Cloudwatch
/SKILLSurveillance AWS CloudWatch pour les journaux, les métriques, les alarmes et les tableaux de bord. À utiliser lors de la mise en place de la surveillance, de la création d'alarmes, de l'interrogation
name: cloudwatch
description: AWS CloudWatch monitoring for logs, metrics, alarms, and dashboards. Use when setting up monitoring, creating alarms, querying logs with Insights, configuring metric filters, building dashboards, or troubleshooting application issues.
last_updated: "2026-01-07"
doc_source: https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/
AWS CloudWatch
Amazon CloudWatch provides monitoring and observability for AWS resources and applications. It collects metrics, logs, and events, enabling you to monitor, troubleshoot, and optimize your AWS environment.
Table of Contents
Core Concepts
Metrics
Time-ordered data points published to CloudWatch. Key components:
- Namespace: Container for metrics (e.g.,
AWS/Lambda) - Metric name: Name of the measurement (e.g.,
Invocations) - Dimensions: Name-value pairs for filtering (e.g.,
FunctionName=MyFunc) - Statistics: Aggregations (Sum, Average, Min, Max, SampleCount, pN)
Logs
Log data from AWS services and applications:
- Log groups: Collections of log streams
- Log streams: Sequences of log events from same source
- Log events: Individual log entries with timestamp and message
Alarms
Automated actions based on metric thresholds:
- States: OK, ALARM, INSUFFICIENT_DATA
- Actions: SNS notifications, Auto Scaling, EC2 actions
Common Patterns
Create a Metric Alarm
AWS CLI:
# CPU utilization alarm for EC2
aws cloudwatch put-metric-alarm \
--alarm-name "HighCPU-i-1234567890abcdef0" \
--metric-name CPUUtilization \
--namespace AWS/EC2 \
--statistic Average \
--period 300 \
--threshold 80 \
--comparison-operator GreaterThanThreshold \
--evaluation-periods 2 \
--dimensions Name=InstanceId,Value=i-1234567890abcdef0 \
--alarm-actions arn:aws:sns:us-east-1:123456789012:alerts \
--ok-actions arn:aws:sns:us-east-1:123456789012:alertsboto3:
import boto3
cloudwatch = boto3.client('cloudwatch')
cloudwatch.put_metric_alarm(
AlarmName='HighCPU-i-1234567890abcdef0',
MetricName='CPUUtilization',
Namespace='AWS/EC2',
Statistic='Average',
Period=300,
Threshold=80.0,
ComparisonOperator='GreaterThanThreshold',
EvaluationPeriods=2,
Dimensions=[
{'Name': 'InstanceId', 'Value': 'i-1234567890abcdef0'}
],
AlarmActions=['arn:aws:sns:us-east-1:123456789012:alerts'],
OKActions=['arn:aws:sns:us-east-1:123456789012:alerts']
)Lambda Error Rate Alarm
aws cloudwatch put-metric-alarm \
--alarm-name "LambdaErrorRate-MyFunction" \
--metrics '[
{
"Id": "errors",
"MetricStat": {
"Metric": {
"Namespace": "AWS/Lambda",
"MetricName": "Errors",
"Dimensions": [{"Name": "FunctionName", "Value": "MyFunction"}]
},
"Period": 60,
"Stat": "Sum"
},
"ReturnData": false
},
{
"Id": "invocations",
"MetricStat": {
"Metric": {
"Namespace": "AWS/Lambda",
"MetricName": "Invocations",
"Dimensions": [{"Name": "FunctionName", "Value": "MyFunction"}]
},
"Period": 60,
"Stat": "Sum"
},
"ReturnData": false
},
{
"Id": "errorRate",
"Expression": "errors/invocations*100",
"Label": "Error Rate",
"ReturnData": true
}
]' \
--threshold 5 \
--comparison-operator GreaterThanThreshold \
--evaluation-periods 3 \
--alarm-actions arn:aws:sns:us-east-1:123456789012:alertsQuery Logs with Insights
# Find errors in Lambda logs
aws logs start-query \
--log-group-name /aws/lambda/MyFunction \
--start-time $(date -d '1 hour ago' +%s) \
--end-time $(date +%s) \
--query-string '
fields @timestamp, @message
| filter @message like /ERROR/
| sort @timestamp desc
| limit 50
'
# Get query results
aws logs get-query-results --query-id <query-id>boto3:
import boto3
import time
logs = boto3.client('logs')
# Start query
response = logs.start_query(
logGroupName='/aws/lambda/MyFunction',
startTime=int(time.time()) - 3600,
endTime=int(time.time()),
queryString='''
fields @timestamp, @message
| filter @message like /ERROR/
| sort @timestamp desc
| limit 50
'''
)
query_id = response['queryId']
# Wait for results
while True:
result = logs.get_query_results(queryId=query_id)
if result['status'] == 'Complete':
break
time.sleep(1)
for row in result['results']:
print(row)Create Metric Filter
Extract metrics from log patterns:
# Create metric filter for error count
aws logs put-metric-filter \
--log-group-name /aws/lambda/MyFunction
