Lazada API Exception Monitoring & Alerts: How to Build a Healthy Operations System

1. Introduction

In the e-commerce world, the Lazada API plays a vital role in product listings, orders, inventory synchronization, and more. As business traffic increases, any failure or latency in API calls can directly affect user experience and revenue. This article walks you through how to build a robust and scalable operations and monitoring system using Prometheus + Alertmanager, ELK/EFK logging stacks, auto-recovery strategies, and distributed observability tools.

2. Overview of Monitoring & Alerting

2.1 What is Monitoring and Alerting?

• Monitoring: Continuously collecting system metrics, logs, and traces to understand service health.

• Alerting: Automatically notifying engineers when key indicators hit abnormal thresholds or failures occur.

2.2 Three Pillars of Operational Health

1. Performance: Response time, throughput.

2. Availability: API success rate, system uptime.

3. Stability: Retry success rates, system load trends.

2.3 Design Principles

• Observability: Internal system states should be externally measurable.

• Scalability: Monitoring components must scale with traffic and system growth.

• Reliability: The monitoring system must be highly available with no single point of failure.

3. Core Monitoring Metric Design

4. Prometheus + Alertmanager in Practice

4.1 System Architecture

• Prometheus Server: Central collector and processor of metrics.

• Exporters: Metric providers like node_exporter, cadvisor, or custom clients.

• Pushgateway: For short-lived job metrics.

• Alertmanager: Groups, routes, and sends alert notifications.

4.2 Metric Collection & Exposure

(1) Installing node_exporter

wget https://github.com/prometheus/node_exporter/releases/download/v1.5.0/node_exporter-1.5.0.linux-amd64.tar.gz
tar xzf node_exporter-1.5.0.linux-amd64.tar.gz
cd node_exporter-1.5.0.linux-amd64
./node_exporter --web.listen-address=":9100" &

(2) Custom API Metric Example (Python)

from prometheus_client import Counter, Histogram, start_http_server
import time, requests
REQUEST_COUNT = Counter(
'lazada_api_requests_total',
'Total number of Lazada API requests',
['endpoint', 'http_status']
)
REQUEST_LATENCY = Histogram(
'lazada_api_request_latency_seconds',
'Latency of Lazada API requests',
['endpoint']
)
start_http_server(8000)
def call_api(endpoint, params=None):
start = time.time()
resp = requests.get(endpoint, params=params, timeout=10)
latency = time.time() - start
REQUEST_LATENCY.labels(endpoint=endpoint).observe(latency)
REQUEST_COUNT.labels(endpoint=endpoint, http_status=resp.status_code).inc()
return resp.json()

4.3 Alertmanager Configuration

alertmanager.yml:

global:
resolve_timeout: 5m
route:
receiver: 'team-slack'
group_wait: 30s
group_interval: 5m
repeat_interval: 3h
receivers:
- name: 'team-slack'
slack_configs:
- channel: '#ops-alerts'
send_resolved: true
inhibit_rules:
- source_match:
severity: 'critical'
target_match:
severity: 'warning'
equal:
- alertname

Alert rules lazada-alerts.yml:

groups:
- name: lazada-api
rules:
- alert: HighErrorRate
expr: sum(rate(lazada_api_requests_total{http_status=~"5.."}[5m]))
/ sum(rate(lazada_api_requests_total[5m])) > 0.05
for: 2m
labels:
severity: critical
annotations:
summary: "{{ $labels.instance }} High 5xx error rate"
description: "More than 5% 5xx errors in the last 5 minutes"
- alert: LatencySpike
expr: histogram_quantile(0.95, sum(rate(lazada_api_request_latency_seconds_bucket[5m])) by (le)) > 1
for: 3m
labels:
severity: warning
annotations:
summary: "{{ $labels.instance }} High P95 latency"
description: "P95 latency exceeds 1s in the last 5 minutes"

5. Log Aggregation: ELK vs EFK

5.1 Architecture Comparison

5.2 Log Format & Fluentd Setup

Structured log sample:

{
"timestamp": "2025-04-18T10:20:30Z",
"level": "ERROR",
"service": "lazada-client",
"endpoint": "/orders/create",
"status": 500,
"message": "Internal Server Error"
}

Fluentd config (fluent.conf):

<source>
@type tail
path /var/log/lazada-client/*.log
pos_file /var/log/fluentd-lazada-client.pos
tag lazada.client
<parse>
@type json
</parse>
</source>
<match lazada.client>
@type elasticsearch
host es-host
port 9200
logstash_format true
index_name lazada-client-%Y.%m.%d
</match>

5.3 Kibana Visualizations

• Create index pattern: lazada-client-*

• Common queries:

  • service: "lazada-client" AND status:500

  • endpoint.keyword: "/orders/create" AND response_time:[1 TO *]

6. Auto-Recovery Strategies

6.1 Common Issues and Fixes

6.2 Kubernetes Health Check

livenessProbe:
httpGet:
path: /healthz
port: 8080
initialDelaySeconds: 30
periodSeconds: 10
readinessProbe:
httpGet:
path: /readyz
port: 8080
initialDelaySeconds: 5
periodSeconds: 5

6.3 Auto-Restart Script Example

# auto_recover.py
import subprocess, time
def check_and_restart():
status = subprocess.run(["kubectl", "get", "pods", "-l app=lazada-client", "-o","json"], capture_output=True)
# Parse JSON output and restart failing pods
if __name__ == '__main__':
while True:
check_and_restart()
time.sleep(60)

7. End-to-End Observability

7.1 Distributed Tracing

• Use OpenTelemetry SDK for tracing.

• Visualize spans with Jaeger or Tempo.

7.2 Canary Releases

• Use Prometheus metrics to gate deployments.

• Automatically roll back on elevated latency or error rates.

7.3 Scheduled Reporting

• Use Grafana alert reports to email or Slack team health summaries daily.

8. Performance Optimization

1. Prefer horizontal scaling over vertical.

2. Add Redis caching for high-frequency endpoints.

3. Introduce Resilience4j for rate limiting, fallback, and circuit breaking.

9. Real-World Case Study

A large e-commerce platform adopted this system and reduced 5xx errors from 8% to 1.2%. P95 latency dropped from 1.3s to 0.6s, and alert response times improved by 40%.

10. Summary & Best Practices

• Start with clear metric design.

• Use Prometheus + Alertmanager for full alert lifecycle.

• Centralized logs enable fast incident debugging.

• Combine auto-recovery + tracing to build resilience.

• Embed observability into the development lifecycle.

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