Selamat datang ke ansuran keempat siri kami untuk melaksanakan sistem pemprosesan pesanan yang canggih! Dalam catatan kami sebelum ini, kami meletakkan asas untuk projek kami, meneroka aliran kerja Temporal termaju dan menyelidiki operasi pangkalan data lanjutan. Hari ini, kami menumpukan pada aspek yang sama penting dalam mana-mana sistem sedia pengeluaran: pemantauan dan amaran.
Dalam seni bina perkhidmatan mikro, terutamanya yang mengendalikan proses kompleks seperti pengurusan pesanan, pemantauan dan amaran yang berkesan adalah penting. Mereka membenarkan kami:
Prometheus ialah kit alat pemantauan dan amaran sistem sumber terbuka. Ia telah menjadi standard dalam dunia asal awan kerana ciri yang berkuasa dan ekosistem yang luas. Komponen utama termasuk:
Kami juga akan menggunakan Grafana, platform sumber terbuka yang popular untuk pemantauan dan pemerhatian, untuk membuat papan pemuka dan menggambarkan data Prometheus kami.
Menjelang akhir siaran ini, anda akan dapat:
Jom selami!
Sebelum kita mula melaksanakan, mari semak beberapa konsep utama yang akan menjadi penting untuk persediaan pemantauan dan amaran kita.
Kebolehcerap merujuk kepada keupayaan untuk memahami keadaan dalaman sistem dengan memeriksa outputnya. Dalam sistem teragih seperti sistem pemprosesan pesanan kami, kebolehmerhatian biasanya merangkumi tiga tiang utama:
Dalam siaran ini, kami akan memberi tumpuan terutamanya pada metrik, walaupun kami akan menyentuh tentang cara ini boleh disepadukan dengan log dan surih.
Prometheus mengikuti seni bina berasaskan tarik:
Prometheus menawarkan empat jenis metrik teras:
PromQL (Prometheus Query Language) est un langage fonctionnel puissant pour interroger les données Prometheus. Il vous permet de sélectionner et d'agréger des données de séries chronologiques en temps réel. Les principales fonctionnalités incluent :
Nous verrons des exemples de requêtes PromQL au fur et à mesure que nous créerons nos tableaux de bord et nos alertes.
Grafana est une application Web d'analyse et de visualisation interactive multiplateforme open source. Il fournit des tableaux, des graphiques et des alertes pour le Web lorsqu'il est connecté à des sources de données prises en charge, dont Prometheus. Les principales fonctionnalités incluent :
Maintenant que nous avons abordé ces concepts, commençons à mettre en œuvre notre système de surveillance et d'alerte.
Commençons par configurer Prometheus pour surveiller notre système de traitement des commandes.
Tout d’abord, ajoutons Prometheus à notre fichier docker-compose.yml :
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: {}
Ensuite, créez un fichier prometheus.yml dans le répertoire ./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']
Cette configuration indique à Prometheus d'extraire les métriques de lui-même, de notre API de traitement des commandes et d'un exportateur Postgres (que nous configurerons plus tard).
Pour exposer les métriques de nos services Go, nous utiliserons la bibliothèque client Prometheus. Tout d’abord, ajoutez-le à votre go.mod :
go get github.com/prometheus/client_golang
Maintenant, modifions notre fichier Go principal pour exposer les métriques :
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")
}
Ce code configure deux métriques :
Pour les environnements plus dynamiques, Prometheus prend en charge divers mécanismes de découverte de services. Par exemple, si vous utilisez Kubernetes, vous pouvez utiliser la configuration 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: (.+)
Cette configuration découvrira et récupérera automatiquement les métriques des pods avec les annotations appropriées.
Prometheus stocke les données dans une base de données de séries chronologiques sur le système de fichiers local. Vous pouvez configurer le temps de rétention et la taille de stockage dans la configuration Prometheus :
global:
scrape_interval: 15s
evaluation_interval: 15s
storage:
tsdb:
retention.time: 15d
retention.size: 50GB
# ... rest of the configuration ...
Cette configuration définit une période de conservation de 15 jours et une taille de stockage maximale de 50 Go.
Dans la section suivante, nous aborderons la définition et la mise en œuvre de métriques personnalisées pour notre système de traitement des commandes.
Maintenant que Prometheus est configuré et que les métriques HTTP de base sont implémentées, définissons et implémentons des métriques personnalisées spécifiques à notre système de traitement des commandes.
Lors de la conception de métriques, il est important de réfléchir aux informations que nous souhaitons tirer de notre système. Pour notre système de traitement des commandes, nous souhaiterons peut-être suivre :
Mettons en œuvre ces métriques :
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
})
)
Maintenant que nous avons défini nos métriques, implémentons-les dans notre service :
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
}
Lorsque vous nommez et étiquetez les métriques, tenez compte de ces bonnes pratiques :
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:
Assurez-vous que :
Pour réduire le bruit d'alerte :
Dans cet article, nous avons couvert la surveillance et les alertes complètes de notre système de traitement des commandes à l'aide de Prometheus et Grafana. Nous avons mis en place des métriques personnalisées, créé des tableaux de bord informatifs, mis en œuvre des alertes et exploré des techniques et considérations avancées.
Dans la prochaine partie de notre série, nous nous concentrerons sur le traçage et la journalisation distribués. Nous couvrirons :
Restez à l'écoute alors que nous continuons à améliorer notre système de traitement des commandes, en nous concentrant ensuite sur l'obtention d'informations plus approfondies sur le comportement et les performances de notre système distribué !
Êtes-vous confronté à des problèmes difficiles ou avez-vous besoin d'un point de vue externe sur une nouvelle idée ou un nouveau projet ? Je peux aider ! Que vous cherchiez à établir une preuve de concept technologique avant de réaliser un investissement plus important ou que vous ayez besoin de conseils sur des problèmes difficiles, je suis là pour vous aider.
Si vous souhaitez travailler avec moi, veuillez nous contacter par e-mail à hungaikevin@gmail.com.
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