Log e metriche di CloudWatch
Utilizzi Amazon CloudWatch per raccogliere, monitorare e analizzare log e metriche delle prestazioni delle funzioni Lambda e di altri servizi AWS
Log e metriche di CloudWatch è una lezione Serverless AWS Lambda Development gratuita su CoddyKit. Questa è la lezione 1 di 4. Puoi leggere la lezione completa qui gratuitamente — poi esercitati direttamente nel browser con un editor di codice integrato e un tutor IA disponibile 24/7. Fa parte del percorso di apprendimento Serverless AWS Lambda Development, e i tuoi progressi si sincronizzano tra il web e l'app CoddyKit. Il corso Serverless AWS Lambda Development include 4 lezioni in totale.
Parti di questa lezione non sono ancora state tradotte e vengono mostrate in inglese.
Your Serverless Watchdog: CloudWatch
Welcome! In this lesson, we'll dive into Amazon CloudWatch, AWS's powerful monitoring and observability service. It's your eyes and ears for understanding what's happening with your AWS resources and applications.
For serverless applications built with AWS Lambda, CloudWatch is absolutely essential to ensure everything runs smoothly.
Why Monitoring Matters for Lambda
Lambda functions execute quickly and often, sometimes hundreds or thousands of times per second. Without proper visibility, it's incredibly hard to know if they're working correctly or encountering issues.
CloudWatch helps you:
- Monitor function health and performance.
- Debug errors efficiently when things go wrong.
- Optimize resource usage and control costs.
Diving into CloudWatch Logs
The first key component is CloudWatch Logs. This is where all your Lambda function's text-based output, runtime messages, and errors are stored. Think of it as a centralized place for all your application's 'print statements' and system messages.
Every time your Lambda function runs, it generates logs. These logs are automatically sent to CloudWatch Logs.
Lambda's Automatic Logging
The great news is you don't need to configure anything special for basic logging. When your Lambda function executes, anything printed to standard output (like console.log in Node.js or print() in Python) is automatically captured.
These captured messages, along with execution details (like start/end times and billing info), are then pushed to CloudWatch Logs without any extra setup from you.
Organizing Your Logs: Groups & Streams
CloudWatch Logs organizes logs into two main concepts:
- Log Group: A container for log streams that share the same retention, monitoring, and access control settings. Each Lambda function typically gets its own Log Group (e.g.,
/aws/lambda/your-function-name). - Log Stream: A sequence of log events from a single source. Each invocation or concurrent execution of your Lambda function typically creates a new log stream within its log group.
Adding Logs in Your Lambda Code
You can add custom log messages to your Lambda function code. This helps you trace execution flow, understand data, and debug issues. Here's a simple Python example using the standard logging module:
import json
import logging
# Configure logging for the Lambda function
logger = logging.getLogger()
logger.setLevel(logging.INFO)
def lambda_handler(event, context):
logger.info("Lambda function started processing event.")
logger.info(f"Received event: {json.dumps(event)}")
message = "Hello from CoddyKit Lambda!"
logger.info(f"Preparing response: {message}")
return {
'statusCode': 200,
'body': json.dumps(message)
}
# Example of how you might test this locally
if __name__ == "__main__":
# Simulate a simple event object
test_event = {"action": "greet", "name": "Learner"}
# The 'context' object is usually provided by Lambda, we can mock it or pass None for basic tests.
result = lambda_handler(test_event, None)
print("--- Lambda Handler Output (Local Test) ---")
print(result)Introducing CloudWatch Metrics
While logs tell you what happened in detail, CloudWatch Metrics tell you how well your functions are performing. Metrics are time-ordered sets of numerical data points that represent a specific measurement.
AWS automatically collects metrics for your Lambda functions, giving you high-level insights into their operational health and performance trends.
Essential Lambda Performance Metrics
Some crucial metrics that CloudWatch automatically collects for your Lambda functions include:
- Invocations: The total number of times your function was triggered.
- Errors: The number of times your function failed during execution.
- Duration: The execution time of your function, measured in milliseconds.
- Throttles: The number of times your function invocations were limited due to concurrency limits.
Monitoring these helps you quickly identify performance bottlenecks or potential issues.
Visualizing Metrics in the Console
You can view these performance metrics as interactive graphs in the CloudWatch console. Simply navigate to the 'Metrics' section, then select 'Lambda', and choose the desired metric for your specific function.
This visual representation makes it easy to spot trends, sudden spikes, or drops in performance, allowing you to react quickly to operational changes and optimize your functions.
Quick Check: Logs vs. Metrics
Let's test your understanding of CloudWatch Logs and Metrics for AWS Lambda.
Recap: CloudWatch for Serverless Insight
We've explored how Amazon CloudWatch is your go-to service for monitoring AWS Lambda functions. You learned about:
- CloudWatch Logs: For collecting, storing, and analyzing detailed runtime messages and custom outputs from your functions.
- CloudWatch Metrics: For tracking key performance indicators like invocations, errors, duration, and throttles.
Together, logs and metrics provide a comprehensive picture of your serverless application's health and performance, empowering you to debug and optimize effectively.
Domande Frequenti
La lezione «Log e metriche di CloudWatch» è gratuita?
Sì — il testo completo di «Log e metriche di CloudWatch» è gratuito qui sul web. Per esercitarvi in modo interattivo (un editor di codice integrato e un tutor IA 24/7) e sbloccare il resto del corso Serverless AWS Lambda Development, passa a CoddyKit PRO. Il corso Serverless AWS Lambda Development include 4 lezioni in totale.
Cosa imparerò in «Log e metriche di CloudWatch»?
Utilizzi Amazon CloudWatch per raccogliere, monitorare e analizzare log e metriche delle prestazioni delle funzioni Lambda e di altri servizi AWS Eserciti Serverless AWS Lambda Development con codice pratico che esegui direttamente nel browser, e un tutor IA 24/7 risponde alle tue domande mentre lavori sulla lezione.
Ho bisogno di esperienza per iniziare Serverless AWS Lambda Development?
Non è richiesta alcuna esperienza precedente. Serverless AWS Lambda Development su CoddyKit è strutturato per principianti e studenti avanzati, quindi puoi iniziare da qui o dall'inizio e procedere al tuo ritmo. Questa è la lezione 1 di 4.
Quanto tempo richiede la lezione «Log e metriche di CloudWatch»?
La maggior parte delle lezioni CoddyKit richiede circa 5–10 minuti. Ogni lezione è breve e interattiva, quindi fai progressi costanti e riprendi esattamente da dove hai lasciato su web e app.
Posso scrivere ed eseguire codice in questa lezione Serverless AWS Lambda Development?
Sì. Ogni lezione Serverless AWS Lambda Development include un editor di codice integrato, quindi scrivi ed esegui codice reale direttamente nel tuo browser e ricevi feedback istantaneo dall'IA — nessuna configurazione locale necessaria.
Tutte le lezioni di questo corso
- Log e metriche di CloudWatch
- Gestione degli errori e retry
- Debug delle applicazioni serverless
- Metriche personalizzate e allarmi CloudWatch