洗練された注文処理システムの実装に関するシリーズの第 4 回へようこそ!前回の投稿では、プロジェクトの基礎を築き、高度な Temporal ワークフローを検討し、高度なデータベース操作について詳しく説明しました。今日、私たちは、本番稼働可能なシステムの同様に重要な側面である監視とアラートに焦点を当てています。
マイクロサービス アーキテクチャ、特に注文管理などの複雑なプロセスを処理するアーキテクチャでは、効果的な監視とアラートが非常に重要です。これらにより、次のことが可能になります。
Prometheus は、オープンソースのシステム監視および警告ツールキットです。強力な機能と広範なエコシステムにより、クラウドネイティブの世界では標準となっています。主要なコンポーネントは次のとおりです:
また、モニタリングと可観測性のための人気のオープンソース プラットフォームである Grafana を使用して、ダッシュボードを作成し、Prometheus データを視覚化します。
この投稿を終えると、次のことができるようになります:
さあ、飛び込みましょう!
実装を開始する前に、監視とアラートの設定に重要ないくつかの重要な概念を確認しましょう。
可観測性とは、システムの出力を調べることによってシステムの内部状態を理解する能力を指します。注文処理システムのような分散システムでは、通常、可観測性には次の 3 つの主要な柱が含まれます。
この投稿では、主にメトリクスに焦点を当てますが、これらをログやトレースと統合する方法についても触れます。
Prometheus はプルベースのアーキテクチャに従います:
Prometheus は 4 つのコア メトリック タイプを提供します。
PromQL (Prometheus Query Language) ialah bahasa berfungsi yang berkuasa untuk menanyakan data Prometheus. Ia membolehkan anda memilih dan mengagregat data siri masa dalam masa nyata. Ciri utama termasuk:
Kami akan melihat contoh pertanyaan PromQL semasa kami membina papan pemuka dan makluman kami.
Grafana ialah analitik sumber terbuka berbilang platform dan aplikasi web visualisasi interaktif. Ia menyediakan carta, graf dan makluman untuk web apabila disambungkan kepada sumber data yang disokong, yang mana Prometheus adalah salah satunya. Ciri utama termasuk:
Sekarang kita telah membincangkan konsep ini, mari kita mula melaksanakan sistem pemantauan dan amaran kami.
Mari mulakan dengan menyediakan Prometheus untuk memantau sistem pemprosesan pesanan kami.
Pertama, mari tambah Prometheus pada fail docker-compose.yml kami:
services:
# ... other services ...
prometheus:
image: prom/prometheus:v2.30.3
volumes:
- ./prometheus:/etc/prometheus
- prometheus_data:/prometheus
command:
- '--config.file=/etc/prometheus/prometheus.yml'
- '--storage.tsdb.path=/prometheus'
- '--web.console.libraries=/usr/share/prometheus/console_libraries'
- '--web.console.templates=/usr/share/prometheus/consoles'
ports:
- 9090:9090
volumes:
# ... other volumes ...
prometheus_data: {}
Seterusnya, buat fail prometheus.yml dalam direktori ./prometheus:
global:
scrape_interval: 15s
evaluation_interval: 15s
scrape_configs:
- job_name: 'prometheus'
static_configs:
- targets: ['localhost:9090']
- job_name: 'order_processing_api'
static_configs:
- targets: ['order_processing_api:8080']
- job_name: 'postgres'
static_configs:
- targets: ['postgres_exporter:9187']
Konfigurasi ini memberitahu Prometheus untuk mengikis metrik daripada dirinya sendiri, API pemprosesan pesanan kami dan pengeksport Postgres (yang akan kami sediakan kemudian).
Untuk mendedahkan metrik daripada perkhidmatan Go kami, kami akan menggunakan pustaka pelanggan Prometheus. Mula-mula, tambahkannya pada go.mod anda:
go get github.com/prometheus/client_golang
Sekarang, mari ubah suai fail Go utama kami untuk mendedahkan metrik:
package main
import (
"net/http"
"github.com/gin-gonic/gin"
"github.com/prometheus/client_golang/prometheus"
"github.com/prometheus/client_golang/prometheus/promhttp"
)
var (
httpRequestsTotal = prometheus.NewCounterVec(
prometheus.CounterOpts{
Name: "http_requests_total",
Help: "Total number of HTTP requests",
},
[]string{"method", "endpoint", "status"},
)
httpRequestDuration = prometheus.NewHistogramVec(
prometheus.HistogramOpts{
Name: "http_request_duration_seconds",
Help: "Duration of HTTP requests in seconds",
Buckets: prometheus.DefBuckets,
},
[]string{"method", "endpoint"},
)
)
func init() {
prometheus.MustRegister(httpRequestsTotal)
prometheus.MustRegister(httpRequestDuration)
}
func main() {
r := gin.Default()
// Middleware to record metrics
r.Use(func(c *gin.Context) {
timer := prometheus.NewTimer(httpRequestDuration.WithLabelValues(c.Request.Method, c.FullPath()))
c.Next()
timer.ObserveDuration()
httpRequestsTotal.WithLabelValues(c.Request.Method, c.FullPath(), string(c.Writer.Status())).Inc()
})
// Expose metrics endpoint
r.GET("/metrics", gin.WrapH(promhttp.Handler()))
// ... rest of your routes ...
r.Run(":8080")
}
Kod ini menyediakan dua metrik:
Untuk persekitaran yang lebih dinamik, Prometheus menyokong pelbagai mekanisme penemuan perkhidmatan. Contohnya, jika anda menjalankan Kubernetes, anda mungkin menggunakan konfigurasi SD Kubernetes:
scrape_configs:
- job_name: 'kubernetes-pods'
kubernetes_sd_configs:
- role: pod
relabel_configs:
- source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
action: keep
regex: true
- source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path]
action: replace
target_label: __metrics_path__
regex: (.+)
Konfigurasi ini secara automatik akan menemui dan mengikis metrik daripada pod dengan anotasi yang sesuai.
Prometheus menyimpan data dalam pangkalan data siri masa pada sistem fail tempatan. Anda boleh mengkonfigurasi masa pengekalan dan saiz storan dalam konfigurasi Prometheus:
global:
scrape_interval: 15s
evaluation_interval: 15s
storage:
tsdb:
retention.time: 15d
retention.size: 50GB
# ... rest of the configuration ...
Konfigurasi ini menetapkan tempoh pengekalan selama 15 hari dan saiz storan maksimum 50GB.
Dalam bahagian seterusnya, kami akan menyelami dalam menentukan dan melaksanakan metrik tersuai untuk sistem pemprosesan pesanan kami.
Sekarang kami telah menyediakan Prometheus dan metrik HTTP asas dilaksanakan, mari tentukan dan laksanakan metrik tersuai khusus untuk sistem pemprosesan pesanan kami.
Apabila mereka bentuk metrik, adalah penting untuk memikirkan tentang cerapan yang ingin kami peroleh daripada sistem kami. Untuk sistem pemprosesan pesanan kami, kami mungkin mahu menjejaki:
Mari kita laksanakan metrik ini:
package metrics
import (
"github.com/prometheus/client_golang/prometheus"
"github.com/prometheus/client_golang/prometheus/promauto"
)
var (
OrdersCreated = promauto.NewCounter(prometheus.CounterOpts{
Name: "orders_created_total",
Help: "The total number of created orders",
})
OrderProcessingTime = promauto.NewHistogram(prometheus.HistogramOpts{
Name: "order_processing_seconds",
Help: "Time taken to process an order",
Buckets: prometheus.LinearBuckets(0, 30, 10), // 0-300 seconds, 30-second buckets
})
OrderStatusGauge = promauto.NewGaugeVec(prometheus.GaugeOpts{
Name: "orders_by_status",
Help: "Number of orders by status",
}, []string{"status"})
PaymentProcessed = promauto.NewCounterVec(prometheus.CounterOpts{
Name: "payments_processed_total",
Help: "The total number of processed payments",
}, []string{"status"})
InventoryUpdates = promauto.NewCounter(prometheus.CounterOpts{
Name: "inventory_updates_total",
Help: "The total number of inventory updates",
})
ShippingArrangementTime = promauto.NewHistogram(prometheus.HistogramOpts{
Name: "shipping_arrangement_seconds",
Help: "Time taken to arrange shipping",
Buckets: prometheus.LinearBuckets(0, 60, 5), // 0-300 seconds, 60-second buckets
})
)
Sekarang kami telah menentukan metrik kami, mari laksanakan metrik tersebut dalam perkhidmatan kami:
package main
import (
"time"
"github.com/yourusername/order-processing-system/metrics"
)
func createOrder(order Order) error {
startTime := time.Now()
// Order creation logic...
metrics.OrdersCreated.Inc()
metrics.OrderProcessingTime.Observe(time.Since(startTime).Seconds())
metrics.OrderStatusGauge.WithLabelValues("pending").Inc()
return nil
}
func processPayment(payment Payment) error {
// Payment processing logic...
if paymentSuccessful {
metrics.PaymentProcessed.WithLabelValues("success").Inc()
} else {
metrics.PaymentProcessed.WithLabelValues("failure").Inc()
}
return nil
}
func updateInventory(item Item) error {
// Inventory update logic...
metrics.InventoryUpdates.Inc()
return nil
}
func arrangeShipping(order Order) error {
startTime := time.Now()
// Shipping arrangement logic...
metrics.ShippingArrangementTime.Observe(time.Since(startTime).Seconds())
return nil
}
Apabila menamakan dan melabel metrik, pertimbangkan amalan terbaik ini:
For API endpoints, we’ve already implemented basic instrumentation. For database operations, we can add metrics like this:
func (s *Store) GetOrder(ctx context.Context, id int64) (Order, error) {
startTime := time.Now()
defer func() {
metrics.DBOperationDuration.WithLabelValues("GetOrder").Observe(time.Since(startTime).Seconds())
}()
// Existing GetOrder logic...
}
For Temporal workflows, we can add metrics in our activity implementations:
func ProcessOrderActivity(ctx context.Context, order Order) error {
startTime := time.Now()
defer func() {
metrics.WorkflowActivityDuration.WithLabelValues("ProcessOrder").Observe(time.Since(startTime).Seconds())
}()
// Existing ProcessOrder logic...
}
Now that we have our metrics set up, let’s visualize them using Grafana.
First, let’s add Grafana to our docker-compose.yml:
services:
# ... other services ...
grafana:
image: grafana/grafana:8.2.2
ports:
- 3000:3000
volumes:
- grafana_data:/var/lib/grafana
volumes:
# ... other volumes ...
grafana_data: {}
Let’s create a dashboard for our order processing system:
For our first panel, let’s create a graph of order creation rate:
Let’s add another panel for order processing time:
For order status distribution:
Continue adding panels for other metrics we’ve defined.
Grafana allows us to create variables that can be used across the dashboard. Let’s create a variable for time range:
Now we can use this in our queries like this: rate(orders_created_total[$time_range])
In the next section, we’ll set up alerting rules to notify us of potential issues in our system.
Now that we have our metrics and dashboards set up, let’s implement alerting to proactively notify us of potential issues in our system.
When designing alerts, consider the following principles:
For our order processing system, we might want to alert on:
Let’s create an alerts.yml file in our Prometheus configuration directory:
groups:
- name: order_processing_alerts
rules:
- alert: HighOrderProcessingErrorRate
expr: rate(order_processing_errors_total[5m]) / rate(orders_created_total[5m]) > 0.05
for: 5m
labels:
severity: critical
annotations:
summary: High order processing error rate
description: "Error rate is over the last 5 minutes"
- alert: SlowOrderProcessing
expr: histogram_quantile(0.95, rate(order_processing_seconds_bucket[5m])) > 300
for: 10m
labels:
severity: warning
annotations:
summary: Slow order processing
description: "95th percentile of order processing time is over the last 5 minutes"
- alert: UnusualOrderRate
expr: abs(rate(orders_created_total[1h]) - rate(orders_created_total[1h] offset 1d)) > (rate(orders_created_total[1h] offset 1d) * 0.3)
for: 30m
labels:
severity: warning
annotations:
summary: Unusual order creation rate
description: "Order creation rate has changed by more than 30% compared to the same time yesterday"
- alert: LowInventory
expr: inventory_level < 10
for: 5m
labels:
severity: warning
annotations:
summary: Low inventory level
description: "Inventory level for is "
- alert: HighPaymentFailureRate
expr: rate(payments_processed_total{status="failure"}[15m]) / rate(payments_processed_total[15m]) > 0.1
for: 15m
labels:
severity: critical
annotations:
summary: High payment failure rate
description: "Payment failure rate is over the last 15 minutes"
Update your prometheus.yml to include this alerts file:
rule_files: - "alerts.yml"
Now, let’s set up Alertmanager to handle our alerts. Add Alertmanager to your docker-compose.yml:
services:
# ... other services ...
alertmanager:
image: prom/alertmanager:v0.23.0
ports:
- 9093:9093
volumes:
- ./alertmanager:/etc/alertmanager
command:
- '--config.file=/etc/alertmanager/alertmanager.yml'
Create an alertmanager.yml in the ./alertmanager directory:
route:
group_by: ['alertname']
group_wait: 30s
group_interval: 5m
repeat_interval: 1h
receiver: 'email-notifications'
receivers:
- name: 'email-notifications'
email_configs:
- to: 'team@example.com'
from: 'alertmanager@example.com'
smarthost: 'smtp.example.com:587'
auth_username: 'alertmanager@example.com'
auth_identity: 'alertmanager@example.com'
auth_password: 'password'
Update your prometheus.yml to point to Alertmanager:
alerting:
alertmanagers:
- static_configs:
- targets:
- alertmanager:9093
In the Alertmanager configuration above, we’ve set up email notifications. You can also configure other channels like Slack, PagerDuty, or custom webhooks.
In our alerts, we’ve used severity labels. We can use these in Alertmanager to implement different routing or notification strategies based on severity:
route:
group_by: ['alertname']
group_wait: 30s
group_interval: 5m
repeat_interval: 1h
receiver: 'email-notifications'
routes:
- match:
severity: critical
receiver: 'pagerduty-critical'
- match:
severity: warning
receiver: 'slack-warnings'
receivers:
- name: 'email-notifications'
email_configs:
- to: 'team@example.com'
- name: 'pagerduty-critical'
pagerduty_configs:
- service_key: '<your-pagerduty-service-key>'
- name: 'slack-warnings'
slack_configs:
- api_url: '<your-slack-webhook-url>'
channel: '#alerts'
Monitoring database performance is crucial for maintaining a responsive and reliable system. Let’s set up monitoring for our PostgreSQL database.
First, add the Postgres exporter to your docker-compose.yml:
services:
# ... other services ...
postgres_exporter:
image: wrouesnel/postgres_exporter:latest
environment:
DATA_SOURCE_NAME: "postgresql://user:password@postgres:5432/dbname?sslmode=disable"
ports:
- 9187:9187
Make sure to replace user, password, and dbname with your actual PostgreSQL credentials.
Some important PostgreSQL metrics to monitor include:
Let’s create a new dashboard for database performance:
Let’s add some database-specific alerts to our alerts.yml:
- alert: HighDatabaseConnections
expr: pg_stat_activity_count > 100
for: 5m
labels:
severity: warning
annotations:
summary: High number of database connections
description: "There are active database connections"
- alert: LowCacheHitRatio
expr: pg_stat_database_blks_hit / (pg_stat_database_blks_hit + pg_stat_database_blks_read) < 0.9
for: 15m
labels:
severity: warning
annotations:
summary: Low database cache hit ratio
description: "Cache hit ratio is "
Monitoring Temporal workflows is essential for ensuring the reliability and performance of our order processing system.
Temporal provides a metrics client that we can use to expose metrics to Prometheus. Let’s update our Temporal worker to include metrics:
import (
"go.temporal.io/sdk/client"
"go.temporal.io/sdk/worker"
"go.temporal.io/sdk/contrib/prometheus"
)
func main() {
// ... other setup ...
// Create Prometheus metrics handler
metricsHandler := prometheus.NewPrometheusMetricsHandler()
// Create Temporal client with metrics
c, err := client.NewClient(client.Options{
MetricsHandler: metricsHandler,
})
if err != nil {
log.Fatalln("Unable to create Temporal client", err)
}
defer c.Close()
// Create worker with metrics
w := worker.New(c, "order-processing-task-queue", worker.Options{
MetricsHandler: metricsHandler,
})
// ... register workflows and activities ...
// Run the worker
err = w.Run(worker.InterruptCh())
if err != nil {
log.Fatalln("Unable to start worker", err)
}
}
Important Temporal metrics to monitor include:
Let’s create a dashboard for Temporal workflows:
Let’s add some Temporal-specific alerts to our alerts.yml:
- alert: HighWorkflowFailureRate
expr: rate(temporal_workflow_failed_total[15m]) / rate(temporal_workflow_completed_total[15m]) > 0.05
for: 15m
labels:
severity: critical
annotations:
summary: High workflow failure rate
description: "Workflow failure rate is over the last 15 minutes"
- alert: LongRunningWorkflow
expr: histogram_quantile(0.95, rate(temporal_workflow_execution_time_bucket[1h])) > 3600
for: 30m
labels:
severity: warning
annotations:
summary: Long-running workflows detected
description: "95th percentile of workflow execution time is over 1 hour"
These alerts will help you detect issues with your Temporal workflows, such as high failure rates or unexpectedly long-running workflows.
In the next sections, we’ll cover some advanced Prometheus techniques and discuss testing and validation of our monitoring setup.
As our monitoring system grows more complex, we can leverage some advanced Prometheus techniques to improve its efficiency and capabilities.
Recording rules allow you to precompute frequently needed or computationally expensive expressions and save their result as a new set of time series. This can significantly speed up the evaluation of dashboards and alerts.
Let’s add some recording rules to our Prometheus configuration. Create a rules.yml file:
groups:
- name: example_recording_rules
interval: 5m
rules:
- record: job:order_processing_rate:5m
expr: rate(orders_created_total[5m])
- record: job:order_processing_error_rate:5m
expr: rate(order_processing_errors_total[5m]) / rate(orders_created_total[5m])
- record: job:payment_success_rate:5m
expr: rate(payments_processed_total{status="success"}[5m]) / rate(payments_processed_total[5m])
Add this file to your Prometheus configuration:
rule_files: - "alerts.yml" - "rules.yml"
Now you can use these precomputed metrics in your dashboards and alerts, which can be especially helpful for complex queries that you use frequently.
The Pushgateway allows you to push metrics from jobs that can’t be scraped, such as batch jobs or serverless functions. Let’s add a Pushgateway to our docker-compose.yml:
services:
# ... other services ...
pushgateway:
image: prom/pushgateway
ports:
- 9091:9091
Now, you can push metrics to the Pushgateway from your batch jobs or short-lived processes. Here’s an example using the Go client:
import (
"github.com/prometheus/client_golang/prometheus"
"github.com/prometheus/client_golang/prometheus/push"
)
func runBatchJob() {
// Define a counter for the batch job
batchJobCounter := prometheus.NewCounter(prometheus.CounterOpts{
Name: "batch_job_processed_total",
Help: "Total number of items processed by the batch job",
})
// Run your batch job and update the counter
// ...
// Push the metric to the Pushgateway
pusher := push.New("http://pushgateway:9091", "batch_job")
pusher.Collector(batchJobCounter)
if err := pusher.Push(); err != nil {
log.Printf("Could not push to Pushgateway: %v", err)
}
}
Don’t forget to add the Pushgateway as a target in your Prometheus configuration:
scrape_configs:
# ... other configs ...
- job_name: 'pushgateway'
static_configs:
- targets: ['pushgateway:9091']
For large-scale systems, you might need to set up Prometheus federation, where one Prometheus server scrapes data from other Prometheus servers. This allows you to aggregate metrics from multiple Prometheus instances.
Here’s an example configuration for a federated Prometheus setup:
scrape_configs:
- job_name: 'federate'
scrape_interval: 15s
honor_labels: true
metrics_path: '/federate'
params:
'match[]':
- '{job="order_processing_api"}'
- '{job="postgres_exporter"}'
static_configs:
- targets:
- 'prometheus-1:9090'
- 'prometheus-2:9090'
This configuration allows a higher-level Prometheus server to scrape specific metrics from other Prometheus servers.
Exemplars allow you to link metrics to trace data, providing a way to drill down from a high-level metric to a specific trace. This is particularly useful when integrating Prometheus with distributed tracing systems like Jaeger or Zipkin.
To use exemplars, you need to enable them in your Prometheus configuration:
global:
scrape_interval: 15s
evaluation_interval: 15s
exemplar_storage:
enable: true
Then, when instrumenting your code, you can add exemplars to your metrics:
import (
"github.com/prometheus/client_golang/prometheus"
"github.com/prometheus/client_golang/prometheus/promauto"
)
var (
orderProcessingDuration = promauto.NewHistogramVec(
prometheus.HistogramOpts{
Name: "order_processing_duration_seconds",
Help: "Duration of order processing in seconds",
Buckets: prometheus.DefBuckets,
},
[]string{"status"},
)
)
func processOrder(order Order) {
start := time.Now()
// Process the order...
duration := time.Since(start)
orderProcessingDuration.WithLabelValues(order.Status).Observe(duration.Seconds(),
prometheus.Labels{
"traceID": getCurrentTraceID(),
},
)
}
This allows you to link from a spike in order processing duration directly to the trace of a slow order, greatly aiding in debugging and performance analysis.
Ensuring the reliability of your monitoring system is crucial. Let’s explore some strategies for testing and validating our Prometheus setup.
When unit testing your Go code, you can use the prometheus/testutil package to verify that your metrics are being updated correctly:
import (
"testing"
"github.com/prometheus/client_golang/prometheus/testutil"
)
func TestOrderProcessing(t *testing.T) {
// Process an order
processOrder(Order{ID: 1, Status: "completed"})
// Check if the metric was updated
expected := `
# HELP order_processing_duration_seconds Duration of order processing in seconds
# TYPE order_processing_duration_seconds histogram
order_processing_duration_seconds_bucket{status="completed",le="0.005"} 1
order_processing_duration_seconds_bucket{status="completed",le="0.01"} 1
# ... other buckets ...
order_processing_duration_seconds_sum{status="completed"} 0.001
order_processing_duration_seconds_count{status="completed"} 1
`
if err := testutil.CollectAndCompare(orderProcessingDuration, strings.NewReader(expected)); err != nil {
t.Errorf("unexpected collecting result:\n%s", err)
}
}
To test that Prometheus is correctly scraping your metrics, you can set up an integration test that starts your application, waits for Prometheus to scrape it, and then queries Prometheus to verify the metrics:
func TestPrometheusIntegration(t *testing.T) {
// Start your application
go startApp()
// Wait for Prometheus to scrape (adjust the sleep time as needed)
time.Sleep(30 * time.Second)
// Query Prometheus
client, err := api.NewClient(api.Config{
Address: "http://localhost:9090",
})
if err != nil {
t.Fatalf("Error creating client: %v", err)
}
v1api := v1.NewAPI(client)
ctx, cancel := context.WithTimeout(context.Background(), 10*time.Second)
defer cancel()
result, warnings, err := v1api.Query(ctx, "order_processing_duration_seconds_count", time.Now())
if err != nil {
t.Fatalf("Error querying Prometheus: %v", err)
}
if len(warnings) > 0 {
t.Logf("Warnings: %v", warnings)
}
// Check the result
if result.(model.Vector).Len() == 0 {
t.Errorf("Expected non-empty result")
}
}
It’s important to verify that your monitoring system performs well under load. You can use tools like hey or vegeta to generate load on your system while observing your metrics:
hey -n 10000 -c 100 http://localhost:8080/orders
While the load test is running, observe your Grafana dashboards and check that your metrics are updating as expected and that Prometheus is able to keep up with the increased load.
To test your alerting rules, you can temporarily adjust the thresholds to trigger alerts, or use Prometheus’s API to manually fire alerts:
curl -H "Content-Type: application/json" -d '{
"alerts": [
{
"labels": {
"alertname": "HighOrderProcessingErrorRate",
"severity": "critical"
},
"annotations": {
"summary": "High order processing error rate"
}
}
]
}' http://localhost:9093/api/v1/alerts
This will send a test alert to your Alertmanager, allowing you to verify that your notification channels are working correctly.
As you implement and scale your monitoring system, keep these challenges and considerations in mind:
High cardinality can lead to performance issues in Prometheus. Be cautious when adding labels to metrics, especially labels with many possible values (like user IDs or IP addresses). Instead, consider using histogram metrics or reducing the cardinality by grouping similar values.
For large-scale systems, consider:
Your monitoring system is critical infrastructure. Consider:
Pastikan bahawa:
Untuk mengurangkan bunyi amaran:
Dalam siaran ini, kami telah merangkumi pemantauan dan amaran komprehensif untuk sistem pemprosesan pesanan kami menggunakan Prometheus dan Grafana. Kami telah menyediakan metrik tersuai, mencipta papan pemuka bermaklumat, melaksanakan makluman dan meneroka teknik dan pertimbangan lanjutan.
Dalam bahagian seterusnya siri kami, kami akan menumpukan pada pengesanan dan pembalakan yang diedarkan. Kami akan meliputi:
Nantikan semasa kami terus mempertingkatkan sistem pemprosesan pesanan kami, memfokus seterusnya untuk mendapatkan cerapan yang lebih mendalam tentang tingkah laku dan prestasi sistem yang diedarkan kami!
Adakah anda menghadapi masalah yang mencabar, atau memerlukan perspektif luaran tentang idea atau projek baharu? Saya boleh tolong! Sama ada anda ingin membina konsep bukti teknologi sebelum membuat pelaburan yang lebih besar, atau anda memerlukan panduan tentang isu yang sukar, saya sedia membantu.
Jika anda berminat untuk bekerja dengan saya, sila hubungi melalui e-mel di hungaikevin@gmail.com.
Mari jadikan cabaran anda sebagai peluang!
以上が注文処理システムの実装: 部品の監視とアラートの詳細内容です。詳細については、PHP 中国語 Web サイトの他の関連記事を参照してください。
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