Pros of OpenTracing

• Simpler API with a focus on distributed tracing
• Easier to get started for developers new to observability
• More mature and stable, with a longer history of production use

Cons of OpenTracing

• Limited scope compared to OpenTelemetry's comprehensive observability approach
• Smaller ecosystem and fewer integrations
• No longer actively developed, as efforts have shifted to OpenTelemetry

Code Comparison

OpenTracing:

Tracer tracer = GlobalTracer.get();
Span span = tracer.buildSpan("operation").start();
span.setTag("key", "value");
span.finish();

OpenTelemetry:

Tracer tracer = GlobalOpenTelemetry.getTracer("instrumentation-library-name");
Span span = tracer.spanBuilder("operation").startSpan();
span.setAttribute("key", "value");
span.end();

The code snippets show similar functionality, but OpenTelemetry uses slightly different method names and a more structured approach to creating tracers. OpenTelemetry also provides additional features like metrics and logs, which are not shown in this basic example.

OpenTracing is simpler and more focused on tracing, while OpenTelemetry offers a more comprehensive observability solution with a slightly steeper learning curve. However, OpenTelemetry is the future of observability, combining the best aspects of OpenTracing and OpenCensus.

Pros of Zipkin

• Mature project with a longer history and established user base
• Simpler architecture, potentially easier to set up and maintain
• Built-in web UI for visualizing and analyzing traces

Cons of Zipkin

• Less flexible and extensible compared to OpenTelemetry
• Limited support for non-Java languages and frameworks
• Smaller ecosystem and fewer integrations with other observability tools

Code Comparison

Zipkin instrumentation example:

Span span = tracer.newTrace().name("encode").start();
try {
  doSomethingExpensive();
} finally {
  span.finish();
}

OpenTelemetry instrumentation example:

Span span = tracer.spanBuilder("encode").startSpan();
try (Scope scope = span.makeCurrent()) {
  doSomethingExpensive();
} finally {
  span.end();
}

Both examples show how to create and manage spans, but OpenTelemetry uses a more explicit scope management approach. Zipkin's API is slightly simpler, while OpenTelemetry offers more control over the span's lifecycle and context propagation.

OpenTelemetry provides a more comprehensive observability solution with support for metrics and logs in addition to traces, making it a more versatile choice for modern distributed systems. However, Zipkin may be preferred for simpler Java-centric projects or those already invested in the Zipkin ecosystem.

Pros of client_java

• Mature and well-established project with a long history in the Prometheus ecosystem
• Simpler implementation for basic metric collection and export
• Tighter integration with Prometheus-specific features and best practices

Cons of client_java

• Limited to Prometheus-specific metrics and data model
• Less flexibility for supporting multiple observability backends
• Narrower scope compared to OpenTelemetry's comprehensive approach

Code Comparison

client_java:

Counter requests = Counter.build()
    .name("requests_total")
    .help("Total requests.")
    .register();

requests.inc();

opentelemetry-java:

Meter meter = GlobalMeterProvider.getMeter("instrumentation-library-name");
LongCounter counter = meter
    .counterBuilder("requests_total")
    .setDescription("Total requests.")
    .build();

counter.add(1);

Summary

While client_java offers a straightforward implementation for Prometheus-specific metrics, opentelemetry-java provides a more versatile and standardized approach to observability. OpenTelemetry supports multiple backends and offers a wider range of telemetry data types, making it more suitable for complex, multi-vendor environments. However, client_java may be preferable for projects deeply integrated with the Prometheus ecosystem or those requiring simpler metric collection.