In Part 1 of this series, we looked at the basics of CloudWatch metrics and one example of how you can leverage CloudWatch metrics to troubleshoot performance issues on AWS. In Part 2, we delved a bit deeper into some more examples and scenarios that allowed us to get a better understanding of how to leverage CloudWatch metrics.
In this third piece, we are going to take a step back. Whether you’re an AWS novice or an expert, identifying WHICH metrics to look at when troubleshooting can pose a real challenge. In this piece, we will look at some strategies and best practises to help you identify the right metrics for troubleshooting performance issues on AWS.
So, let’s dive in!
1. The bigger picture: Application Architecture
Before diving into metrics and troubleshooting performance issues in AWS, it’s essential to have a comprehensive understanding of your application’s architecture. Identify the key components, e.g., where is your compute layer hosted, where is your database, your storage, etc. Your architecture acts as a blueprint that outlines how different components (services, databases, etc.) interact to deliver your application’s functionality. By comprehending this blueprint, you can map potential performance issues to specific components and identify the relevant CloudWatch metrics for each.
2. Identify the affected service
Is it a slow website, sluggish database queries, or high latency in your Lambda functions? When troubleshooting performance issues in your AWS environment, identifying the affected service is a crucial step. It’s like being a detective at a crime scene — knowing where to look is only half the battle. CloudWatch offers a vast array of metrics across different categories like compute, network, database, and more. Knowing the affected service allows you to filter out irrelevant categories and focus on the metrics most likely to pinpoint the issue. For example, CPU utilization for EC2 instances wouldn’t be relevant if you’re investigating slow database queries.
3. Identify Common Performance Issues and Related Metrics
Understanding common problems that can arise and the relevant CloudWatch metrics that can help diagnose these issues is crucial when looking into performance problems in your AWS environment. By understanding these bottlenecks and their corresponding CloudWatch metrics, you can swiftly determine possible causes and take corrective action. For example:
For High Latency or Slow Performance, you need to look at:
- Elastic Load Balancer (ELB): TargetResponseTime
- API Gateway: Latency
- EC2 Instances: CPUUtilization, DiskReadOps, DiskWriteOps, NetworkIn, NetworkOut
- RDS Instances: ReadLatency, WriteLatency
For High Error Rates, you need to look at:
- ELB: HTTPCode_ELB_4XX_Count, HTTPCode_ELB_5XX_Count
- API Gateway: 4XXError, 5XXError
- Lambda: Errors, Throttles
For Traffic Spikes or Sudden Increase in Load:
- ELB/API Gateway: RequestCount
- EC2 Instances: NetworkIn, NetworkOut
- RDS Instances: DatabaseConnections, NetworkReceiveThroughput, NetworkTransmitThroughput
A grasp of your application architecture and common performance pitfalls empowers you to swiftly identify the right CloudWatch metrics for troubleshooting. Over time, this process becomes more intuitive, allowing you to troubleshoot efficiently and maintain optimal performance for your AWS environment.
4. Leverage Existing CloudWatch Documentation
CloudWatch documentation serves as your trusty roadmap when navigating the vast world of CloudWatch metrics. It helps you make sense of the data, find the right metrics for troubleshooting, and fix performance problems in your AWS environment. Here’s how CloudWatch documentation can assist:
- Metric Descriptions provide a clear explanation of what it represents and how it’s measured.
- Dimensional Breakdown often details the dimensions associated with each metric. Understanding dimensions allows you to filter and analyze metrics with greater granularity.
- Best Practices: CloudWatch documentation outlines best practices for collecting, monitoring, and analyzing metrics.
5. Follow the User Experience
In the realm of AWS performance troubleshooting, User Experience is regarded as the “Golden Signal”. This underscores the paramount importance of focusing on metrics that directly or indirectly impact how your users interact with your applications. Ultimately, the success of your applications hinges on user satisfaction. A slow website, unresponsive interface, or delayed responses can lead to frustration and user churn.
CloudWatch offers various metrics that directly or indirectly impact user experience. Some key examples include:
- Website Load Time is the time it takes for a web page to fully load on a user’s device. Slow load times can lead to user abandonment and negatively impact conversion rates.
- Database Query Latency is the time it takes for a database to respond to a query. High latency can result in sluggish application performance and delayed responses for users.
- API Response Times is the time it takes for your API to respond to a request. Slow API response times can hinder the overall performance of applications that rely on APIs.
- Application Error Rates are the frequency of errors encountered by users within your applications. Frequent errors can disrupt user workflows and damage trust in your services.
6. Monitor Dependencies and Downstream Services
Application problems can ripple outwards. Dependencies (like databases and message queues) and downstream services (what your application interacts with) can significantly impact overall performance and reliability. For example, if an application is slow, check not just the EC2 metrics but also the RDS metrics if your application relies on a database.
Key dependencies and downstream services to monitor include databases, message queues, caches, storage, networking component etc. By keeping an eye on these components, you can quickly identify and address performance issues that may not be immediately apparent from primary application metrics alone.
7. Compare Against Historical Data
For troubleshooting and monitoring your AWS environment, comparing current metrics to historical data is key. This reveals trends and anomalies, helping you distinguish between normal fluctuations and potential issues requiring attention. Comparing metric data against historical data is important for:
- Establishing a Baseline: Historical data helps establish normal operating conditions. Comparing current metrics against this baseline allows you to determine if the current performance is within expected ranges.
- Identifying Anomalies: Anomalies are data points that deviate significantly from the norm. By comparing current metrics with historical data, you can quickly spot unusual behaviour that might indicate issues.
- Understanding Trends: Trends show the general direction in which a metric is moving over time. Identifying trends helps you anticipate future behaviour, such as increasing resource usage that might eventually lead to performance bottlenecks if not addressed.
By following these strategies and best practices, you can transform CloudWatch metrics from a vast dataset into a powerful troubleshooting tool. With a targeted approach to metric selection, you can acquire more insight into the performance of your AWS environment, spot possible bottlenecks early on, and guarantee a seamless and effective user experience. Remember, effective troubleshooting is an ongoing process. With more AWS resources and CloudWatch expertise under your belt, you’ll become more adept at picking the pertinent metrics for every circumstance.
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