Common CloudWatch metrics and logs (for example, CPU utilization with Amazon EC2, queue length with Amazon RDS, 5xx errors with an Application Load Balancer [ALB])

What is the significance of monitoring CPU utilization for Amazon EC2 instances in CloudWatch, and how can it impact your application performance?

Monitoring CPU utilization is crucial because it helps in understanding the compute load on an EC2 instance. High CPU usage may indicate that the instance is under-provisioned and struggling to handle the workload, which can lead to degraded performance or even service outages. Conversely, consistently low CPU utilization might suggest over-provisioning, leading to unnecessary costs. By carefully monitoring CPU utilization, DevOps engineers can make informed decisions about scaling and cost optimization.

How can monitoring queue length in Amazon RDS with CloudWatch help maintain database performance, and what actions can be taken based on this metric?

Queue length in Amazon RDS represents the number of disk I/O operations that are waiting to be written to or read from the disk. Monitoring this metric helps in identifying bottlenecks in data processing and potential performance issues. A consistently high queue length could suggest the need for better I/O capacity or optimized query performance. Actions such as increasing provisioned IOPS, optimizing queries, or scaling up the database instance size might be considered based on this metric.

What are 5xx errors in the context of an Application Load Balancer (ALB) and why is it important to monitor them using CloudWatch?

5xx errors represent server-side errors that occur when the ALB receives a request but cannot get a proper response from the target’s back-end servers. Monitoring these errors is critical because they indicate issues with the application or infrastructure that need immediate attention to ensure service availability and to provide a smooth user experience. High numbers of 5xx errors may require investigation into application code, server health, or capacity issues.

In CloudWatch, how would you set up an alarm for high CPU utilization for an EC2 instance, and what actions would you configure in response to this alarm?

To set up an alarm in CloudWatch for high CPU utilization on an EC2 instance, navigate to the CloudWatch dashboard, create a new alarm, and specify the EC2 metric for CPU Utilization. Define the threshold that signifies high CPU usage and the period over which the metric should be evaluated. In response to the alarm, you could configure actions such as sending notifications, triggering an Auto Scaling policy to scale out the EC2 fleet, or executing an AWS Lambda function to perform an automated task.

Can you explain the difference between CloudWatch Logs and CloudWatch Metrics, and provide examples where each would be used?

CloudWatch Logs are used for monitoring, storing, and accessing log files from EC2 instances, AWS CloudTrail, and other sources. They provide detailed information about specific events and are useful for troubleshooting. For example, they can be used to track application errors or security incidents. CloudWatch Metrics, on the other hand, provides a more aggregate view of system performance, such as CPU utilization, network I/O, or disk read/write operations. These are used for real-time monitoring of resources and setting alarms based on thresholds.

How can you use CloudWatch to monitor memory usage on your Amazon EC2 instances, given that memory utilization is not a metric provided by AWS out of the box?

Memory usage monitoring on EC2 instances requires custom metrics. You can use the CloudWatch agent or custom scripts to collect memory usage data from the instance and push it to CloudWatch as a custom metric. Once the data is in CloudWatch, you can view, graph, and set alarms on memory utilization just like any other metric.

What CloudWatch metric would you use to monitor the read/write throughput of your Amazon RDS instance, and why is this important?

The ReadThroughput and WriteThroughput metrics in CloudWatch should be used to monitor the I/O throughput of an Amazon RDS instance. Monitoring these metrics is important because they provide insights into the volume of data the application reads from and writes to the database, which is directly related to the database performance and the application’s responsiveness.

How can CloudWatch Logs help you identify and diagnose application issues, and what features does CloudWatch provide to search and analyze log data?

CloudWatch Logs helps in identifying and diagnosing application issues by allowing the collection and analysis of log data. It provides features like log groups and log streams to organize logs, and you can search and filter log data using query language. Moreover, CloudWatch Logs Insights provides an interactive interface to explore, analyze, and visualize your log data, helping you quickly find the root cause of issues.

What are some common CloudWatch metrics you would monitor for an Amazon DynamoDB table and why?

Common CloudWatch metrics for DynamoDB include ReadCapacityUnits and WriteCapacityUnits (providing insight into provisioned capacity utilization), ConsumedReadCapacityUnits and ConsumedWriteCapacityUnits (showing the level of consumed capacity), ThrottledRequests (indicating whether requests are being throttled due to capacity limits), and ConditionalCheckFailedRequests (useful to monitor failed conditional writes). Monitoring these metrics is important to ensure that your DynamoDB table has sufficient capacity to meet demand and maintain performance.

What benefits does integrating Amazon CloudWatch with AWS Auto Scaling provide?

Integrating CloudWatch with AWS Auto Scaling allows you to dynamically adjust the number of instances in response to real-time changes in demand, based on CloudWatch metrics like CPU utilization, network I/O, and custom metrics. This ensures that you maintain optimal application performance and cost-efficiency by scaling the infrastructure automatically according to defined policies.

Explain a scenario where you would use CloudWatch Events and the actions you could automate following a specific event.

CloudWatch Events can be used to respond to state changes in AWS resources. For example, you could create an event rule to trigger an AWS Lambda function or send an SNS notification when an Auto Scaling group launches or terminates EC2 instances. This helps automate workflows and quickly respond to infrastructure changes without manual intervention.

When configuring an alarm, why is it essential to set the appropriate period for a CloudWatch metric, and what considerations should you take into account?

Setting the appropriate period for a CloudWatch metric is essential because it defines the time length over which data points are aggregated into a single metric for evaluation. If the period is too short, the alarm may trigger too often, including false positives. If the period is too long, you may miss quick spikes or drop-offs in performance. You should consider the nature of the workload, metric volatility, and the responsiveness required when choosing the period.