stanza VS opentracing-javascript

Been a while since I looked into fluentd and elasticsearch but I heard Stanza (soon to be moved to the OpenTelemetry Collector) is meant to be a modern replacement for it, but seems it's still in it's early days.

And then they contacted some famous log collection projects such as the Fluent Bit and also others like Stanza as well as Syslog, I guess. And I didn't read the whole thread of the conversation around log collection. But now, they set the Stanza as the first implementation of OpenTelemetry logs. And in Stanza, the observIQ with Stanza is merged under the OpenTelemetry log repository, so that's the status. And then, they try to standardize the format of logs based on the Stanza format, such as what kind of information should be included in log.

observIQ is a hosted log management platform that's powered by our FOSS log agent, Stanza: https://github.com/observIQ/stanza. Stanza is lightweight and robust (written in Go), and can be configured to act as a Syslog receiver in about a minute (guided configuration in our app).

observIQ is a simple, cost-effective hosted log management platform designed with small teams in-mind. Log collection is powered by Stanza: https://github.com/observIQ/stanza , our high-performance OSS log agent. Stanza was also recently adopted as the official log agent of the CNCF's OpenTelemetry project. observIQ offers guided-installation, pre-made dashboards, threshold-based alerts, live tail and more.

We're a new hosted log management platform with simple, native support for gathering logs from Openshift. Deployment typically takes a few minutes. We also automatically enrich Openshift logs metadata like node, namespace, pod, container - no additional configuration required. Logs are gathered with your high-performance OSS log agent, Stanza: https://github.com/observIQ/stanza

We offer a super simple, hosted log management platform that allows you to easily ingest any syslog traffic by setting up high-speed OSS agent as a syslog receiver in just a few click (setup takes about 30 seconds). You can find more about our log agent here: https://github.com/observIQ/stanza

Self-promotion alert, my apologies: Check us out at observIQ: https://observiq.com/ We provide a super-simple host log management platform powered our high-performance OSS Log Agent, Stanza (https://github.com/observIQ/stanza). Average setup time is about 5 minutes, end-to-end. Our platform is designed with start-ups and small businesses in-mind and are priced about half as much as our competitors. Lastly, we offer 14-day free trial, but also offer a completely free plan that provides 3 gigs of ingestion/day and 3 day retention - with access to the full feature set of the platform (Dashboards, Alerts, Live Tail). Shoot me a message or chat if you have any questions!

Similarly, our platform will be rolling out this feature soon, and will utilize our lightweight FOSS log agent Stanza - written in Go. The agent may work for your project https://github.com/observIQ/stanza

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.