not doing event sourcing from the get go, doing distributed system is stupid beyond imagination.
When you do event sourcing, you can do CQRS and therefore have zero need for some humongous database that scales ad infinitum and costs and arm and a leg.
Generally, the microservices that I've seen work well are the type of things that you could decide to "buy" in the build vs buy debate - like you say, stuff that are either "fire and forget" or stuff where you only care about a fixed output produced, not the guts of how it's done.
Anything that depends on your core business logic within the service (if customer type X, do custom process Y) is probably not going to be a clean fit for microservices as you'd think, especially with an emergent design.
I once participated in implementing a system as a monolith, and later on handled the rewrite to microservices, to 'future-proof' the system.
The nice thing is that I have the Jira tickets for both projects, and I have actual hard proof, the microservice version absolutely didn't go smoother or take less time or dev hours.
You can really match up a lot of it feature-by-feature and it'll be plainly visible that the microservice version of the feature took longer and had more bugs.
And Imo this is the best case scenario for microservices. The 'good thing' about microservices, is once you have the interfaces, you can start coding. This makes these projects look more productive at least initially.
But the issue is that, more often than not, the quality of the specs are not great to awful, I've seen projects where basically Team A and Team B coded their service against a wildly different interface, and it was only found in the final stretch that these two parts do not meet.
While this is true, in fact for efficiency reasons it's often better to treat even local dispatch like it's "network" -- chasing pointers and doing things one at a time in a loop is far less efficient on a modern architecture than doing things in bulk and vectorized.
Non uniform memory hierarchies, caches, branch predictors, SIMD, and now GPUs, etc. all tend to reward working with data in batches.
If I were to think of a "pure" model of computation that unified remote and local it would be to treat the entire machine in terms of the relational data model, not objects. To treat all data manipulation and decisions like a query.
And to ideally in fact have the same concept of a query optimizer / planner that a DBMS has, which is able to make decisions on how to proceed based on the cost of the storage model, the indexes, etc. because it has a bigger picture of what the programmer is trying to accomplish.
Microservices are just a slightly more reliable version of that, since you can hassle the author as coworker instead of via harried FCWSNEGW support mouse.
The most unmaintainable system I've ever encountered in my long career was a 30+ year-old microservices architecture consisting of over 100 processes. Even the simplest use cases had devolved into a nightmare of timing issues, fragile dependencies, and unnecessary complexity.
Yes let me repeat that it was a 30+ years old microservices architecture. There is nothing new about microservices.