opentracing-javascript VS Echo

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.

Echo - web framework for Go

The three behaviors I've described that we want all depend on two things, the first of which is "idiomatic error handling". We need to be able to simply return err in our handlers. Unfortunately, the standard libray doesn't give us this. But some third-party frameworks do. The most popular one I'm familiar with is labstack echo, whose HandlerFunc looks like this:

In this tutorial, I will be using the Echo framework to build the backend. You can learn more about Echo here.

____ __ / __/___/ / ___ / _// __/ _ \/ _ \ /___/\__/_//_/\___/ v4.11.1 High performance, minimalist Go web framework https://echo.labstack.com ____________________________________O/_______ O\ ⇨ http server started on [::]:8080

Some of the features: - ✅ Using Vertical Slice Architecture as a high level architecture - ✅ Using Event Driven Architecture on top of RabbitMQ Message Broker with a custom [Event Bus](pkg/messaging/bus/) - ✅ Using Event Sourcing in Audit Based services like [Orders Service](services/orders/) - ✅ Using CQRS Pattern and Mediator Patternon top of Go-MediatR library - ✅ Using Dependency Injection and Inversion of Controlon top of uber-go/fx library - ✅ Using RESTFul api with Echo framework and using swagger with swaggo/swag library - ✅ Using Postgres and EventStoreDB to write databases with fully supports transactions(ACID) - ✅ Using MongoDB and Elastic Search for read databases (NOSQL) - ✅ Using OpenTelemetry for collection Distributed Tracing with using Jaeger and Zipkin - ✅ Using OpenTelemetry for collection Metrics with using Prometheus and Grafana - ✅ Using Unit Test for testing small units with mocking dependent classes and using Mockery for mocking dependencies - ✅ Using End2End Test and Integration Test for testing features with all of their real dependeinces using docker containers (cleanup tests) and testcontainers-go library

If you come from NodeJS background, you may find Echo (https://echo.labstack.com) most similar to express.

Can I try to rewrite it using the following? I'll just hand you the code I don't care about credit, I just enjoy cleaning things up. - https://github.com/spf13/cobra - https://echo.labstack.com/ - SQLite - and not a bunch of if statements

Use a library for HTTP serving, such as Gin, Chi, or Echo. I personally use Chi, as it's just the right level of abstraction for how I like to work. Despite what others say here, don't try to re-implement everything in a modern serving library using the standard library.

Here I'm not really sure what you're referring to: * You can set request timeout and it has nothing to do with whether you handled your error or not. * In most cases you either bubble it up the callstack or do something with error in place you o received it i.e. you switch to default value, retry or sth along those lines. In some cases frameworks like echo will translate error into 5XX response for you if you don't do anything with it in top level handler. * Panics are recoverable. Also in case your handler panics it won't crash entire server -> stdlib HTTP server just closes connection, frameworks might even provide panic handler which will return 5XX instead of nothing. * try/catch doesn't really solve anything I mentioned here ¯_(ツ)_/¯. You just hope somebody caught your exception somewhere else.