Welcome to the fifth installment of our series on implementing a sophisticated order processing system! In our previous posts, we’ve covered everything from setting up the basic architecture to implementing advanced workflows and comprehensive monitoring. Today, we’re diving into the world of distributed tracing and logging, two crucial components for maintaining observability in a microservices architecture.
In a microservices architecture, a single user request often spans multiple services. This distributed nature makes it challenging to understand the flow of requests and to diagnose issues when they arise. Distributed tracing and centralized logging address these challenges by providing:
To implement distributed tracing and logging, we’ll be using two powerful toolsets:
OpenTelemetry : An observability framework for cloud-native software that provides a single set of APIs, libraries, agents, and collector services to capture distributed traces and metrics from your application.
ELK Stack : A collection of three open-source products - Elasticsearch, Logstash, and Kibana - from Elastic, which together provide a robust platform for log ingestion, storage, and visualization.
By the end of this post, you’ll be able to:
Let’s dive in!
Before we start implementing, let’s review some key concepts that will be crucial for our distributed tracing and logging setup.
Distributed tracing is a method of tracking a request as it flows through various services in a distributed system. It provides a way to understand the full lifecycle of a request, including:
A trace typically consists of one or more spans. A span represents a unit of work or operation. It tracks specific operations that a request makes, recording when the operation started and ended, as well as other data.
OpenTelemetry is an observability framework for cloud-native software. It provides a single set of APIs, libraries, agents, and collector services to capture distributed traces and metrics from your application. Key components include:
Effective logging in distributed systems requires careful consideration:
The ELK stack is a popular choice for log management:
Log aggregation involves collecting log data from various sources and storing it in a centralized location. This allows for:
Log analysis involves extracting meaningful insights from log data, which can include:
With these concepts in mind, let’s move on to implementing distributed tracing in our order processing system.
Let’s start by implementing distributed tracing in our order processing system using OpenTelemetry.
First, we need to add OpenTelemetry to our Go services. Add the following dependencies to your go.mod file:
require (
go.opentelemetry.io/otel v1.7.0
go.opentelemetry.io/otel/exporters/jaeger v1.7.0
go.opentelemetry.io/otel/sdk v1.7.0
go.opentelemetry.io/otel/trace v1.7.0
)
Next, let’s set up a tracer provider in our main function:
package main
import (
"log"
"go.opentelemetry.io/otel"
"go.opentelemetry.io/otel/attribute"
"go.opentelemetry.io/otel/exporters/jaeger"
"go.opentelemetry.io/otel/sdk/resource"
tracesdk "go.opentelemetry.io/otel/sdk/trace"
semconv "go.opentelemetry.io/otel/semconv/v1.4.0"
)
func initTracer() func() {
exporter, err := jaeger.New(jaeger.WithCollectorEndpoint(jaeger.WithEndpoint("http://jaeger:14268/api/traces")))
if err != nil {
log.Fatal(err)
}
tp := tracesdk.NewTracerProvider(
tracesdk.WithBatcher(exporter),
tracesdk.WithResource(resource.NewWithAttributes(
semconv.SchemaURL,
semconv.ServiceNameKey.String("order-processing-service"),
attribute.String("environment", "production"),
)),
)
otel.SetTracerProvider(tp)
return func() {
if err := tp.Shutdown(context.Background()); err != nil {
log.Printf("Error shutting down tracer provider: %v", err)
}
}
}
func main() {
cleanup := initTracer()
defer cleanup()
// Rest of your main function...
}
This sets up a tracer provider that exports traces to Jaeger, a popular distributed tracing backend.
Now, let’s add tracing to our order processing workflow. We’ll start with the CreateOrder function:
import (
"context"
"go.opentelemetry.io/otel"
"go.opentelemetry.io/otel/attribute"
"go.opentelemetry.io/otel/trace"
)
func CreateOrder(ctx context.Context, order Order) error {
tr := otel.Tracer("order-processing")
ctx, span := tr.Start(ctx, "CreateOrder")
defer span.End()
span.SetAttributes(attribute.Int64("order.id", order.ID))
span.SetAttributes(attribute.Float64("order.total", order.Total))
// Validate order
if err := validateOrder(ctx, order); err != nil {
span.RecordError(err)
span.SetStatus(codes.Error, "Order validation failed")
return err
}
// Process payment
if err := processPayment(ctx, order); err != nil {
span.RecordError(err)
span.SetStatus(codes.Error, "Payment processing failed")
return err
}
// Update inventory
if err := updateInventory(ctx, order); err != nil {
span.RecordError(err)
span.SetStatus(codes.Error, "Inventory update failed")
return err
}
span.SetStatus(codes.Ok, "Order created successfully")
return nil
}
This creates a new span for the CreateOrder function and adds relevant attributes. It also creates child spans for each major step in the process.
When making calls to other services, we need to propagate the trace context. Here’s an example of how to do this with an HTTP client:
import (
"net/http"
"go.opentelemetry.io/contrib/instrumentation/net/http/otelhttp"
)
func callExternalService(ctx context.Context, url string) error {
client := http.Client{Transport: otelhttp.NewTransport(http.DefaultTransport)}
req, err := http.NewRequestWithContext(ctx, "GET", url, nil)
if err != nil {
return err
}
_, err = client.Do(req)
return err
}
This uses the otelhttp package to automatically propagate trace context in HTTP headers.
For asynchronous operations, we need to ensure we’re passing the trace context correctly. Here’s an example using a worker pool:
func processOrderAsync(ctx context.Context, order Order) {
tr := otel.Tracer("order-processing")
ctx, span := tr.Start(ctx, "processOrderAsync")
defer span.End()
workerPool <- func() {
processCtx := trace.ContextWithSpan(context.Background(), span)
if err := processOrder(processCtx, order); err != nil {
span.RecordError(err)
span.SetStatus(codes.Error, "Async order processing failed")
} else {
span.SetStatus(codes.Ok, "Async order processing succeeded")
}
}
}
This creates a new span for the async operation and passes it to the worker function.
To integrate OpenTelemetry with Temporal workflows, we can use the go.opentelemetry.io/contrib/instrumentation/go.temporal.io/temporal/oteltemporalgrpc package:
import (
"go.temporal.io/sdk/client"
"go.temporal.io/sdk/worker"
"go.opentelemetry.io/contrib/instrumentation/go.temporal.io/temporal/oteltemporalgrpc"
)
func initTemporalClient() (client.Client, error) {
return client.NewClient(client.Options{
HostPort: "temporal:7233",
ConnectionOptions: client.ConnectionOptions{
DialOptions: []grpc.DialOption{
grpc.WithUnaryInterceptor(oteltemporalgrpc.UnaryClientInterceptor()),
grpc.WithStreamInterceptor(oteltemporalgrpc.StreamClientInterceptor()),
},
},
})
}
func initTemporalWorker(c client.Client, taskQueue string) worker.Worker {
w := worker.New(c, taskQueue, worker.Options{
WorkerInterceptors: []worker.WorkerInterceptor{
oteltemporalgrpc.WorkerInterceptor(),
},
})
return w
}
This sets up Temporal clients and workers with OpenTelemetry instrumentation.
We’ve already set up Jaeger as our trace backend in the initTracer function. To visualize our traces, we need to add Jaeger to our docker-compose.yml:
services:
# ... other services ...
jaeger:
image: jaegertracing/all-in-one:1.35
ports:
- "16686:16686"
- "14268:14268"
environment:
- COLLECTOR_OTLP_ENABLED=true
Now you can access the Jaeger UI at http://localhost:16686 to view and analyze your traces.
In the next section, we’ll set up centralized logging using the ELK stack to complement our distributed tracing setup.
Now that we have distributed tracing in place, let’s set up centralized logging using the ELK (Elasticsearch, Logstash, Kibana) stack.
First, let’s add Elasticsearch to our docker-compose.yml:
services:
# ... other services ...
elasticsearch:
image: docker.elastic.co/elasticsearch/elasticsearch:7.14.0
environment:
- discovery.type=single-node
- "ES_JAVA_OPTS=-Xms512m -Xmx512m"
ports:
- "9200:9200"
volumes:
- elasticsearch_data:/usr/share/elasticsearch/data
volumes:
elasticsearch_data:
driver: local
This sets up a single-node Elasticsearch instance for development purposes.
Next, let’s add Logstash to our docker-compose.yml:
services:
# ... other services ...
logstash:
image: docker.elastic.co/logstash/logstash:7.14.0
volumes:
- ./logstash/pipeline:/usr/share/logstash/pipeline
ports:
- "5000:5000/tcp"
- "5000:5000/udp"
- "9600:9600"
depends_on:
- elasticsearch
Create a Logstash pipeline configuration file at ./logstash/pipeline/logstash.conf:
input {
tcp {
port => 5000
codec => json
}
}
filter {
if [trace_id] {
mutate {
add_field => { "[@metadata][trace_id]" => "%{trace_id}" }
}
}
}
output {
elasticsearch {
hosts => ["elasticsearch:9200"]
index => "order-processing-logs-%{+YYYY.MM.dd}"
}
}
This configuration sets up Logstash to receive JSON logs over TCP, process them, and forward them to Elasticsearch.
Now, let’s add Kibana to our docker-compose.yml:
services:
# ... other services ...
kibana:
image: docker.elastic.co/kibana/kibana:7.14.0
ports:
- "5601:5601"
environment:
ELASTICSEARCH_URL: http://elasticsearch:9200
ELASTICSEARCH_HOSTS: '["http://elasticsearch:9200"]'
depends_on:
- elasticsearch
You can access the Kibana UI at http://localhost:5601 once it’s up and running.
To send structured logs to Logstash, we’ll use the logrus library. First, add it to your go.mod:
go get github.com/sirupsen/logrus
Now, let’s set up a logger in our main function:
import (
"github.com/sirupsen/logrus"
"gopkg.in/sohlich/elogrus.v7"
)
func initLogger() *logrus.Logger {
log := logrus.New()
log.SetFormatter(&logrus.JSONFormatter{})
hook, err := elogrus.NewElasticHook("elasticsearch:9200", "warning", "order-processing-logs")
if err != nil {
log.Fatalf("Failed to create Elasticsearch hook: %v", err)
}
log.AddHook(hook)
return log
}
func main() {
log := initLogger()
// Rest of your main function...
}
This sets up a JSON formatter for our logs and adds an Elasticsearch hook to send logs directly to Elasticsearch.
Now, let’s update our CreateOrder function to use structured logging:
func CreateOrder(ctx context.Context, order Order) error {
tr := otel.Tracer("order-processing")
ctx, span := tr.Start(ctx, "CreateOrder")
defer span.End()
logger := logrus.WithFields(logrus.Fields{
"order_id": order.ID,
"trace_id": span.SpanContext().TraceID().String(),
})
logger.Info("Starting order creation")
// Validate order
if err := validateOrder(ctx, order); err != nil {
logger.WithError(err).Error("Order validation failed")
span.RecordError(err)
span.SetStatus(codes.Error, "Order validation failed")
return err
}
// Process payment
if err := processPayment(ctx, order); err != nil {
logger.WithError(err).Error("Payment processing failed")
span.RecordError(err)
span.SetStatus(codes.Error, "Payment processing failed")
return err
}
// Update inventory
if err := updateInventory(ctx, order); err != nil {
logger.WithError(err).Error("Inventory update failed")
span.RecordError(err)
span.SetStatus(codes.Error, "Inventory update failed")
return err
}
logger.Info("Order created successfully")
span.SetStatus(codes.Ok, "Order created successfully")
return nil
}
This code logs each step of the order creation process, including any errors that occur. It also includes the trace ID in each log entry, which will be crucial for correlating logs with traces.
Now that we have both distributed tracing and centralized logging set up, let’s explore how to correlate this information for a unified view of system behavior.
We’ve already included the trace ID in our log entries. To make this correlation even more powerful, we can add a custom field to our spans that includes the log index:
span.SetAttributes(attribute.String("log.index", "order-processing-logs-"+time.Now().Format("2006.01.02")))
This allows us to easily jump from a span in Jaeger to the corresponding logs in Kibana.
We’ve already added trace IDs to our log entries in the previous section. This allows us to search for all log entries related to a particular trace in Kibana.
To link our Prometheus metrics to traces, we can use exemplars. Here’s an example of how to do this:
import (
"github.com/prometheus/client_golang/prometheus"
"github.com/prometheus/client_golang/prometheus/promauto"
"go.opentelemetry.io/otel/trace"
)
var (
orderProcessingDuration = promauto.NewHistogramVec(
prometheus.HistogramOpts{
Name: "order_processing_duration_seconds",
Help: "Duration of order processing in seconds",
Buckets: prometheus.DefBuckets,
},
[]string{"status"},
)
)
func CreateOrder(ctx context.Context, order Order) error {
// ... existing code ...
start := time.Now()
// ... process order ...
duration := time.Since(start)
orderProcessingDuration.WithLabelValues("success").Observe(duration.Seconds(), prometheus.Labels{
"trace_id": span.SpanContext().TraceID().String(),
})
// ... rest of the function ...
}
This adds the trace ID as an exemplar to our order processing duration metric.
With logs, traces, and metrics all correlated, we can create a unified view of our system’s behavior:
This allows you to seamlessly navigate between metrics, traces, and logs, providing a comprehensive view of your system’s behavior and making it easier to debug issues.
With our logs centralized in Elasticsearch, let’s explore some strategies for effective log aggregation and analysis.
For high-volume services, logging every event can be prohibitively expensive. Implement log sampling to reduce the volume while still maintaining visibility:
func shouldLog() bool {
return rand.Float32() < 0.1 // Log 10% of events
}
func CreateOrder(ctx context.Context, order Order) error {
// ... existing code ...
if shouldLog() {
logger.Info("Order created successfully")
}
// ... rest of the function ...
}
In Kibana, create dashboards that provide insights into your system’s behavior. Some useful visualizations might include:
Use Kibana’s alerting features to set up alerts based on log patterns. For example:
Elasticsearch provides machine learning capabilities that can be used for anomaly detection in logs. You can set up machine learning jobs in Kibana to detect:
These machine learning insights can help you identify issues before they become critical problems.
In the next sections, we’ll cover best practices for logging in a microservices architecture and explore some advanced OpenTelemetry techniques.
When implementing logging in a microservices architecture, there are several best practices to keep in mind to ensure your logs are useful, manageable, and secure.
Consistency in log formats across all your services is crucial for effective log analysis. In our Go services, we can create a custom logger that enforces a standard format:
import (
"github.com/sirupsen/logrus"
)
type StandardLogger struct {
*logrus.Logger
ServiceName string
}
func NewStandardLogger(serviceName string) *StandardLogger {
logger := logrus.New()
logger.SetFormatter(&logrus.JSONFormatter{
FieldMap: logrus.FieldMap{
logrus.FieldKeyTime: "timestamp",
logrus.FieldKeyLevel: "severity",
logrus.FieldKeyMsg: "message",
},
})
return &StandardLogger{
Logger: logger,
ServiceName: serviceName,
}
}
func (l *StandardLogger) WithFields(fields logrus.Fields) *logrus.Entry {
return l.Logger.WithFields(logrus.Fields{
"service": l.ServiceName,
}).WithFields(fields)
}
This logger ensures that all log entries include a “service” field and use consistent field names.
Contextual logging involves including relevant context with each log entry. In a microservices architecture, this often means including a request ID or trace ID that can be used to correlate logs across services:
func CreateOrder(ctx context.Context, logger *StandardLogger, order Order) error {
tr := otel.Tracer("order-processing")
ctx, span := tr.Start(ctx, "CreateOrder")
defer span.End()
logger := logger.WithFields(logrus.Fields{
"order_id": order.ID,
"trace_id": span.SpanContext().TraceID().String(),
})
logger.Info("Starting order creation")
// ... rest of the function ...
}
It’s crucial to ensure that sensitive information, such as personal data or credentials, is not logged. You can create a custom log hook to redact sensitive information:
type SensitiveDataHook struct{}
func (h *SensitiveDataHook) Levels() []logrus.Level {
return logrus.AllLevels
}
func (h *SensitiveDataHook) Fire(entry *logrus.Entry) error {
if entry.Data["credit_card"] != nil {
entry.Data["credit_card"] = "REDACTED"
}
return nil
}
// In your main function:
logger.AddHook(&SensitiveDataHook{})
In a production environment, you need to manage log retention and rotation to control storage costs and comply with data retention policies. While Elasticsearch can handle this to some extent, you might also want to implement log rotation at the application level:
import (
"gopkg.in/natefinch/lumberjack.v2"
)
func initLogger() *logrus.Logger {
logger := logrus.New()
logger.SetOutput(&lumberjack.Logger{
Filename: "/var/log/myapp.log",
MaxSize: 100, // megabytes
MaxBackups: 3,
MaxAge: 28, //days
Compress: true,
})
return logger
}
For certain operations, you may need to maintain an audit trail for compliance reasons. You can create a separate audit logger for this purpose:
type AuditLogger struct {
logger *logrus.Logger
}
func NewAuditLogger() *AuditLogger {
logger := logrus.New()
logger.SetFormatter(&logrus.JSONFormatter{})
// Set up a separate output for audit logs
// This could be a different file, database, or even a separate Elasticsearch index
return &AuditLogger{logger: logger}
}
func (a *AuditLogger) LogAuditEvent(ctx context.Context, event string, details map[string]interface{}) {
span := trace.SpanFromContext(ctx)
a.logger.WithFields(logrus.Fields{
"event": event,
"trace_id": span.SpanContext().TraceID().String(),
"details": details,
}).Info("Audit event")
}
// Usage:
auditLogger.LogAuditEvent(ctx, "OrderCreated", map[string]interface{}{
"order_id": order.ID,
"user_id": order.UserID,
})
Now that we have a solid foundation for distributed tracing, let’s explore some advanced techniques to get even more value from OpenTelemetry.
Custom span attributes and events can provide additional context to your traces:
func ProcessPayment(ctx context.Context, order Order) error {
_, span := otel.Tracer("payment-service").Start(ctx, "ProcessPayment")
defer span.End()
span.SetAttributes(
attribute.String("payment.method", order.PaymentMethod),
attribute.Float64("payment.amount", order.Total),
)
// Process payment...
if paymentSuccessful {
span.AddEvent("PaymentProcessed", trace.WithAttributes(
attribute.String("transaction_id", transactionID),
))
} else {
span.AddEvent("PaymentFailed", trace.WithAttributes(
attribute.String("error", "Insufficient funds"),
))
}
return nil
}
Baggage allows you to propagate key-value pairs across service boundaries:
import (
"go.opentelemetry.io/otel/baggage"
)
func AddUserInfoToBaggage(ctx context.Context, userID string) context.Context {
b, _ := baggage.Parse(fmt.Sprintf("user_id=%s", userID))
return baggage.ContextWithBaggage(ctx, b)
}
func GetUserIDFromBaggage(ctx context.Context) string {
if b := baggage.FromContext(ctx); b != nil {
if v := b.Member("user_id"); v.Key() != "" {
return v.Value()
}
}
return ""
}
For high-volume services, tracing every request can be expensive. Implement a sampling strategy to reduce the volume while still maintaining visibility:
import (
"go.opentelemetry.io/otel/sdk/trace"
"go.opentelemetry.io/otel/sdk/trace/sampling"
)
sampler := sampling.ParentBased(
sampling.TraceIDRatioBased(0.1), // Sample 10% of traces
)
tp := trace.NewTracerProvider(
trace.WithSampler(sampler),
// ... other options ...
)
While we’ve been using Jaeger as our tracing backend, you might want to create a custom exporter for a different backend or for special processing:
type CustomExporter struct{}
func (e *CustomExporter) ExportSpans(ctx context.Context, spans []trace.ReadOnlySpan) error {
for _, span := range spans {
// Process or send the span data as needed
fmt.Printf("Exporting span: %s\n", span.Name())
}
return nil
}
func (e *CustomExporter) Shutdown(ctx context.Context) error {
// Cleanup logic here
return nil
}
// Use the custom exporter:
exporter := &CustomExporter{}
tp := trace.NewTracerProvider(
trace.WithBatcher(exporter),
// ... other options ...
)
OpenTelemetry can be integrated with many existing monitoring tools. For example, to send traces to both Jaeger and Zipkin:
jaegerExporter, _ := jaeger.New(jaeger.WithCollectorEndpoint(jaeger.WithEndpoint("http://jaeger:14268/api/traces")))
zipkinExporter, _ := zipkin.New("http://zipkin:9411/api/v2/spans")
tp := trace.NewTracerProvider(
trace.WithBatcher(jaegerExporter),
trace.WithBatcher(zipkinExporter),
// ... other options ...
)
These advanced techniques will help you get the most out of OpenTelemetry in your order processing system.
In the next sections, we’ll cover performance considerations, testing and validation strategies, and discuss some challenges and considerations when implementing distributed tracing and logging at scale.
When implementing distributed tracing and logging, it’s crucial to consider the performance impact on your system. Let’s explore some strategies to optimize performance.
type AsyncLogger struct {
ch chan *logrus.Entry
}
func NewAsyncLogger(bufferSize int) *AsyncLogger {
logger := &AsyncLogger{
ch: make(chan *logrus.Entry, bufferSize),
}
go logger.run()
return logger
}
func (l *AsyncLogger) run() {
for entry := range l.ch {
entry.Logger.Out.Write(entry.Bytes())
}
}
func (l *AsyncLogger) Log(entry *logrus.Entry) {
select {
case l.ch <- entry:
default:
// Buffer full, log dropped
}
}
func (l *AsyncLogger) SampledLog(entry *logrus.Entry, sampleRate float32) {
if rand.Float32() < sampleRate {
l.Log(entry)
}
}
sampler := trace.ParentBased(
trace.TraceIDRatioBased(0.1), // Sample 10% of traces
)
tp := trace.NewTracerProvider(
trace.WithSampler(sampler),
// ... other options ...
)
func ProcessOrder(ctx context.Context, order Order) error {
ctx, span := tracer.Start(ctx, "ProcessOrder")
defer span.End()
// Don't create a span for this quick operation
validateOrder(order)
// Create a span for this potentially slow operation
ctx, paymentSpan := tracer.Start(ctx, "ProcessPayment")
err := processPayment(ctx, order)
paymentSpan.End()
if err != nil {
return err
}
// ... rest of the function
}
Use the OpenTelemetry SDK’s built-in batching exporter to reduce the number of network calls:
exporter, err := jaeger.New(jaeger.WithCollectorEndpoint(jaeger.WithEndpoint("http://jaeger:14268/api/traces")))
if err != nil {
log.Fatalf("Failed to create Jaeger exporter: %v", err)
}
tp := trace.NewTracerProvider(
trace.WithBatcher(exporter,
trace.WithMaxExportBatchSize(100),
trace.WithBatchTimeout(5 * time.Second),
),
// ... other options ...
)
PUT _ilm/policy/logs_policy
{
"policy": {
"phases": {
"hot": {
"actions": {
"rollover": {
"max_size": "50GB",
"max_age": "1d"
}
}
},
"delete": {
"min_age": "30d",
"actions": {
"delete": {}
}
}
}
}
}
Use a caching layer like Redis to store frequently accessed logs and traces:
import (
"github.com/go-redis/redis/v8"
)
func getCachedTrace(traceID string) (*Trace, error) {
val, err := redisClient.Get(ctx, "trace:"+traceID).Bytes()
if err == redis.Nil {
// Trace not in cache, fetch from storage and cache it
trace, err := fetchTraceFromStorage(traceID)
if err != nil {
return nil, err
}
redisClient.Set(ctx, "trace:"+traceID, trace, 1*time.Hour)
return trace, nil
} else if err != nil {
return nil, err
}
var trace Trace
json.Unmarshal(val, &trace)
return &trace, nil
}
Proper testing and validation are crucial to ensure the reliability of your distributed tracing and logging implementation.
Use the OpenTelemetry testing package to unit test your trace instrumentation:
import (
"testing"
"go.opentelemetry.io/otel/sdk/trace/tracetest"
)
func TestProcessOrder(t *testing.T) {
sr := tracetest.NewSpanRecorder()
tp := trace.NewTracerProvider(trace.WithSpanProcessor(sr))
otel.SetTracerProvider(tp)
ctx := context.Background()
err := ProcessOrder(ctx, Order{ID: "123"})
if err != nil {
t.Errorf("ProcessOrder failed: %v", err)
}
spans := sr.Ended()
if len(spans) != 2 {
t.Errorf("Expected 2 spans, got %d", len(spans))
}
if spans[0].Name() != "ProcessOrder" {
t.Errorf("Expected span named 'ProcessOrder', got '%s'", spans[0].Name())
}
if spans[1].Name() != "ProcessPayment" {
t.Errorf("Expected span named 'ProcessPayment', got '%s'", spans[1].Name())
}
}
Set up integration tests that cover your entire tracing pipeline:
func TestTracingPipeline(t *testing.T) {
// Start a test Jaeger instance
jaeger := startTestJaeger()
defer jaeger.Stop()
// Initialize your application with tracing
app := initializeApp()
// Perform some operations that should generate traces
resp, err := app.CreateOrder(Order{ID: "123"})
if err != nil {
t.Fatalf("Failed to create order: %v", err)
}
// Wait for traces to be exported
time.Sleep(5 * time.Second)
// Query Jaeger for the trace
traces, err := jaeger.QueryTraces(resp.TraceID)
if err != nil {
t.Fatalf("Failed to query traces: %v", err)
}
// Validate the trace
validateTrace(t, traces[0])
}
Test your Logstash configuration to ensure it correctly parses and processes logs:
input {
generator {
message => '{"timestamp":"2023-06-01T10:00:00Z","severity":"INFO","message":"Order created","order_id":"123","trace_id":"abc123"}'
count => 1
}
}
filter {
json {
source => "message"
}
}
output {
stdout { codec => rubydebug }
}
Run this configuration with logstash -f test_config.conf and verify the output.
Perform load tests to understand the performance impact of tracing:
func BenchmarkWithTracing(b *testing.B) {
// Initialize tracing
tp := initTracer()
defer tp.Shutdown(context.Background())
b.ResetTimer()
for i := 0; i < b.N; i++ {
ctx, span := tp.Tracer("benchmark").Start(context.Background(), "operation")
performOperation(ctx)
span.End()
}
}
func BenchmarkWithoutTracing(b *testing.B) {
for i := 0; i < b.N; i++ {
performOperation(context.Background())
}
}
Compare the results to understand the overhead introduced by tracing.
Set up monitoring for your tracing and logging systems:
As you implement and scale your distributed tracing and logging system, keep these challenges and considerations in mind:
In this post, we’ve covered comprehensive distributed tracing and logging for our order processing system. We’ve implemented tracing with OpenTelemetry, set up centralized logging with the ELK stack, correlated logs and traces, and explored advanced techniques and considerations.
In the next and final part of our series, we’ll focus on Production Readiness and Scalability. We’ll cover:
Stay tuned as we put the finishing touches on our sophisticated order processing system, ensuring it’s ready for production use at scale!
Are you facing challenging problems, or need an external perspective on a new idea or project? I can help! Whether you're looking to build a technology proof of concept before making a larger investment, or you need guidance on difficult issues, I'm here to assist.
If you're interested in working with me, please reach out via email at hungaikevin@gmail.com.
Let's turn your challenges into opportunities!
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