Pros of opentracing-go

• Lightweight and focused solely on tracing API
• Language-specific implementation of OpenTracing standard
• Easier to integrate into existing Go projects

Cons of opentracing-go

• Requires additional components for full distributed tracing functionality
• Less out-of-the-box features compared to Jaeger
• Maintenance has slowed down due to the transition to OpenTelemetry

Code Comparison

opentracing-go:

span := opentracing.StartSpan("operation_name")
defer span.Finish()

span.SetTag("key", "value")
span.LogFields(
    log.String("event", "soft error"),
    log.String("type", "cache timeout"),
    log.Int("waited.millis", 1500),
)

Jaeger:

tracer := cfg.NewTracer()
opentracing.SetGlobalTracer(tracer)

span := tracer.StartSpan("operation_name")
defer span.Finish()

span.SetTag("key", "value")
span.LogKV("event", "soft error", "type", "cache timeout", "waited.millis", 1500)

While opentracing-go provides a standardized API for distributed tracing in Go, Jaeger offers a more comprehensive solution with built-in tracing, collection, and visualization capabilities. opentracing-go is ideal for projects requiring a lightweight tracing API, while Jaeger is better suited for full-featured distributed tracing implementations.

Pros of OpenTelemetry-Go

• Vendor-neutral and standardized approach to observability
• Supports multiple backends and integrations
• More comprehensive, covering metrics, logs, and traces

Cons of OpenTelemetry-Go

• Steeper learning curve due to broader scope
• Less mature compared to Jaeger's established ecosystem
• May require additional configuration for full functionality

Code Comparison

Jaeger:

tracer, closer := jaeger.NewTracer(
    "service-name",
    jaeger.NewConstSampler(true),
    jaeger.NewInMemoryReporter(),
)
defer closer.Close()

OpenTelemetry-Go:

tp := sdktrace.NewTracerProvider(
    sdktrace.WithSampler(sdktrace.AlwaysSample()),
    sdktrace.WithBatcher(exporter),
)
defer tp.Shutdown(context.Background())
otel.SetTracerProvider(tp)

Both examples show basic tracer setup, but OpenTelemetry-Go offers more flexibility in configuration and backend choices. Jaeger's setup is simpler for quick start, while OpenTelemetry requires more initial configuration but provides greater extensibility.

OpenTelemetry-Go is part of a larger, standardized observability framework, making it a more future-proof choice for complex, multi-vendor environments. However, Jaeger remains a solid option for those seeking a dedicated, battle-tested tracing solution with a gentler learning curve.

Pros of APM Server

• Seamless integration with Elasticsearch and Kibana for powerful data analysis and visualization
• Built-in support for a wide range of programming languages and frameworks
• Offers real-time monitoring and alerting capabilities out of the box

Cons of APM Server

• Requires Elasticsearch as a backend, which may increase complexity and resource usage
• Less flexible in terms of storage options compared to Jaeger's multiple storage backends
• Steeper learning curve for users not familiar with the Elastic Stack ecosystem

Code Comparison

APM Server (Go):

func (p *processor) loadProcessors(config *Config) error {
    for _, processorConfig := range config.Processors {
        processor, err := processors.New(processorConfig)
        if err != nil {
            return err
        }
        p.processors = append(p.processors, processor)
    }
    return nil
}

Jaeger (Go):

func (b *Builder) CreateSpanReader() (spanstore.Reader, error) {
    spanReader, err := b.storageFactory.CreateSpanReader()
    if err != nil {
        return nil, err
    }
    return spanReader, nil
}

Both projects use Go and implement similar functionality for processing and storing spans, but APM Server's code is more focused on configuring processors, while Jaeger's code emphasizes creating span readers for different storage backends.

Pros of SkyWalking

• More comprehensive observability platform, including APM, metrics, and logging
• Supports a wider range of programming languages and frameworks
• Provides a built-in service mesh observability solution

Cons of SkyWalking

• Steeper learning curve due to more complex architecture
• Potentially higher resource consumption for full-stack deployment
• Less native cloud provider integrations compared to Jaeger

Code Comparison

SkyWalking agent configuration (Java):

-javaagent:/path/to/skywalking-agent.jar
-Dskywalking.agent.service_name=your-service-name
-Dskywalking.collector.backend_service=oap-server:11800

Jaeger client configuration (Java):

import io.jaegertracing.Configuration;

Configuration config = Configuration.fromEnv();
Tracer tracer = config.getTracer();

Both projects offer distributed tracing capabilities, but SkyWalking provides a more comprehensive observability solution with additional features like service mesh monitoring and metrics collection. Jaeger, on the other hand, focuses primarily on distributed tracing and offers simpler integration with cloud-native environments.

SkyWalking's agent-based approach allows for more detailed monitoring across various languages and frameworks, while Jaeger's client libraries provide a more lightweight solution for distributed tracing. The choice between the two depends on the specific requirements of your project and the desired level of observability.

Pros of Zipkin

• Simpler architecture and easier to set up for small to medium-scale deployments
• Better support for legacy systems and a wider range of programming languages
• More mature project with a longer history and established community

Cons of Zipkin

• Less scalable for very large distributed systems compared to Jaeger
• Fewer built-in features for advanced analysis and visualization
• Limited support for OpenTelemetry, which is becoming an industry standard

Code Comparison

Zipkin instrumentation example (Java):

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

Jaeger instrumentation example (Java):

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

Both Jaeger and Zipkin are popular distributed tracing systems used for monitoring and troubleshooting microservices architectures. While they share similar core functionality, they have distinct features and design philosophies that make them suitable for different use cases and environments.

Zipkin is often preferred for its simplicity and ease of integration with legacy systems, while Jaeger excels in large-scale deployments and offers more advanced features. The choice between the two depends on factors such as the scale of the system, existing infrastructure, and specific requirements for analysis and visualization.