Amazon Web Services (AWS) CloudWatch is a powerful monitoring and observability tool that provides a comprehensive suite for tracking the performance and health of your AWS resources and applications. While AWS CloudWatch comes pre-configured with a variety of default metrics for resources like EC2, RDS, and S3, it also allows users to create custom metrics and manage logs for deeper insights into their infrastructure.

Custom metrics are user-defined metrics that go beyond AWS's default monitoring options, allowing for tailored data collection that fits specific business needs. Additionally, AWS CloudWatch Logs offers robust logging capabilities, enabling the aggregation, monitoring, and real-time analysis of log data. By combining custom metrics with efficient log management, you can gain end-to-end visibility into your AWS environment.

This article will provide a step-by-step guide to creating custom CloudWatch metrics, best practices for log management, and how to leverage these capabilities for optimizing performance and troubleshooting in AWS.

Overview of Amazon CloudWatch

Before diving into custom metrics and logs, let’s understand the core features of Amazon CloudWatch.

What is Amazon CloudWatch?

Amazon CloudWatch is a monitoring service for AWS cloud resources and applications. It allows you to collect, access, and analyze performance data in the form of logs and metrics. CloudWatch is used for:

• Monitoring AWS resources like EC2 instances, RDS databases, Lambda functions, and more.
• Setting alarms to notify users when performance thresholds are breached.
• Log management for system, application, and custom log data.
• Visualization of metrics using dashboards.

Key Features of CloudWatch

• Metrics: Data points collected over time to measure the performance of resources.
• Logs: Centralized logging for system, application, and custom logs.
• Alarms: Notifications are triggered when a metric crosses a defined threshold.
• Dashboards: Visual representation of metrics for real-time monitoring.
• Events: Real-time monitoring and response to events across AWS services.

Custom CloudWatch Metrics

What Are Custom Metrics?

AWS CloudWatch provides default metrics for most AWS services. However, if you want to monitor a specific performance indicator not covered by these defaults such as memory utilization on EC2 instances or custom application-level metrics you can create custom metrics. Custom metrics allow you to track any measurable data you can send to CloudWatch via the AWS CLI, SDKs, or APIs.

Benefits of Using Custom Metrics

• Granular Monitoring: Track performance metrics specific to your application or business.
• Enhanced Visibility: Monitor non-default metrics like memory usage or disk I/O.
• Tailored Alerts: Set alarms and notifications based on business-specific performance indicators.

Creating Custom Metrics

Creating custom CloudWatch metrics involves publishing data to CloudWatch using the PutMetricData API. Here’s a step-by-step process to create a custom metric:

Identify the Metric

Decide which metric you want to monitor. For example, if you're tracking memory usage on an EC2 instance, you’ll need a way to extract this data using tools like free, top, or vmstat on Linux.

Set Up an IAM Role

Ensure that the EC2 instance or resource you're working with has an IAM role with the necessary permissions to publish metrics to CloudWatch. The policy should include the cloudwatch:PutMetricData permission.
Version: 2012-10-17,
Statement: 
Effect: Allow,
Action: cloud watch: PutMetricData,
Resource

SWrite a Script to Collect the Data

You can use a script to collect the custom metric data and send it to CloudWatch. Below is an example of a Python script using Boto3, the AWS SDK for Python, to send custom memory usage metrics.

import psutil
import boto3

Initialize CloudWatch client
cloudwatch = boto3.client('cloud watch)

Collect memory usage data
memory usage = psutil.virtual memory().percent

Publish custom metric to CloudWatch
cloud watch.put metric data
Namespace=CustomMetrics,
MetricData=
MetricName: memory usage,
Dimensions: 
Name: InstanceId,
Value: i-0123456789abcdef0
Unit: Percent,
Value: memory usage

print(Memory usage metric sent to CloudWatch.

Automate the Process

Use cron jobs (Linux) or Task Scheduler (Windows) to automate the execution of this script at regular intervals, for example, every minute.

Visualize Custom Metrics in CloudWatch

Once your custom metrics are published, you can visualize them on the CloudWatch dashboard:

• Go to the CloudWatch Console.
• Navigate to Metrics.
• Select the custom namespace (e.g., CustomMetrics).
• Choose the metric (e.g., MemoryUsage) and add it to a graph.

Managing CloudWatch Logs

Logs are vital for troubleshooting issues, gaining insights into application behavior, and ensuring compliance. AWS CloudWatch Logs provides a centralized solution for managing and analyzing log data from AWS resources and custom applications.

Key Benefits of CloudWatch Logs

• Centralized Log Management: Consolidate logs from multiple AWS resources and applications in one place.
• Real-Time Monitoring: View log streams in real-time for debugging and troubleshooting.
• Log Retention: Set log retention policies to manage storage costs.
• Custom Metrics from Logs: Create metrics from specific log events for more precise monitoring.

Setting Up CloudWatch Logs

Create a Log Group

Log groups are containers for log streams that share the same retention, monitoring, and access control settings.

• Go to the CloudWatch Console.
• Click on Logs in the left-hand panel.
• Choose Create log group.
• Specify a name for your log group (e.g., /var/log/myapp).

Install the CloudWatch Agent

To push logs from EC2 instances or on-premise servers to CloudWatch, you'll need to install and configure the CloudWatch Logs Agent.

For Amazon Linux:
sudo yum install amazon-cloud watch-agent

Configure the CloudWatch Agent

Create a configuration file for the CloudWatch agent to specify which logs to monitor. Below is an example configuration that monitors /var/log/messages and sends it to CloudWatch.
logs: 
logs collected: 
files: 
collect list: 

file path: /var/log/messages,
log group name: /var/log/messages,
log stream name: instance id

Log Streams

Log streams are sequences of log events from a specific source, such as an EC2 instance. Once logs are being sent to CloudWatch, you can view individual log streams by:

• Navigating to the Log Group.
• Selecting a Log Stream based on the instance ID or other identifiers.

Creating Alarms and Alerts

CloudWatch allows you to set up alarms based on thresholds for metrics. These alarms can be tied to both default AWS metrics and custom metrics, ensuring that you are alerted when something goes wrong in your environment.

Setting Up an Alarm on a Custom Metric

Once your custom metric (e.g., memory usage) is being published, you can create an alarm that triggers when the metric exceeds a defined threshold.

• In the CloudWatch console, go to Alarms.
• Click Create Alarm.
• Select the custom metric (e.g., MemoryUsage).
• Set the threshold (e.g., memory usage > 80%).
• Define the actions (e.g., send a notification via SNS or trigger an autoscaling action).

Optimizing Log Management with CloudWatch Logs Insights

CloudWatch Logs Insights is a powerful query engine that allows you to search and analyze your log data in real-time. It can be used to extract useful information, troubleshoot application issues, and even create metric filters for deeper insights.

Writing Queries in CloudWatch Logs Insights

The following example shows how you can search for error messages in your application logs:
fields @timestamp, @message
|filter @message like /ERROR/
sort @timestamp desc
limit 20