inai VS opentracing-javascript

This is useful! Thanks for writing this out. I find in-process REST intriguing as it stands a little in contrast with OOP techniques. One possibility I’ve with in-process REST is that it seems to make live patches feasible - more feasible than in erlang. I use the idea as the core of Inai - https://github.com/imaginea/inai ... so am curious to play with that aspect of ObjS.

A bit of a shameless plug, but really looking for feedback .. I've been experimenting with Inai [1][2], a framework that can help build modular microservice-like software with similar Dev team independence properties but can independently be built and deployed as a monolith or as separate services operationally. So far, I've had fun building an internal project at much higher speed than I've managed any similar project and had more fun doing it. I feel the idea (which is still nascent) has some merit, but would like to know what folks think.

[1] source - https://github.com/Imaginea/Inai

[2] blog post describing Inai - https://labs.imaginea.com/inai-rest-in-the-small/

At some point, this would be come close to the AppleScript protocol or Symbian OS.

> So the drawing part of an application would just be a process that sends the screen/compositor process a message describing the state of its window as a tree, and receives messages for events in response.

I've been toying with an interpretation of this here - https://github.com/Imaginea/inai - and kind of having fun with it .. and even built a prototype internal app using it. Super early stage and so stuff won't necessarily make sense at the outset .. or possibly ever. Thoughts welcome though.

> A big advantage is it makes the semantics of composing GUIs a lot more reasonable "replace this leaf of my tree with this other process' tree" ...

The "dom" service in Inai pretty much feels like that. I felt like an idiot to try and (for lack of a better expression) REST-ify the DOM, but it seemed to work to my surprise.

> An application could also "proxy" for a widget, including over a network link, so you get fairly simple network transparency this way too.

.. yeah due to the "REST" nature, this becomes pretty straightforward.

Spring Spring web framework Spring WebFlux Reactive REST Services Spring Data R2DBC a specification to integrate SQL databases using reactive drivers Redisson Redis Java Client Zipkin open source, end-to-end distributed tracing Spring Cloud Sleuth auto-configuration for distributed tracing Prometheus monitoring and alerting Grafana for to compose observability dashboards with everything from Prometheus Kubernetes automating deployment, scaling, and management of containerized applications Docker and docker-compose Helm The package manager for Kubernetes Flywaydb for migrations

Elasticsearch client for Go RabbitMQ Go RabbitMQ Client Library Jaeger open source, end-to-end distributed tracing Prometheus monitoring and alerting Grafana for to compose observability dashboards with everything from Prometheus Echo web framework Kibana is user interface that lets you visualize your Elasticsearch Docker and docker-compose Kubernetes K8s Helm The package manager for Kubernetes

PostgeSQL as event store database Kafka as messages broker gRPC Go implementation of gRPC Jaeger open source, end-to-end distributed tracing Prometheus monitoring and alerting Grafana for to compose observability dashboards with everything from Prometheus MongoDB MongoDB database Elasticsearch Elasticsearch client for Go. Echo web framework Kibana Kibana is data visualization dashboard software for Elasticsearch Migrate for migrations

OpenTracing was an open-source project aimed at providing vendor-neutral APIs and instrumentation for distributed tracing. In distributed cloud-native applications, it is difficult for engineering teams to see how requests are performing across services. And that’s where distributed tracing comes into the picture.

OpenTracing is now archived, and it is suggested to migrate to OpenTelemetry.

In this article let's try to create closer to real world Event Sourcing CQRS microservice using: 🚀👨‍💻🙌 EventStoreDB The database built for Event Sourcing gRPC Go implementation of gRPC MongoDB Web and API based SMTP testing Elasticsearch Elasticsearch client for Go. Jaeger open source, end-to-end distributed tracing Prometheus monitoring and alerting Grafana for to compose observability dashboards with everything from Prometheus swag Swagger for Go Echo web framework Kibana Kibana is user interface that lets you visualize your Elasticsearch

I agree; but I think it'll take years, if ever, to change this culture.

Logging is a byproduct of a past time; everything is a file, stdout is a file, lets persist that file, now we have multiple replicas, lets collect the file into a multi-terabyte searchable database.

The biggest downside: Its WILDLY expensive. Large orgs often have an entire team dedicated to maintaining logging (ELK) infrastructure. This price-tag inevitably leads to bikeshedding on backend teams about how to "reduce the amount we're logging" or "cleaning up the logs" or "structuring them to be more useful".

Outside of development, they are so rarely useful. Yet inevitably someone will say: "you're just not structuring your logs correctly." Maybe that's true; similarly, I don't find vim to be a highly productive editing experience. Maybe I just don't have the thousands of extensions it would take to make it so. Or maybe You're stuck in the past and ignoring two decades of tooling improvement. Both can be true.

I'm phrasing this as a false dichotomy, because in many teams: it is. Logging is easy; its built-in to most languages; so devs log. The information we need is in the system; its a needle in a haystack, but the needle is there. We log when a request comes in, when it hits pertinent functions, when its finished, how its finished, the manager says: "just look at the logs". Instead of "what better tooling can we make an investment in so future investigations of this nature don't take a full day."

For starters: Tracing. Tracing systems should be built-in to EVERY LANGUAGE, just like console.log. We have a standard [1] sponsored by the Linux Foundation and supported by every major trace ingestion system. This is not a problem of camps and proprietary systems; its a problem of culture. I should be able to call a nodejs stdlib function at startup, specify where I want traces to go, sampling rate, etc, and immediately get every single function call instrumented. Its literally just highly-structured-by-default logging! Dump the spans to stdout by default! Our log ingestion systems can read each line, determine if its a span, if so route to trace ingestion, otherwise route to log ingestion.

This is a critical step because it asserts that tracing is actually a very powerful tool that everyone needs to learn, like logging. Everyone knows about logging. Why? console.log. Its there, it gets used. Tracing right now is relegated to a subculture of "advanced diagnostics"; you gotta adopt a tracing provider, bring in dependencies, learn each implementation of OpenTracing, authenticate to send traces over HTTP... as a community, we should normalize just "dump traces like you dump logs, to stdout", have a formatter to make them nice to use in development, and now all that instrumentation work that any dev is capable of utilizing (just like console.log) "just works" in production.

[1] https://opentracing.io/

Service mesh - ssup2ket services run on service mesh for detailed traffic control and easy monitoring. Service mesh is applied through Istio. Istio uses OpenTracing for easy request tracing between multiple services.