歡迎來到我們關於實施複雜訂單處理系統系列的第五部分！在我們之前的文章中，我們涵蓋了從設定基本架構到實施高級工作流程和全面監控的所有內容。今天，我們將深入探討分散式追蹤和日誌記錄的世界，這是維護微服務架構中可觀察性的兩個關鍵元件。
在微服務架構中，單一使用者請求通常跨越多個服務。這種分散式特性使得理解請求流並在出現問題時診斷問題變得困難。分散式追蹤和集中式日誌記錄透過提供以下功能來解決這些挑戰：
為了實現分散式追蹤和日誌記錄，我們將使用兩個強大的工具集：
OpenTelemetry：雲端原生軟體的可觀察性框架，提供一組 API、函式庫、代理程式和收集器服務，用於從應用程式擷取分散式追蹤和指標。
ELK Stack：來自 Elastic 的三個開源產品（Elasticsearch、Logstash 和 Kibana）的集合，它們共同提供了一個用於日誌攝取、儲存和視覺化的強大平台。
讀完本文，您將能夠：
讓我們開始吧！
在開始實施之前，讓我們回顧一些對於我們的分散式追蹤和日誌記錄設定至關重要的關鍵概念。
分散式追蹤是一種追蹤請求流經分散式系統中的各種服務的方法。它提供了一種了解請求的完整生命週期的方法，包括：
一條跡線通常由一個或多個跨度組成。跨距代表一個工作或操作單元。它追蹤請求進行的特定操作，記錄操作何時開始和結束，以及其他資料。
OpenTelemetry 是雲端原生軟體的可觀察性框架。它提供一組 API、庫、代理和收集器服務，用於從應用程式捕獲分散式追蹤和指標。關鍵組件包括：
分散式系統中的有效日誌記錄需要仔細考慮：
ELK 堆疊是日誌管理的熱門選擇：
日誌聚合涉及從各種來源收集日誌資料並將其儲存在集中位置。這允許：
日誌分析涉及從日誌資料中提取有意義的見解，其中可以包括：
記住這些概念，讓我們繼續在我們的訂單處理系統中實現分散式追蹤。
讓我們先使用 OpenTelemetry 在我們的訂單處理系統中實現分散式追蹤。
首先，我們需要將 OpenTelemetry 加入我們的 Go 服務。將以下依賴項新增至您的 go.mod 檔案：
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
)
接下來，讓我們在主函數中設定一個追蹤器提供者：
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...
}
這會設定一個追蹤器提供程序，將追蹤匯出到 Jaeger（一種流行的分散式追蹤後端）。
現在，讓我們將追蹤加入到訂單處理工作流程中。我們將從 CreateOrder 函數開始：
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
}
這會為 CreateOrder 函數建立一個新的跨度並新增相關屬性。它還為流程中的每個主要步驟建立子跨度。
當呼叫其他服務時，我們需要傳播追蹤上下文。以下是如何使用 HTTP 用戶端執行此操作的範例：
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
}
這使用 otelhttp 套件自動傳播 HTTP 標頭中的追蹤上下文。
對於非同步操作，我們需要確保正確傳遞追蹤上下文。這是使用工作池的範例：
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")
}
}
}
這會為非同步操作建立一個新的範圍並將其傳遞給工作函數。
要將 OpenTelemetry 與 Temporal 工作流程集成，我們可以使用 go.opentelemetry.io/contrib/instrumentation/go.temporal.io/temporal/oteltemporalgrpc 套件：
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
}
這將使用 OpenTelemetry 工具設定 Temporal 用戶端和工作人員。
我們已經在 initTracer 函數中將 Jaeger 設為追蹤後端。為了可視化我們的痕跡，我們需要將 Jaeger 添加到我們的 docker-compose.yml 中：
services:
# ... other services ...
jaeger:
image: jaegertracing/all-in-one:1.35
ports:
- "16686:16686"
- "14268:14268"
environment:
- COLLECTOR_OTLP_ENABLED=true
現在您可以透過 http://localhost:16686 存取 Jaeger UI 來查看和分析您的痕跡。
在下一節中，我們將使用 ELK 堆疊設定集中式日誌記錄，以補充我們的分散式追蹤設定。
現在我們已經有了分散式跟踪，讓我們使用 ELK（Elasticsearch、Logstash、Kibana）堆疊設定集中式日誌記錄。
首先，讓我們將 Elasticsearch 加入我們的 docker-compose.yml 中：
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
這會設定一個單節點 Elasticsearch 實例以用於開發目的。
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:
在這篇文章中，我們介紹了訂單處理系統的全面分散式追蹤和日誌記錄。我們使用 OpenTelemetry 實現了跟踪，使用 ELK 堆疊設置集中式日誌記錄、關聯日誌和跟踪，並探索了高級技術和注意事項。
在我們系列的下一部分也是最後一部分，我們將重點放在生產就緒性和可擴展性上。我們將介紹：
請繼續關注我們對複雜的訂單處理系統進行最後的修飾，確保其準備好大規模生產使用！
您是否面臨著具有挑戰性的問題，或需要外部視角來看待新想法或專案？我可以幫忙！無論您是想在進行更大投資之前建立技術概念驗證，還是需要解決困難問題的指導，我都會為您提供協助。
如果您有興趣與我合作，請透過電子郵件與我聯繫：hungaikevin@gmail.com。
讓我們將挑戰轉化為機會！
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