Need to Monitor your Software Applications

Every software engineering team aims to release bug-free software applications to keep systems reliable with minimal downtime. However, it's easy to forget that building the app is just one piece of the puzzle—monitoring and observability are just as essential to ensure everything runs smoothly. Ensuring their smooth functionality and optimal performance is crucial for business success.

Whether you're a developer, technical lead, product/engineering manager, or founder, understanding why monitoring and observability are critical and how to implement them effectively can significantly impact your application’s stability.

The modern software application is a complex web of interconnected services and infrastructure. As these systems become more distributed (through microservices, cloud environments, etc.), ensuring their health and stability becomes increasingly challenging.

Monitoring and observability are vital aspects of software operation that tell us a lot about application behavior and system performance, they play a critical role in a team’s ability to respond to both failures and anomalies before they escalate into larger problems.

Selecting the suitable Monitoring Tools;

In choosing a monitoring tool, It’s important that you analyse the different toolsets available to you and understand what information these tools can provide (a tool like Datadog Dashboards offers real-time insights into the performance and health of systems and applications within an organization), how customizable they are, and how best they could be integrated into the rest of your application infrastructure.

There are numerous tools available to help your team implement effective monitoring and observability practices.

Metrics to Monitor;

A crucial aspect of monitoring is understanding which metrics to track to ensure you get the valuable insights you need. Choosing the right metrics is essential for effective monitoring and observability. Let’s look at a few metric categories that cover critical system aspects and provide visibility into performance, reliability, and user experience.

Infrastructure Metrics

• CPU Usage: Tracks how much processing power is being used. High CPU usage over time can lead to performance degradation or system crashes.
• Memory Usage: Monitors how much RAM is consumed by your applications. Memory leaks or inefficient memory usage can lead to out-of-memory errors and crashes.
• Disk I/O: Observes disk input/output operations, which are crucial for applications reliant on reading and writing data. High disk I/O may indicate bottlenecks that could slow down the system.
• Network Traffic: Measures inbound and outbound traffic on the network interface. Spikes in traffic could indicate potential issues, such as a DDoS attack or data transfer overload.

Application Performance Metrics

• Response Time (Latency): Tracks how long it takes for the system to respond to requests. High latency can lead to poor user experience and may indicate performance bottlenecks.
• Throughput: Measures the number of requests the application handles per second. Monitoring throughput helps assess whether the system can handle the current load efficiently.
• Error Rates: Monitors the percentage of failed requests or transactions. An increased error rate often indicates issues with services or dependencies.
• Queue Depth: Tracks the number of pending requests or jobs in the queue. High queue depths may suggest system overload or bottlenecks in processing.

User Experience Metrics

• Page Load Time: Monitors how long it takes for a page to load in a user’s browser. Slow page load times can result in poor user satisfaction and decreased engagement.
• Transaction Success Rate: Observes the percentage of successful user transactions (e.g., purchases, form submissions). A drop in success rate may indicate issues with your application’s functionality or third-party integrations.
• API Response Times: Tracks how long it takes for APIs to respond to requests. Slow APIs can degrade the overall performance of your system, especially in microservices architectures.
• Session Duration: Measures how long users stay active within an application. A sharp decline in session duration could be a sign of user dissatisfaction or app performance issues.

System Health Metrics

• Uptime/Downtime: Monitors how long the application or service is available. Uptime should ideally be as close to 100% as possible.
• Health Checks: Regularly scheduled checks that monitor the state of your services. Tools like Healthcheck automate health checks for scheduled jobs and alert you if something goes wrong.
• Service Dependencies: Observe the health and performance of external dependencies (e.g., databases, third-party APIs). Issues with dependencies can often propagate and affect your system’s performance.

Business Metrics

• Customer Impact: Tracks how system performance or downtime affects customer experience, revenue generation, or user retention.
• Cost: Monitors how efficiently resources are being used (e.g., cloud infrastructure costs). A system that is over-provisioned may incur unnecessary costs, while an under-provisioned one may result in performance bottlenecks.

By tracking these key metrics, teams can gain a comprehensive view of their application’s health, identify potential issues early, and make informed decisions to optimize performance.

Custom Alerts and Notifications

One of the beauties of alerts and notifications is to keep teams informed of potential issues before they escalate into larger problems. Imagine your team getting to know about a break or error before your end users. Many monitoring tools allow for various custom alerts and notifications to help make things easier, Alerts can be configured to trigger at specific thresholds, ensuring that the team is notified only when the issue reaches a critical state.

One common challenge is managing the volume of alerts. Too many notifications can overwhelm teams, leading to alert fatigue where critical alerts are missed. By setting appropriate thresholds and using features like Datadog’s anomaly detection, teams can reduce the number of false alarms. Similarly, Healthcheck.io can be configured to only notify teams if a job fails multiple times consecutively, avoiding unnecessary interruptions.

Some monitoring tools allow you to integrate these notifications with communication platforms like Slack, Discord, email, SMS, and webhooks. which can help teams be more responsive to these notifications and ensure that they can be addressed as soon as possible. you can set up escalation policies to ensure that alerts are handled by the appropriate team member, or automatically escalated if not addressed within a certain timeframe.

Conclusion

Investing in a robust monitoring strategy is not only a technical necessity but a business imperative, ensuring high availability, performance, and customer satisfaction.
An effective monitoring plan should be proactive, not reactive. By utilizing monitoring tools, you can stay ahead of issues and ensure your systems remain healthy, scalable, and reliable. The plan should account for various application components, such as databases, APIs, and external services, and regularly update the monitoring configurations as the system evolves.