opentelemetry-specification VS RabbitMQ

OpenTelemetry is a standard for implementing telemetry in your applications. It provides a specification, containing the requirements that all implementations should follow as well as some implementations for major languages, including an API and a SDK to interact with it.

At this point, we are about to do the real work where we have to configure OpenTelemetry and export telemetry data to New Relic. Exporting this kind of data relies on a specific protocol; the OpenTelemetry Protocol or OTLP.

OpenTelemetry provides instrumentation libraries for your application. The development of these libraries is guided by the OpenTelemetry specification. The OpenTelemetry specification describes the cross-language requirements and design expectations for all OpenTelemetry implementations in various programming languages.

It follows a specification-driven development. The OpenTelemetry specification has design and implementation guidelines for how the instrumentation libraries should be implemented. In addition, it provides client libraries in all the major programming languages which follow the specification.

Today we learned how to manually configure OpenTelemetry for Python to connect to Lightstep (this also works for any Observability back-end that ingests the OTLP format). We also learned how to link related services together through manual context propagation.

According to the opentelemetry specification for messaging systems, When a process receives messages in a batch it is impossible for this process to determine the parent span for the span that it is currently creating.

That's why you have otel logging: https://github.com/open-telemetry/opentelemetry-specificatio...

OTLP protocol for shipping telemetry data

Lightstep ingests data in native OpenTelemetry Protocol (OTLP) format, so we will use the OTLP Exporter. The exporter can be called either otlp or follow the naming format otlp/. We could call it otlp/bob if we wanted to. We're calling our exporter otlp/ls to signal to us that we are using the OTLP exporter to send the data to Lightstep.

Lightstep Observability supports the native OpenTelemetry Protocol (OTLP). It can receive data in the OTLP format either via HTTP or gRPC. You will need to specify which method you wish to use in your code, as we’ll see in the upcoming code snippets.

You're better off with something really, really straightforward, like AWS Simple Queue Service or SQS or RabbitMQ, which are two very popular message queues. (The "MQ" part means Message Queue, in case that wasn't obvious). There are a lot of these out there, including ActiveMQ, IronMQ, and others. Pick the one that makes the most sense to you.

RabbitMQ is an open-source message broker for asynchronous messaging, queueing and delivery acknowledgement. It can be deployed in the cloud or on-premise with Kubernetes, Chef, Docker or Puppet.

Oh there goes rabbit!

Starting with the first alpha release - 2.0.0alpha1 -, Starlite includes a simple event bus that can be used to emit and receive events, supporting both synchronous and asynchronous listeners. Currently only a basic in-memory, per-process backend is included, but future versions will add support for inter-process communication by adding backends for Redis, RabbitMQ and others.

What people really want is to design the logic of an app independently from the component hierarchy. That means you need to store state somewhere other than the components and you need to dispatch events that are not attached to the component hierarchy. Also, a one way data flow has well known benefits as described by things like CQRS, RabbitMQ, and MediatR.

RabbitMQ (*https://www.rabbitmq.com*)

OpenTalk is a young project for creating online meeting software similar to Jitsi or BigBlueButton. It is a completely new development, and while it is not a fork of an existing open-source project, it integrates with other projects such as the Janus WebRTC server, Redis for volatile state, RabbitMQ for communication between server instances, and PostreSQL for persistent state.

Do you apply Apache Kafka or RabbitMQ in your software project? If so, then you definitely have some message schemas. Have you ever encountered in backward compatibility issue? An accidental message format changing and your entire system is no longer functioning? I bet you have such an unpleasant experience at least once in your career.

Suppose we develop the service that consumes data from one input (e.g. Apache Kafka, RabbitMQ, etc.), deduplicates messages, and produces the result to some output. Look at the diagram below that describes the process.