logstash-logback-encoder VS opentelemetry-go

I use the logstash json format.

We're using https://github.com/logfellow/logstash-logback-encoder with logback (on Play Framework, but should work fine on Lambda as well).

For those interested in the details, I've created an example implementation based on Spring-flavoured REST and Logbook+logstash-logback-encoder within my own json-log-filter project for PoC / reference.

what's the difference to https://github.com/logfellow/logstash-logback-encoder ??

Don't use filebeat. Filebeat is for systems that you cannot change logging for. Push logs directly to logstash via logstash appender. Since I'm mainly logback user, there's one directly by logstash at https://github.com/logstash/logstash-logback-encoder. Quick search indicates that there's https://github.com/viskan/logstash-appender/ for log4j also and it seems it also supports MDC abuse as indicated by https://github.com/viskan/logstash-appender/blob/master/src/main/java/com/viskan/log4j/logstash/appender/LogstashAppender.java#L256. By abusing the MDC you won't need to write a processing pattern in logstash to extract metadata from giant blob line as each key in MDC will get assigned additional value, making your records in elastic search more useful.

You can use the Logstash Logback encooder. You mentioned Elk, so there must be a Logstash running somewhere you can connect to with this appender

We need to add traceId and spanId values to the application log. In production we would use the logstash-logback-encoder to generate logs in JSON format and send them to an ELK but for the demo we use this plain text logback layout:

Our first approach was to implement a separate SDK for each independent technology stack. We decided to use OpenTelemetry which is widely adopted and covers most of our needs.

Distributed system administrators need mechanisms and tools for monitoring individual nodes in order to analyze the system and promptly detect anomalies. Developers also need effective mechanisms for analyzing, diagnosing issues, and identifying bugs in protocol implementations. Logging, tracing, and collecting metrics are common observability techniques to allow monitoring and obtaining diagnostic information from the system; most of the explored code bases use these techniques. OpenTelemetry and Prometheus are popular open-source monitoring solutions, which are used in many of the explored code bases.

OpenTelemetry

When choosing distributed tracing tools, considerations include your technology stack, business requirements, and monitoring complexity. Zipkin, SkyWalking, and OpenTelemetry are popular distributed tracing solutions, each with its unique features.

You can follow this process with any large token AI system like Claude by identifying tracing data relevant to the code you are working on, using it as context to prompt OpenAI or other LLMs. Generally, you’d generate tracing data by implementing OpenTelemetry (aka OTEL) libraries into your application, adding spans to your functions with Jaeger, or using commercial SaaS tools like Honeycomb and Datadog.

While many programming languages provide robust support for Open Telemetry, this instance focuses on Golang. It's important to note that, in the current context, the logs SDK for Golang is not implemented. For future reference consult the list of supported languages and explore the Open Telemetry repositories. Always prioritize the main repository and its contrib repository, housing extensions and instrumentation libraries crucial to the Open Telemetry framework. Stay updated with the latest developments to ensure seamless integration and enhanced functionality.

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NEW UPDATES (since we last posted to HN): We now support OpenTelemetry (https://opentelemetry.io/) natively which will help you to monitor, observe and debug any app, service, database or stack.

OpenTelemetry is the fastest growing Cloud Native Computing Foundation (CNCF) project. It standardizes the instrumentation and collection of traces, metrics, and logs from applications, and is supported by all the major observability projects, languages, and tools. One standard to rule them all!

OpenTelemetry is a collection of tools, APIs, and SDKs. Use it to instrument, generate, collect, and export telemetry data (metrics, logs, and traces) to help you analyze your software’s performance and behavior. https://opentelemetry.io/

Tracetest uses your existing OpenTelemetry traces to power trace-based testing with assertions against your trace data at every point of the request transaction. You only need to point Tracetest to your existing trace data source, or send traces to Tracetest directly!