tempo

Pros of Jaeger

• More mature project with a larger community and ecosystem
• Supports multiple storage backends out-of-the-box
• Offers a rich set of features for trace analysis and visualization

Cons of Jaeger

• Can be complex to set up and maintain in large-scale environments
• May require more resources for deployment and operation
• Limited scalability for high-volume tracing scenarios

Code Comparison

Jaeger (Go):

func (s *spanStore) WriteSpan(span *model.Span) error {
    dbSpan := dbmodel.FromDomain(span)
    return s.client.Insert(dbSpan)
}

Tempo (Go):

func (r *Reader) FindTraceByID(ctx context.Context, traceID []byte) (*tempopb.Trace, error) {
    return r.reader.FindTraceByID(ctx, traceID)
}

Both projects use Go and have similar code structures for core functionality. Jaeger's code tends to be more complex due to its broader feature set, while Tempo's code is often more focused on specific distributed tracing tasks.

Tempo is designed for high scalability and cost-effectiveness, particularly when used with object storage. It integrates well with other Grafana observability tools but has a more limited feature set compared to Jaeger. Jaeger offers more flexibility in deployment options and analysis capabilities but may require more resources and management overhead.

Pros of OpenTelemetry Collector

• More versatile, supporting multiple telemetry data types (traces, metrics, logs)
• Vendor-agnostic, allowing integration with various backends and tools
• Highly extensible with a plugin architecture for custom components

Cons of OpenTelemetry Collector

• More complex setup and configuration due to its versatility
• Potentially higher resource usage when handling multiple data types

Code Comparison

OpenTelemetry Collector configuration (YAML):

receivers:
  otlp:
    protocols:
      grpc:
      http:

processors:
  batch:

exporters:
  otlp:
    endpoint: "backend:4317"

service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [batch]
      exporters: [otlp]

Tempo configuration (YAML):

server:
  http_listen_port: 3200

distributor:
  receivers:
    otlp:
      protocols:
        grpc:
        http:

storage:
  trace:
    backend: local
    local:
      path: /tmp/tempo/blocks

While OpenTelemetry Collector offers a more flexible configuration for various telemetry data types and processing pipelines, Tempo's configuration is more focused on trace data ingestion and storage. OpenTelemetry Collector allows for more customization in data processing and exporting, whereas Tempo is optimized for trace data handling within the Grafana ecosystem.

Pros of SigNoz

• All-in-one observability platform (traces, metrics, logs)
• User-friendly UI with built-in dashboards and alerts
• Self-hosted option for data privacy and control

Cons of SigNoz

• Less mature project compared to Tempo
• Smaller community and ecosystem
• Limited integrations with other observability tools

Code Comparison

SigNoz (Go):

func (s *SignozServer) Start() error {
    s.initializeComponents()
    s.setupRoutes()
    return s.server.ListenAndServe()
}

Tempo (Go):

func (s *Server) Start() error {
    s.registerModules()
    s.initializeStorage()
    return s.httpServer.ListenAndServe()
}

Both projects use Go and have similar server initialization patterns. However, SigNoz focuses on a more comprehensive observability solution, while Tempo specializes in distributed tracing.

SigNoz offers a more integrated approach with its all-in-one platform, making it easier for users to set up and manage their observability stack. On the other hand, Tempo benefits from Grafana's extensive ecosystem and integration capabilities.

Tempo's specialization in distributed tracing may provide better performance and scalability for large-scale tracing use cases. SigNoz's broader feature set could be advantageous for smaller teams or projects requiring a quick, all-encompassing observability solution.

Pros of SkyWalking

• More comprehensive observability platform, including APM, metrics, and logging
• Supports multiple languages and frameworks out-of-the-box
• Provides a built-in UI for visualization and analysis

Cons of SkyWalking

• Higher complexity and resource usage due to its all-in-one nature
• Steeper learning curve for setup and configuration
• Less focus on distributed tracing compared to Tempo

Code Comparison

SkyWalking (Java agent configuration):

agent.service_name=${SW_AGENT_NAME:Your_ApplicationName}
collector.backend_service=${SW_AGENT_COLLECTOR_BACKEND_SERVICES:127.0.0.1:11800}

Tempo (Docker Compose snippet):

tempo:
  image: grafana/tempo:latest
  command: ["-config.file=/etc/tempo.yaml"]
  volumes:
    - ./tempo.yaml:/etc/tempo.yaml

Both projects aim to provide observability solutions, but they differ in scope and focus. SkyWalking offers a more comprehensive platform with built-in visualization, while Tempo specializes in distributed tracing and integrates well with other Grafana ecosystem tools. The choice between them depends on specific project requirements and existing infrastructure.

Pros of APM Server

• Integrated with Elastic Stack, providing seamless data analysis and visualization
• Supports a wide range of programming languages and frameworks
• Offers detailed performance metrics and error tracking

Cons of APM Server

• Requires Elasticsearch for data storage, which can be resource-intensive
• Less focus on distributed tracing compared to Tempo
• Steeper learning curve for users not familiar with the Elastic ecosystem

Code Comparison

APM Server (Go):

func (p *Processor) ProcessTransformable(t transform.Transformable) []beat.Event {
    var events []beat.Event
    for _, transformable := range t.Transform() {
        events = append(events, p.createEvent(transformable))
    }
    return events
}

Tempo (Go):

func (i *instance) PushBytes(ctx context.Context, req *tempopb.PushBytesRequest) (*tempopb.PushResponse, error) {
    for _, b := range req.Traces {
        trace, err := i.decoder.PrepareTrace(b)
        if err != nil {
            return nil, err
        }
        i.pushTraceToWal(trace)
    }
    return &tempopb.PushResponse{}, nil
}

Both repositories use Go and focus on processing and storing trace data. APM Server's code snippet shows event transformation, while Tempo's code demonstrates trace decoding and storage in a Write-Ahead Log (WAL).