AWS CloudWatch
for Better Monitoring
Proactive monitoring and logging are essential for preventing issues and minimizing downtime in your cloud infrastructure. Application and workload owners can often forget about logging and monitoring or inconsistently configure and implement it. This means that workloads enter production with limited observability, which causes delays in identifying issues and increases the time taken to troubleshoot and resolve them. At a minimum, your logging and monitoring solution must address the systems layer for the operating system (OS)-level logs and metrics, in addition to the application layer for application logs and metrics. By continuously monitoring applications, developers and IT professionals can detect issues early, troubleshoot problems, and improve the user experience. AWS CloudWatch is a monitoring service for AWS cloud resources and the applications running on AWS. It collects and tracks metrics and log files, sets alarms, and automatically reacts to changes in AWS cloud resources.
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There are many ways to leverage AWS CloudWatch for monitoring and logging. But, these are some of the best ones for collecting, analyzing, and visualizing application and infrastructure metrics:
Data Collection with AWS CloudWatch Logs
1- Use detailed monitoring: Enable detailed monitoring for EC2 instances, S3 buckets, and other AWS resources to gather data at one-minute intervals.
2- Custom metrics: Publish custom application metrics using the CloudWatch SDK or CloudWatch Agent to track application-specific parameters.
3- Searching and analyzing logs: After your logs and metrics are captured into a consistent format and location, you can search and analyze them to help improve operational efficiency, in addition to identifying and troubleshooting issues. Collect and store logs using CloudWatch Logs and analyze them for centralized management and insights into application behavior and potential security threats. This practice is vital for troubleshooting and understanding the context of operational issues.
4- Enable enhanced metrics: Use enhanced monitoring for services like RDS, ECS, and Lambda for more granular insights.
5- Tagging: Apply consistent tagging to resources for easier aggregation and filtering of metrics.
Data Analysis with AWS Monitoring Service
1- Identify and monitor key metrics: Define a set of essential metrics that align with your application’s performance and health objectives. Performing one-time and automated analysis of important metrics helps you detect and resolve issues before they impact your workloads. AWS CloudWatch makes it easy to graph and compare multiple metrics by using multiple statistics over a specific time period. You can use AWS CloudWatch to search across all metrics with the required dimension values to find the metrics that you need for your analysis. You can use CloudWatch alarms to reduce manual monitoring in your workloads or applications. The CloudWatch Alarms can notify you of any abnormalities or performance issues or when thresholds are breached on critical metrics. You should begin by reviewing the metrics that you are capturing for each workload component and determine the appropriate thresholds for each metric. Make sure that you identify which team members must be notified when a threshold is breached. You should establish and target distribution groups, rather than individual team members. This proactive monitoring can help in quick troubleshooting and maintaining optimal application performance.
2- Enable logging for all critical resources: Ensure that logging is enabled for all key components of your infrastructure, including instances, applications, and services.
3- Establish meaningful log formats with CloudWatch Application Insights: Define log formats that capture relevant information for troubleshooting and analysis. Use CloudWatch Logs insights to query logs and perform analysis. Create queries to identify error patterns, trends, and performance issues. Setting up monitoring through CloudWatch Application Insights can also help application teams proactively align to operations and reduce mean time to recovery (MTTR). CloudWatch Application Insights can help reduce the effort required to establish application-level logging and monitoring. It also provides a component-based framework that assists teams in dividing logging and monitoring responsibilities.
4- Create dashboards for centralized monitoring: Develop dashboards that provide a holistic view of your cloud infrastructure’s health and performance. Dashboards help you quickly focus on areas of concern for applications and workloads. CloudWatch provides automatic dashboards and you can also easily create dashboards that use CloudWatch metrics. CloudWatch dashboards provide more insight than viewing metrics in isolation because they help you correlate multiple metrics and identify trends. You can also use widgets to display graphs, alarms, and logs.
5- Metric math: Use metric math to create new metrics based on mathematical expressions.You can use metric math to help calculate metrics in formats and expressions that are relevant for your workloads. The calculated metrics can be saved and viewed on a dashboard for tracking purposes. Combine multiple metrics to analyze relationships and trends.
Visualization
1- Use heat maps: Heat maps in CloudWatch Dashboards show you the data trends over time. This visual representation helps in understanding the interaction between different services and identifying potential bottlenecks or points of failure. Keeping an up-to-date service map is essential for effective troubleshooting and optimizing application performance. So, regularly review and update dashboards and alarms based on evolving application needs.
2- Use Grafana: AWS Managed Grafana helps you create visualizations and dashboards from many different data sources, including CloudWatch metrics. AWS Managed Grafana integrates with AWS Organizations to enable you to read data from AWS sources such as CloudWatch and AWS OpenSearch Service across all your accounts. This makes it possible to create dashboards that display visualizations using data across your accounts. Integrate CloudWatch with Grafana for advanced visualization options. Create custom dashboards with detailed graphs and alerts.
3- ServiceLens: Utilize ServiceLens for tracing and monitoring applications, combining metrics, logs and traces. You can use CloudWatch ServiceLens to correlate traces, metrics, logs, and alarms for diagnosing issues. You should also consider including additional dimensions in metrics and identifiers in logs for your workloads to help you quickly search for and identify issues across systems and services.
Proactive Problem Identification with CloudWatch Metrics and Alarms
1- Automating responses to alarms or events by CloudWatch Event: This can range from scaling resources to address load changes, to triggering Lambda functions for custom remediation actions (e.g, restarting services on failure). Leverage AWS SDKs and the CloudWatch API for automation and custom integrations.
2- Anomaly detection: Enable anomaly detection to automatically set dynamic thresholds based on historical data. You can use CloudWatch anomaly detection if you are unsure about the thresholds to apply for a particular metric or if you want an alarm to automatically adjust the threshold values based on observed historical values. CloudWatch anomaly detection is particularly useful for metrics that might have regular, predictable changes in activity, for example, daily purchase orders for same-day delivery increasing before a cutoff time. Anomaly detection enables thresholds that adjust automatically and can help reduce false alarms. You can enable anomaly detection for each metric and statistic, and configure CloudWatch to alarm based on outliers.
3- Log-based alarms: A log pattern set is a collection of log patterns to search for based on regular expressions, along with a low, medium, or high severity for when the pattern is detected. For metrics, you choose the metrics to monitor for each component from a list of service-specific and supported metrics. For alarms, CloudWatch Application Insights automatically creates and configures standard or anomaly detection alarms for the metrics being monitored. Set up alarms based on log patterns, for example, specific error messages.
Troubleshooting
1- Root cause analysis: Use CloudWatch Logs and insights to drill down into logs during incidents. Correlate logs and metrics for a comprehensive understanding of issues. For this, you should consider how logging and monitoring data is correlated so that you can quickly identify the relevant logging and monitoring data to diagnose specific issues.
2- AWS X-Ray: You can use AWS X-Ray to trace your application requests across multiple components. X-Ray samples and visualizes requests on a service graph when they flow through your application components and each component is represented as a segment. X-Ray generates trace identifiers so that you can correlate a request when it flows through multiple components, which helps you view the request from end to end. You can further enhance this by including annotations and metadata to help uniquely search for and identify the characteristics of a request. Integrate AWS X-Ray for tracing requests through applications to identify performance bottlenecks.
Cost Management by CloudWatch Metrics and Alarms
1- Optimize data collection: Collect only necessary metrics and logs to manage costs effectively.
2- Monitor usage: Monitor your AWS usage with CloudWatch to identify underutilized resources or opportunities for cost optimization, such as modifying instance sizes or leveraging reserved instances. If you use the embedded metric format to create CloudWatch metrics, you can query your embedded metric format logs to generate one-time metrics by using the supported aggregation functions. This helps reduce your CloudWatch monitoring costs by capturing data points necessary to generate specific metrics on an as-needed basis, instead of actively capturing them as custom metrics. Set budgets and alarms for cost management.
Final Note
By following these best practices, AWS engineers can effectively utilize CloudWatch to maintain visibility into their cloud infrastructure, ensuring optimal system health, performance, and security. It involves setting up comprehensive monitoring, creating insightful visualizations, and leveraging automated alerts and analyses. Implementing these best practices can lead to proactive problem identification and more efficient troubleshooting.
About The Author
Pouya Nourizadeh
Enterprise AWS Architect
Founder, Bringdev
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