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In Database Last updated: June 28, 2023
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AWS CloudWatch is a native service of the Amazon Cloud ecosystem that logs and monitors all the other services in the Amazon Cloud. It collects and tracks metrics or log files and sets alarms for various activities extracted from them.

You can gain system-wide visibility into application performance, resource utilization, and services operational health. You can also use AWS CloudWatch to detect anomalous behavior in your environments, take automated actions, troubleshoot issues, and discover insights from the logs to take actions changing your system or operations for the better.

Monitoring your logs helps to detect security threats and identifies potential vulnerabilities. By monitoring for suspicious activity and unusual behavior, you can then respond to suspicious activity and take steps to prevent future attacks.

Also, maintaining logs and monitoring the systems is, more often than not, a mandatory activity for compliance purposes. By using AWS CloudWatch, you are meeting these requirements.

Those are all direct benefits that you gain from the utilization of AWS CloudWatch. So let’s check out how to start with all of that.

Setting Up AWS CloudWatch for Your Service


The setup can be simple and straightforward, or you can spend weeks fine-tuning all the aspects or properties of your logging and monitoring system and still not completely done (basically calling it a living improvement process).

But in a nutshell, this is how you can start:

  1. Assuming you already have an AWS account, enable CloudWatch by navigating to the CloudWatch console and clicking the “Get started” button.
  2. Create a log group to start collecting the logs for your service. A log group is a collection of log streams that share the same retention, monitoring, and access control settings. You can create a log group by clicking the “Create log group” button in the CloudWatch console.
  3. Create a log stream to narrow down the log events to the same source (service). You can create a log stream by clicking the “Create log stream” button in the CloudWatch console.
  4. If you aim to collect logs from EC2 instances, install the CloudWatch agent. The agent is a piece of software that runs on your instances and sends log data to CloudWatch. You can install the agent using the AWS Systems Manager or by running a script on your instances.
  5. Create a metric filter to extract metrics from your logs based on a defined matching pattern. You can create a metric filter by clicking the “Create metric filter” button in the CloudWatch console.
  6. Finally, collect and visualize all the extracted data in one place – create a dashboard. A dashboard is a widget collection that displays metrics and other data you place there. You can create a dashboard by clicking the “Create dashboard” button in the CloudWatch console.

Monitoring with AWS CloudWatch

As already said, you can monitor any service in Amazon Cloud using AWS Cloudwatch. To give a more detailed idea of how such monitoring can look, here is how to do it for the most common AWS services you most likely use in your system.


EC2 Instances

You can monitor EC2 instances by collecting metrics such as CPU utilization, network traffic, disk usage, and memory usage. You can also monitor the status of your EC2 instances and receive notifications when instances stop or terminate.

To monitor EC2 instances, install the CloudWatch agent on your instances and configure it to send metrics to CloudWatch. As a next step, you can then create alarms to alert you when metrics exceed certain thresholds.

RDS Databases

You can monitor Amazon RDS databases by collecting metrics such as CPU utilization, memory usage, or disk usage of the database. You can also monitor the status of the databases and receive notifications when databases are stopped, paused, or terminated.

To monitor RDS databases, enable enhanced monitoring and configure it to send metrics to CloudWatch. Again, you can then create alarms to alert you when metrics are off.

Lambda Functions

You can monitor AWS Lambda functions by collecting metrics such as lambda invocation count, duration, and error rate. You can also monitor the status of your functions and receive notifications when functions fail.

To monitor Lambda functions, you need to enable CloudWatch Logs and configure your functions to send logs to CloudWatch. You can then create metric filters to extract metrics from your logs and take actions based on the information extracted from the logs.

Elastic Load Balancers

Monitoring Elastic Load Balancers is done by collecting metrics such as request count, latency, and HTTP response codes. You can also monitor the status of your load balancers and receive notifications when load balancers fail.

To monitor load balancers, you need to enable access logs and configure your load balancers to send logs to CloudWatch. You can then create metric filters to extract metrics from your logs and create alarms whenever the metrics are off your defined healthy state.

Auto Scaling Groups

You can monitor Auto Scaling Groups by collecting group size, CPU utilization, and network traffic. You can also monitor the status of your groups and receive notifications when groups scale up or down.

To monitor Auto Scaling Groups, you need to enable detailed monitoring and configure it to send metrics to CloudWatch. You can then create alarms to alert you when metrics exceed certain thresholds.

Elastic Beanstalk Applications

You can monitor AWS Elastic Beanstalk applications by collecting metrics such as CPU utilization and request count. You can also monitor the status of your applications and receive notifications when applications fail.

To monitor Elastic Beanstalk applications, you need to enable enhanced health reporting and configure it to send metrics to CloudWatch. You can then create alarms to alert you when metrics are off the predefined thresholds.

Managing CloudWatch Alarms

There are available CloudWatch alarms for key metrics and each service. Those can be configured upfront, following best practices, and with their help troubleshooting issues as they arise.

By effectively managing your alarms, you get alerted to critical issues and can take appropriate actions to maintain the health and performance of your AWS resources and applications.


#1. Setting up Alarms for a Metric

To set up alarms, first, select the metric you want to monitor. Then create an alarm based on that metric by specifying a threshold value and a comparison operator.

For example, you can create an alarm that triggers when CPU utilization exceeds 80% for more than 5 minutes. Once you have created an alarm, configure actions to take when triggers the alarm. For example, sending an email to a specific list of recipients, sending an SMS notification, or even scaling up your system resources.

#2. Configuring Alarm Actions

When configuring alarm actions, it’s possible to choose from a variety of options, including sending notifications to an SNS topic, triggering an AWS Lambda function (that, in turn, can do whatever you want the function to do inside your Python script), stopping or terminating an EC2 instance.

You can also configure multiple actions for each alarm and by that, take different actions depending on the severity of the alarm. For example, sending an email notification for a minor alarm but terminating an instance for a critical alarm.

#3. Alarm Best Practices

It’s always good to follow best practices to ensure that alarms are effective and reliable. Some best practices include:

  • Setting appropriate thresholds based on historical data,
  • using multiple metrics in parallel to detect issues,
  • testing your alarms regularly to ensure they are working correctly.

Avoid creating too many alarms, as this can lead to alert fatigue, cluttering the monitoring system and making it difficult to actually identify really critical issues.

If you are experiencing issues with CloudWatch alarms, there are several troubleshooting steps you can take:

  • Check the alarm history to see if any actions were taken when the alarm was triggered.
  • Check the metric data to see if there are any anomalies or spikes that might have triggered the alarm.
  • If the issues persist, you can try adjusting the alarm threshold or adding additional metrics to the alarm to improve its accuracy.

Analyzing and Visualizing Metrics with CloudWatch Dashboards


Analyzing and visualizing metrics via dashboards gives you readable insights into the health and performance of your AWS resources and applications. CloudWatch dashboards provide a customizable view of your metrics.

You can place there various charts, graphs, and other visualizations that show the trends over time and highlight any issues the system might have. The ultimate goal is to be able to abstract from the log files data and have the important information in a much more readable and user-friendly format for anyone wanting to check and monitor the system state.

To create a CloudWatch dashboard, you can use the CloudWatch console or the CloudWatch API. Then just add widgets to the dashboard that display metrics you want to have visible on the dashboard. You can also add text and images to provide context or additional information.

Once you have created a dashboard, you can customize it to meet your specific needs. You can resize and rearrange widgets, change the time range of the data displayed, and add annotations to highlight important events or changes. You can also share your dashboard with other users, allowing them to view the same metrics and visualizations.

Lastly, you can easily deploy the same dashboard across a variety of AWS accounts and environments.

Collecting and Analyzing Logs

Analyzing the logs usually means using the Logs Insights feature of CloudWatch.

Once you have collected log data in CloudWatch Logs, you can start using Logs Insights. CloudWatch Logs Insights allows you to query and visualize log data using a simple yet powerful query language. It’s very similar to the SQL selects language, although not quite the same. The results are very similar, though.

You can use Insights to search for specific log events, filter log data based on specific criteria, and create visualizations such as charts and tables. With that, you gain yet another valuable insight into the behavior of your applications and infrastructure, which can be used to troubleshoot issues, optimize performance, or improve security.

Also read: How to Utilize AWS Logs Insights to Query Dashboard Metrics from AWS Services Logs

Automating Tasks with CloudWatch Events

If you have enough log data information in CloudWatch, you can use it to trigger actions based on events that occur in your AWS resources and applications. CloudWatch Events provides a way to schedule and automate tasks, for example, starting or stopping EC2 instances whenever the utilization of the instance goes out of the normal usage zone (e.g., stopping the instance during the night and starting again during the working day).

To automate tasks with CloudWatch Events, create a rule that specifies the event pattern to match and the action to take when the event occurs. You can do that using the CloudWatch console or the CloudWatch Events API. Then configure one or more targets for the rule, such as an AWS Lambda function, an SNS topic, or an EC2 instance.

CloudWatch Events supports a wide range of event sources, like AWS services, custom applications, and third-party services. So use CloudWatch Events to automate tasks whenever you need scaling resources, trigger backups, or responding to security incidents.

By automating tasks, you reduce manual intervention in your system and ensure that your AWS resources and applications are always running at optimal levels.

Advanced CloudWatch Features

There are several advanced CloudWatch features that can be set up to gain deeper insights into your AWS resources. One of them is the Logs insights already mentioned. Here are some of the other key advanced features:

  • CloudWatch Contributor Insights can identify the top contributors to your resource utilization (e.g., EC2 instances or Lambda functions). You can use Contributor Insights to identify the most resource-intensive operations and optimize the resources accordingly.
  • CloudWatch Anomaly Detection uses machine learning algorithms to automatically detect anomalous behavior in your metrics. Use Anomaly Detection to identify unusual spikes or drops in your metrics and take action to address them.
  • CloudWatch Synthetics creates canaries that simulate user behavior, and it can test the availability and performance of your applications. Use Synthetics to proactively explore and detect issues before your business users will do.
  • CloudWatch Logs Insights Query Acceleration will speed up your log queries by up to 10x. You can use Query Acceleration to analyze large volumes of log data quickly and efficiently.

Integrating CloudWatch with AWS Services


When building an AWS system, the Clodwatch integration is always a top priority on your list. Only with this deep integration you can collect and monitor metrics and logs across all your services or system components. It is also easy to set up and use, and the integration is native to most of the AWS services. So there are really few excuses not to use this benefit for your AWS cloud system.

You will gain a comprehensive view of your AWS resources and applications and the ability to monitor their health, performance, and availability. Then, after all the information is collected, just use the already existing data to set up alarms and automate tasks based on events that occur in your AWS environment.

Final Words

AWS CloudWatch is a comprehensive cloud service capable of covering all logging, monitoring, and system status visualizing needs for your project.

Including such components in your architecture is exactly how you proactively manage your systems and ensure their reliability. I would say don’t even prioritize it for later phases but start building the robust monitoring system from Sprint 1. You will appreciate it later.

Next, check out the best AWS monitoring tools.

  • Michal Vojtech
    Delivery-oriented architect with implementation experience in data/data warehouse solutions with telco, billing, automotive, bank, health, and utility industries. Certified for AWS Database Specialty and AWS Solution Architect… read more
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