Project Structure

Deploy engines as separate app instances and have them only communicate over network boundaries. This is something we’re starting to do more.

As I think today microservice can do much more than just gives predictions using a single model, like:

Python is known for using more memory than more optimized languages and, in this case, it uses 7 times more than PostgresML.

PostgresML outperforms traditional Python microservices by a factor of 8 in local tests and by a factor of 40 on AWS EC2.

Artifactory/Nexus/Docker repo was unavailable for a tiny fraction of a second when downloading/uploading packagesThe Jenkins builder randomly got stuck

Microservices can really bring value to the table, but the question is; at what cost? Even though the promises sound really good, you have more moving pieces within your architecture which naturally leads to more failure. What if your messaging system breaks? What if there’s an issue with your K8S cluster? What if Jaeger is down and you can’t trace errors? What if metrics are not coming into Prometheus?

If you’re going with a microservice:

some of the pros for going microservices

because commonly an API is not intended to handle high performance requirements.

There’s several benefits to splitting code into multiple packages, whether it be a library, micro-services or micro-frontends.

and how moving to a monolith was the solution that worked for Segment.

Clients are updated to use the new service rather than the monolith endpoint. In the interim, steps such as database replication enable microservices to host their own storage even when transactions are still handled by the monolith. Eventually, all clients are migrated onto the new services. The monolith is "starved" (its services no longer called) until all functionality has been replaced. The combination of serverless and proxies can facilitate much of this migration.

Good microservice design follows a few simple rules

What Microservices are supposed to be: Small, independently useful, independently versionable, independently shippable services that execute a specific domain function or operation. What Microservices often are: Brittle, co-dependent, myopic services that act as data access objects over HTTP that often fail in a domino like fashion.

In the mid-90s, “COM+” (Component Services) and SOAP were popular because they reduced the risk of deploying things, by splitting them into small components

central service registry

minimizing latency and enabling event-driven interactions with applications.

Tiago Roberto Lammers - Nossa jornada DevOps na Delivery Much para microserviços e o que aprendemos

One way to identify cycles is to build a dependency graph representing all services in the system and all RPCs exchanged among them. Begin building the graph by putting each service on a node of the graph and drawing directed edges to represent the outgoing RPCs. Once all services are placed in the graph, the existing dependency cycles can be identified using common algorithms such as finding a topological sorting via a depth-first search. If no cycles are found, that means the services' dependencies can be represented by a DAG (directed acyclic graph).

Dependency cycles are most dangerous when they involve the mechanisms used to access and modify a service. The operator knows what steps to take to repair the broken service, but it's impossible to take those steps without the service.