Serverless Monitoring: Best Practices
5 Best Practices to Follow When Monitoring Serverless Applications
The landscape of application development and deployment is undergoing a monumental shift with the advent of serverless computing. Liberating developers from the burdensome task of managing infrastructure has unleashed a new era of creativity and efficiency in coding. However, this transformative technology’s distributed and event-driven nature introduces distinctive hurdles in monitoring. In this exploration, we will delve into the intricacies of serverless environments, anticipating the challenges and uncovering effective strategies to optimize monitoring for the future.
But, if you follow the best practices alongside specialized tools like AWS CloudWatch, Helios, Datadog, New Relic, and Prometheus, you can easily tackle those challenges. So, in this article, I will discuss 5 best practices you need to follow when monitoring serverless applications.
1. Define clear monitoring objectives and metrics
To create an effective monitoring strategy, define clear objectives and metrics. This involves identifying specific outcomes you want to achieve. Maintaining a clear roadmap helps you decide the information and metrics required to reach your goals before implementing the monitoring strategy.
For instance, to ensure the high availability of serverless functions, track metrics such as error rate, invocation count, and latency. Keep the error rate below the defined threshold, ensure successful invocations, and maintain latency within acceptable limits. Leverage tools like AWS CloudWatch, Helios, Datadog , or New Relic to monitor these metrics.
2. Choose the right monitoring tools and services
As serverless computing abstracts away infrastructure management, traditional monitoring approaches become inadequate for handling distributed, event-driven architectures. This is where dedicated tools and services for serverless monitoring step in. Leveraging specialized monitoring tools is a best practice, providing enhanced observability, real-time insights, and tailored features for the unique characteristics of serverless environments.
When choosing a monitoring tool, there are several key factors you should take into account:
- Scalability and performance: The tool should handle your applications’ scale and performance requirements.
- Integration with serverless platforms: The tool should have native integration support for serverless platforms like AWS Lambda.
- Ease of integration: How easily you can integrate the tool into your applications, like minimal code changes or agent-based instrumentation.
- Alerting and notification: The tool should be able to send notifications and integrate well with communication channels like email or Slack.
- Data visualization and dashboards: The tool should offer interactive and customizable dashboards for visualizing metrics, logs, and traces.
- Cost and pricing model: Make sure the pricing model suits your organization.
However, it isn’t easy to pick a monitoring tool since many are available in the market. Here are some of the leading tools and services you can use for serverless monitoring:
- AWS CloudWatch: A comprehensive monitoring and observability service AWS provides for serverless applications developed in AWS.
- AWS X-Ray: A powerful distributed tracing service to understand the flow of requests across serverless functions and services. This is also limited to serverless applications developed in AWS.
- Helios: Observability and troubleshooting platform that provides end-to-end visibility into application workflows, including Lambda functions, HTTP requests, Kafka, and RabbitMQ.
- Datadog: Monitoring and analytics platform that provides real-time visibility into your applications, including AWS Lambda functions.
3. Enable detailed logging and log aggregation
Troubleshooting and debugging are significantly enhanced with proper logging mechanisms. Detailed logs offer vital information needed to identify and resolve issues within your serverless functions. They help trace execution flow, track errors, and analyze application behavior during runtime.
To optimize the logging process, always opt for structured logs. Their predefined format with specific fields and values makes them easier to process and analyze systematically.
If you are using an AWS Lambda function, it automatically integrates with AWS CloudWatch. All you need to do is, select the Lambda function through the AWS console, navigate to the monitor tab, and click the View CloudWatch logs button.
You can also use Helios for logging, which lets you fetch logs from AWS CloudWatch with a single click.
Here are several best practices to follow when enabling detailed logging for your serverless applications:
Define log levels
Assigning distinct log levels to different logs provides developers with a valuable advantage: the ability to filter logs and concentrate on relevant information during analysis. In software development, the common log levels include “debug,” “info,” “warning,” “error,” and “critical.”
For instance, you can easily define log levels within a Lambda function using a third-party library like logging, enabling you to fine-tune your log output and streamline your debugging efforts effectively
import logging
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
def lambda_handler(event, context):
logger.debug("This is a debug log message.")
logger.info("This is an info log message.")
logger.warning("This is a warning log message.")
logger.error("This is an error log message.")
logger.critical("This is a critical log message.")Define log retention policies
The log retention period is when logs are stored before being destroyed. Compliance, auditing, or legal purposes may require longer retention periods, while shorter retention periods can help manage storage costs. You can define a retention period for CloudWatch logs through the Actions dropdown of the log group.
Implement log analysis and search
Use log analysis and search tools to query and filter logs effectively. Tools like Elasticsearch, Splunk, or CloudWatch Logs Insights provide powerful querying capabilities. If you are using AWS CloudWatch, select the log group you need to search and click on the View in logs insights button. It will open a tab like the one below where you can run custom queries.
Configure log alerts
Log-based alerts can generate real-time notifications when specific log events or error patterns occur. This allows you to act proactively and address potential issues. You can use a tool like Helios to define various custom labels, alert rules, and notifications for your application. Refer to the video below for more details on setting up Helios notifications.
4. Implement real-time monitoring and alerts
As mentioned earlier, employing alerts and notifications is a proactive approach to tackling application issues. However, alerts alone may not offer the real-time monitoring needed for serverless applications. To ensure continuous visibility into your serverless environment, it’s crucial to complement alerts with real-time monitoring techniques. By integrating both strategies, you can stay on top of your serverless ecosystem and swiftly respond to emerging challenges.
A proper combination of real-time monitoring and alerts can help you:
- Detect issues and anomalies as they happen.
- Proactively respond to issues before they escalate.
- Observe the performance and behavior of your serverless applications in real time.
- Identify performance bottlenecks and resource inefficiencies.
- Make informed decisions for optimizing resource allocation and improving application performance.
AWS CloudWatch is a powerful tool that provides real-time monitoring and alerts for various AWS services, including AWS Lambda. To continuously monitor a specific metric for your Lambda function in real time, you can easily set up a CloudWatch Alarm. When the metric surpasses the threshold you define, the alarm will be triggered, and you’ll receive timely notifications through your preferred channel, such as SMS, email, or Slack. This seamless integration of monitoring and alerting empowers you to stay informed and take prompt action whenever necessary.
The steps below show how to create a CloudWatch Alarm for a Lambda function:
Step 1 — Navigate to the Monitor tab of the Lambda function.
Step 2 — Open the metric to set up an Alarm in CloudWatch.
Step 3 — Click the bell icon.
Step 4 — Define the configuration, including the threshold, notification, and scaling options. Once the configuration is finalized, click the Create Alarm button.
In addition to AWS CloudWatch Alarms, you can use tools like AWS X-Ray and Helios to enable real-time monitoring for your serverless applications.
5. Automate monitoring and remediation tasks
Automating monitoring and remediation tasks streamlines the process of identifying and resolving issues. As serverless applications scale, manual handling of monitoring and remediation becomes impractical. Automation guarantees that monitoring and remediation seamlessly scale with the application, regardless of size and complexity. Moreover, it minimizes human errors and ensures consistent and accurate incident responses.
Here is how you can automate monitoring and remediation tasks:
Auto scaling policies
Define auto-scaling policies for Lambda functions to automatically scale resources based on predefined conditions, such as CPU utilization or request rates. It will adjust the number of function instances to handle varying workloads, ensuring optimal performance.
To define auto-scaling policies for AWS Lambda, you have several options: the AWS management console, CLI, or CloudFormation. For instance, you can use the following CLI commands to set the number of provisioned concurrency executions for the Lambda function:
aws lambda put-provisioned-function-concurrency --function-name my-function \
--provisioned-concurrent-executions 100Event-Driven Remediation
AWS EventBridge is a fully managed event bus service introduced to simplify the process of building event-driven applications. It allows you to create and manage event rules to detect specific events, such as API errors or system failures.
For example, the below AWS EventBridge rule defines an event pattern for monitoring AWS API Gateway execution state changes.
{
"source": "aws.api-gateway",
"detail-type": "API Gateway Execution State Change",
"detail": {
"status": "FAILED"
},
"targets": [
{
"arn": "arn:aws:lambda:us-east-1:123456789012:function:RemediationLambdaFunction"
}
]
}Self-Healing Mechanisms
Self-healing mechanisms can handle common errors and failures in the Lambda functions automatically For example, if a function encounters a temporary issue, it can retry the operation a few times before reporting an error:
pythonCopy code
import boto3
import time
def lambda_handler(event, context):
retries = while retries > 0:
try:
# Perform the main function logic here
result = some_function(event)
return result
except Exception as e:
# Log the error and retry after a short delayprint(f"Error: {str(e)}")
retries -= 1
time.sleep(1)
# If all retries fail, raise an error or invoke a remediation functionraise Exception("Function failed after retries.")Conclusion
Monitoring serverless applications is not just important; it is crucial for ensuring their peak performance and unwavering reliability. By diligently following the best practices and harnessing the power of specialized tools like AWS CloudWatch and Helios, developers can confidently conquer the challenges presented by serverless architectures’ distributed and event-driven nature.
With these methodologies and technologies, you can effectively monitor and manage your serverless applications with utmost precision and efficiency. Embrace these approaches, and your serverless deployments will thrive like never before.
Now armed with these invaluable insights, you can confidently optimize your monitoring strategy and elevate the performance of your serverless applications to new heights. Thank you for investing your time in absorbing this knowledge-rich article, and here’s to your future success in the realm of serverless computing!
