grpc-go VS opentracing-javascript

The reflection service is open-sourced (at least for some sdks):

* https://github.com/grpc/grpc-go/blob/master/Documentation/se...

* https://chromium.googlesource.com/external/github.com/grpc/g...

We’re hoping to make this rate at least optional via this pull request but as the time of writing this blog, it’s nothing we can do to circle our way around it.

First, I started with gRPC’s recommended starter repository for learning gRPC, their **helloworld **example, which is a part of the official gRPC repository.

IDE: use whatever make you productive. I personally use vscode. VCS: git, as golang communities use github heavily as base for many libraries. AFAIK Linter: use staticcheck for linting as it looks like mostly used linting tool in go, supported by many also. In Vscode it will be recommended once you install go plugin. Libraries/Framework: actually the standard libraries already included many things you need, decent enough for your day-to-day development cycles(e.g. `net/http`). But here are things for extra: - Struct fields validator: validator - Http server lib: chi router , httprouter , fasthttp (for non standard http implementations, but fast) - Web Framework: echo , gin , fiber , beego , etc - Http client lib: most already covered by stdlib(net/http), so you rarely need extra lib for this, but if you really need some are: resty - CLI: cobra - Config: godotenv , viper - DB Drivers: sqlx , postgre , sqlite , mysql - nosql: redis , mongodb , elasticsearch - ORM: gorm , entgo , sqlc(codegen) - JS Transpiler: gopherjs - GUI: fyne - grpc: grpc - logging: zerolog - test: testify , gomock , dockertest - and many others you can find here

If you read the first comment, you’ll see the API was documented as being experimental.

https://github.com/grpc/grpc-go/issues/3798#issuecomment-670...

The lack of this analysis still results in bugs and CVEs. See how many races are found and fixed in gRPC releases: https://github.com/grpc/grpc-go/releases (search "race"). It's a shame Google does not publish these as CVEs, because many of them qualify.

I like to point people at this release to show that not even Google -- in its own language on its own library for its own RPC protocol -- can write thread-safe Go, so what chance does anyone else have. Maybe we have to stop thinking of Go as a language for mission critical parallel computing and think of it more like a Python 4 made for low-risk prototyping. Mature libraries help for that prototyping, you know how to put them together and get something working, that something just won't be scaleable, efficient, or thread-safe.

I first learned about this technique when gRPC generated code started using it. See the short readme and the long issue discussion. I think a lot more of the rationale from the discussion should have made it into the readme, since this is the only time most Go developers will ever see this technique used, especially since it can't be retrofitted to existing interfaces without breaking existing implementations.

I don't have any experience with goRPC (I'm assuming you're referring to https://github.com/valyala/gorpc), but just to note that that repo hasn't been updated in 7 years and has open issues that are that old, too. https://github.com/grpc/grpc-go has 17.5k stars and is actively maintained. That doesn't say anything about their relative performance - goRPC might be faster - but you probably won't have a fun time if you run into issues.

Found the root cause from https://github.com/grpc/grpc-go/commit/383b1143 (original issue: https://github.com/grpc/grpc-go/issues/75):

    // Note that ServeHTTP uses Go's HTTP/2 server implementation which is

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.