opentelemetry-specification VS pino

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.

Fastify provides a built-in logging mechanism based on Pino that allows you to capture various events in your applications. Once enabled, Fastify logs all incoming requests to the server and errors that occur while processing said requests. It also provides a convenient way to log custom messages through the log() method on the Fastify instance or the request object.

1. pino

Have a look at https://github.com/pinojs/pino

NodeJS -> Pino, Winston, Bunyan, Npmlog, e.t.c.

Using pino as a logger, for every request on the _server_ , a unique ID generated client side in the headers, so a log may be something like:

pino (logger)

Pino is also a popular Node.js logging library alternative for developers. There are claims that it is 5x faster than other Node.js logging libraries besides providing standard features such as the ability to choose a storage medium, log levels, and formatting capabilities. Pino is also highly extensible, flexible, and easy to integrate with Node.js frameworks such as Express, Fastify, and Restify. The library is a low-overhead Node.js logging library and supports asynchronous logging despite being fast. Pino also gives you the ability to create child loggers.

Logging: logs are extremely important but are costly; we chose the best logger to almost remove this cost, Pino!

Links: [1] Parseable Github: https://github.com/parseablehq/parseable [2] Parseable Docs: https://www.parseable.io/docs/introduction [3] Pino Github: https://github.com/pinojs/pino [4] Pino Docs: https://getpino.io/

If you want a structured logger, pino is nice.