Introduction
In Part 4, we implemented the orderd service. In a distributed system, tracking what happens across multiple services is crucial. Logging provides insights into what occurred, while tracing helps follow a request's journey across services.
In this post, we will set up distributed tracing in our e-commerce system userd and orderd services using OpenTelemetry and Jaeger. This will help us visualize the flow of requests and debug performance bottlenecks.
Check when get order service is called how the trace is made to user service and db methods.
Why OpenTelemetry and Jaeger?
OpenTelemetry (OTel) is a vendor-neutral observability framework that provides standardized tools for metrics, logs, and traces. Jaeger, originally developed at Uber, is a distributed tracing tool that helps monitor and troubleshoot transactions in microservices-based systems.
Why not just logs?
- Logs capture discrete events but do not provide a structured way to see how a request moves across multiple services.
- Tracing provides an end-to-end view of a request and shows where delays occur.
Benefits of OpenTelemetry and Jaeger:
- ✅ End-to-end tracing: Tracks requests across microservices.
- ✅ Root cause analysis: Helps find performance bottlenecks.
- ✅ Open standard: Works with multiple backends like Jaeger, Zipkin, Prometheus, and Datadog.
- ✅ Automatic instrumentation: Hooks into gRPC, HTTP, and database calls without major code changes.
Implementing Tracing in userd
We update userd to send traces using OpenTelemetry.
Step 1: Configure OpenTelemetry in main.go
Check the exporter here, we will exporting trace to Jaeger endpoint that way it can collect all the traces.
ctx := context.Background()
exporter, err := otlptracegrpc.New(ctx, otlptracegrpc.WithInsecure(), otlptracegrpc.WithEndpoint("localhost:4317"))
if err != nil {
log.Fatalf("failed to create OTLP trace exporter: %v", err)
}
tracerProvider := trace.NewTracerProvider(
trace.WithBatcher(exporter),
trace.WithResource(resource.NewWithAttributes(
semconv.SchemaURL,
semconv.ServiceNameKey.String("userd"),
)),
)
otel.SetTracerProvider(tracerProvider)
Step 2: Add Tracing to gRPC Server
serverHandler := otelgrpc.NewServerHandler(
otelgrpc.WithTracerProvider(tracerProvider),
)
server := grpc.NewServer(
grpc.StatsHandler(serverHandler),
)
Step 3: Add Trace Spans in UserService methods
example, adding trace span to Register service in service/register.go
ctx, span := s.trace.Start(ctx, "Register")
defer span.End()
Step 4: Add trace to db calls.
In order to achieve this, we need to add plugin and call db methods with context
Updated db/db.go db provider
// New creates new database provider
// connects to db and returns the provider
func New(dbURL string) Provider {
db, err := gorm.Open(postgres.Open(dbURL), &gorm.Config{})
if err != nil {
log.Fatalf("Failed to connect to database: %v", err)
}
if err := db.Use(tracing.NewPlugin()); err != nil {
log.Fatalf("Failed to use tracing plugin: %v", err)
}
// Auto-migrate User model
db.AutoMigrate(&User{})
return &provider{db}
}
Implementing Tracing in orderd
orderd needs both server-side and client-side tracing since it calls userd.
Step 1,2,3,4 remains same as userd service.
Step 5: Add gRPC Client Tracing for userd
openTelemetryClientHandler := otelgrpc.NewClientHandler(
otelgrpc.WithTracerProvider(tracerProvider),
)
grpcConn, err := grpc.NewClient(
userServiceURL,
grpc.WithTransportCredentials(insecure.NewCredentials()),
grpc.WithStatsHandler(openTelemetryClientHandler),
)
userServiceClient := user.NewUserServiceClient(grpcConn)
Setting Up Jaeger
Step 1: Run Jaeger using Docker
docker run -d --name jaeger \
-e COLLECTOR_OTLP_ENABLED=true \
-p 16686:16686 \
-p 4317:4317 \
-p 4318:4318 \
jaegertracing/all-in-one:latest
Step 2: View Traces in Jaeger UI
Open http://localhost:16686 in your browser to visualize traces.
Code Links
For complete change log, pls check
Conclusion
We successfully added OpenTelemetry tracing to our userd and orderd services. By using Jaeger, we can visualize request flows, diagnose bottlenecks, and improve performance in our distributed system.
Next Steps:
Explore monitoring and metrics collection using Prometheus.
Stay tuned for the next post! 🚀
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