Serverless Monitoring Best Practices
Serverless monitoring has significantly transformed the approach of building and deploying applications, providing unparalleled scalability, cost savings and operational overhead.
This shift to serverless architecture introduces new monitoring challenges. Nowadays, it is very rare that traditional monitoring techniques, with short-lived functions and managed infrastructure, cater well to monitoring serverless applications. As a result, serverless environments require a different understanding of monitoring, along with key strategies that can directly impact the health, performance and cost of your serverless applications.
The Observability Trio: Logs, Metrics and Traces
At the heart of effective serverless monitoring lies the ‘observability trio’ outlined below:
- Comprehensive Logging: Logs are your primary source to see what’s happening inside your serverless functions.
- Structure Your Logs Properly: Avoid using console.log statements with random strings. Instead, adopt a standardized structure logging format — preferably in JSON. This ensures that your logs are machine-readable and easily parsed, queried and analyzed. Include essential contextual data such as ‘requestId’, ‘functionName’, ‘timestamp’, ‘logLevel’ and other business data.
- Centralized Logging Aggregation: The coupling of serverless functions generates logs independently for basic debugging. Thus, a centralized logging service comes in handy — services such as AWS CloudWatch Logs, Middleware or Elastic Stack. Through these services, you get a reliable single source of truth for all logging data in your application, allowing for substantial searching, filtering and analytics.
- Strategic Utilization of Logging Levels: Different logging levels (DEBUG, INFO, WARN, ERROR or FATAL) should be implemented to control the verbosity of logs. Logs such as DEBUG should be enabled only for debugging, especially in a production environment, which should primarily focus on WARNING and ERROR messages.
- Adequate Log Retention: Log retention policies should be defined such that they give an optimum balance between cost and access to data. Longer retention should be given for important logs, while others can be deleted earlier.
- Meaningful Metrics: Metrics provide quantitative data about your function’s performance and resource utilization.
- Core Function Metrics: Key performance metrics (KPIs) that are made available by your cloud provider (for instance, AWS CloudWatch for Lambda and Azure Monitor for Azure Functions) regarding:
- Invocations: How frequently is your function triggered
- Errors: Number of failed invocations
- Duration/Latency: Average execution time of your function, and the time taken in the process (average, p90 or p99)
- Cold Starts: The incidence and duration of cold starts may affect the experience of the users
- Memory Usage: How much memory is consumed by your function
- Concurrency: Number of concurrent executions
- Throttles: When your function exceeds its concurrency limits
- Customization of Business Metrics: Your code must not only track platform metrics but also measure specific business metrics that matter to you. These could include actions such as payment gateways, adding items to carts or making a call to third-party services.
- Dashboards With Proper Insights: Clear and intuitive dashboards that display key metrics and real-time insights are extremely helpful. They provide an overview of serverless application health, enabling you to quickly identify trends, anomalies or potential system bottlenecks.
- Distributed Tracing: A request in a busy serverless architecture would at times traverse a multitude of functions, APIs and services. Distributed tracing can follow requests across all these areas of interest.
- End-to-End Visibility: Follow a request from its first entrance point (like an API gateway) down the chain of function invocations and interactions with outside services (databases, queues or external APIs).
- Correlation IDs: Enabling unique identifiers that follow a request will allow logging and metrics from disparate services to be forced into one particular transaction.
- Identify Bottlenecks and Dependencies: Tracing helps identify delays and determines which services contribute to performance degradation. This helps in optimizing complex serverless workflows.
- Tracing Tools: Leverage tools from cloud providers — such as AWS X-Ray or third-party solutions like OpenTelemetry or Middleware — for enriched distributed tracing capabilities.
Advanced Strategies and Best Practices
Beyond the core trio, consider these advanced strategies for a truly effective serverless monitoring setup:
Cost Monitoring and Optimization: Serverless costs directly depend on usage.
- Granular Cost Monitoring: Measure invocation counts, duration and memory usage granularly to know which ones drive costs.
- Budget Alerts: Set up budget alerts so that you are notified when costs become unreasonable.
- Right-Sized: Check memory usage to right-size your function configurations and avoid paying for more than what the function requires.
- Pinpoint Inefficient Patterns: Monitoring can expose functions that have markedly different invocation counts or duration — the above factors may be architecturally inefficient.
Automated Remediation: The responses should be automated as much as possible, rather than merely being forwarded.
- Self-Healing: Automated remedial actions should normally be established for simple and predictable problems, such as triggering another Lambda function to clear the cache or re-processing.
- Incidence Management Linkup: Create a pathway from monitoring alerts to incident management utilities like PagerDuty or Opsgenie. These are then tightly integrated into highly efficient on-call rotations and escalation procedures.
Synthetic Monitoring: Simulates user interaction with your serverless application to detect issues before customers experience them.
- API Endpoint Checks: Ensure your functions are reachable by periodically pinging your API Gateway endpoints to verify they respond appropriately.
- Complete User Journeys: Use synthetic transactions that emulate a typical user flow, such as user login, product search or checkout, to verify that the entire application chain is operating as intended.
These were some of the best practices for monitoring serverless applications and getting the best insights from them.
