Web Server monitoring with Netdata

Web servers are the backbone of the internet, powering websites, content delivery platforms, and web applications that connect users around the world. They enable the seamless exchange of information, driving the digital economy and shaping the way we work, communicate, and consume content. In an era of ever-increasing reliance on web-based services, it is crucial to ensure the stability, performance, and security of web servers. This is where web server monitoring comes into play.

However, ensuring smooth web server operation can be a complex and challenging task. It may seem simple at start, but to achieve the confidence level required to ensure the web server is working as it should, there are many hidden details and common pitfalls. In this comprehensive guide, we will explore the ins and outs of web server monitoring, delving into the key metrics, tools, techniques, and strategies that can help ensure your web servers remain at peak performance.

Use Cases Of Effective Web Server Monitoring

An effectively monitored web server allows you to achieve the following use-cases with ease:

• Capacity planning By determining the amount of resources needed to handle your web servers’ workload and ensuring that you have enough resources to handle future growth. This can include monitoring server resources like CPU, memory, and disk usage, analyzing usage patterns, and scaling up or down as needed. Capacity planning helps ensure that your web server can handle current and future demands and prevents performance issues and downtime.

• Performance monitoring and optimization By monitoring and analyzing various metrics related to the performance of your web server, including response times, request rates, and resource utilization. This can help you identify bottlenecks or other issues that are impacting your server’s performance and optimize your server to improve performance. This can include optimizing server settings, caching, load balancing, identifying issues at back-end database servers, message brokers, etc.

• Security monitoring By monitoring your web servers for potential security threats and incidents, including attempted hacks, unauthorized access, and malware.

• Fast troubleshooting and root cause analysis To quickly identify and resolve issues with your web servers to minimize downtime and ensure optimal performance.

Techniques For Effective Web Server Monitoring

So what does it take to effectively monitor your web server? Well, to holistically understand the performance of your web server, you should employ each of the following techniques effectively:

1. Monitor and track the web workload Understanding the usage patterns and seasonality of the web workload is important to ensure that the server has enough resources to handle incoming requests. This can help you scale your server as needed and optimize your application for better performance.

2. Monitor for errors Monitoring errors can help you identify issues with your application or web server, such as failed requests or HTTP errors. This can also help you detect security incidents or attacks.

3. Monitor resources utilization Monitoring the server’s resources utilization, such as CPU, memory, network traffic and bandwidth, and disk I/O operations and capacity, can help you ensure that the server has enough resources to handle incoming requests. This can also help you identify any bottlenecks or issues with your servers’ infrastructure.

4. Use synthetic testing Synthetic testing can help you simulate user traffic and test the performance of your web server and application. This can help you identify any issues with the server or application and ensure that it is functioning optimally.

5. Set up alerts Setting up alerts can help you proactively detect any issues with your web servers and applications, such as high CPU usage, low memory, or failed requests. This can help you respond quickly to any issues and prevent downtime.

6. Setup machine learning Machine learning can identify patterns on all important metrics and assist you to detect outliers in workload and application behavior.

Now let’s dive deeper into each of the techniques described above.

Monitor and track web workload

There are 2 main sources for extracting information that allows you to monitor and track web server workload:

1. Metrics exposed by the web server itself.

Web servers expose various metrics that can help you monitor and track the web workload and errors. Some common metrics include:

• Requests per second: The number of HTTP requests processed by the web server per second.
• Bytes served: The amount of data transferred by the web server to clients.
• Active connections: The number of active connections to the web server.
• Connection queue: The number of connections waiting to be processed by the web server.
• Response time: The time taken by the web server to process and respond to an HTTP request.

These metrics can be accessed using various tools and libraries, such as the mod_status module for Apache or the ngx_http_stub_status_module for Nginx. Monitoring these metrics can help you understand the workload and performance of your web server and identify any issues or bottlenecks.

2. Metrics extracted by parsing the web server log files.

Web server log files contain detailed information about requests and responses, including client IP addresses, user agents, request methods, response codes, and more. By parsing and analyzing these log files, you can extract valuable metrics and insights about the web server workload and errors. Some common log file metrics include:

• Request rates: The number of HTTP requests per second, broken down by request method (GET, POST, PUT, DELETE, etc.).
• Response codes: The distribution of HTTP response codes (200, 404, 500, etc.) returned by the web server.
• Client IPs and user agents: The distribution of client IP addresses and user agents accessing the web server.
• Latency: The time taken by the server to process and respond to requests, broken down by percentiles (e.g., 95th percentile latency).

Monitor for errors

Monitoring errors is a crucial aspect of ensuring the performance, stability, and security of web servers and applications. By extracting error-related information from server metrics and log files, you can detect and address issues before they escalate and impact users.

Here are some key error metrics and logs that should be monitored in a web server and application context:

• Error rate: The number of failed requests or HTTP errors per second can help you identify issues with the server or application, such as configuration problems, resource limitations, or software bugs. Monitoring the error rate allows you to detect sudden spikes or trends that may indicate an ongoing problem.
• Error logs: Web servers and applications generate error logs that contain detailed information about errors and warnings. These logs can help you identify specific issues, such as server misconfigurations, application exceptions, or security-related events.
• Error types: Monitoring the distribution of different error types (e.g., HTTP 4xx vs. 5xx errors) can provide valuable insights into the nature of the issues affecting your server or application. For example, a high number of 4xx errors might indicate issues with client requests, whereas a high number of 5xx errors might suggest server-side problems.
• Application-specific error metrics: Depending on the application you are running, there might be additional error metrics specific to that application. For example, monitoring database query errors, cache hit/miss ratios, or API error rates can provide valuable insights into application-specific issues.

Monitor resources utilization

Monitoring the resources used by your web server is essential for ensuring optimal performance and handling incoming requests. Netdata provides real-time, out-of-the-box monitoring for various system resources, such as:

• CPU usage: The percentage of CPU time spent on user processes, system processes, and idle time.
• Memory usage: The amount of memory used by the web server and other processes, including free and available memory.
• Network traffic: The amount of data sent and received by the web server over the network, including bandwidth usage and network errors.
• Disk I/O: The number of read and write operations performed by the web server, including disk latency and throughput.

By monitoring these resources, you can quickly identify any bottlenecks or issues that may impact your web server’s performance, and proactively address them before they lead to downtime or other problems.

Enable synthetic testing

Synthetic monitoring is a proactive approach to web server monitoring that helps ensure optimal performance, reliability, and user satisfaction. It involves simulating user interactions with a web server or application using scripted tests. These tests are designed to mimic the behavior of real users, allowing administrators to monitor the performance and availability of their servers from an end-user perspective.

Some of the key benefits of synthetic monitoring for web servers include:

• Proactive problem identification: Synthetic monitoring allows administrators to identify potential issues before they affect real users. By continuously monitoring the performance and availability of key web server components, synthetic monitoring can help prevent downtime, slow response times, and other performance-related issues.
• Benchmarking and performance comparisons: Synthetic monitoring enables administrators to compare the performance of their web server against industry standards, competitors, or previous versions. This can help identify areas for improvement and drive continuous optimization.
• Geographical monitoring: Synthetic monitoring can be performed from multiple locations around the world, allowing administrators to assess the performance of their web server for users in different regions. This can help identify and address regional performance bottlenecks and ensure a consistent user experience across geographies.
• Monitoring third-party integrations: Many web servers rely on third-party services, such as content delivery networks (CDNs), APIs, or external databases. Synthetic monitoring can help ensure the performance and availability of these third-party components, reducing the risk of service disruptions.

By integrating synthetic monitoring into your web server monitoring strategy, you can proactively ensure optimal performance, reliability, and user satisfaction. With a keen eye on end-user experience and a focus on continuous improvement, synthetic monitoring can help you stay ahead of the competition and deliver a seamless, high-quality web experience for your users.

Set up Alerts

Setting up alerts is a powerful strategy for proactively detecting and addressing potential issues with your web servers and applications. By monitoring key performance indicators, such as high CPU usage, low memory, or failed requests, you can be notified of any issues before they escalate, allowing you to respond quickly and prevent downtime.

The importance of proactive alerting

Proactive alerting plays a vital role in maintaining the performance, reliability, and security of web servers and applications. By setting up alerts, you can:

• Identify issues before they impact users: Detecting problems early allows you to address them before they lead to a degraded user experience or, worse still, downtime.
• Optimize resource usage: Monitoring resource utilization and receiving alerts can help you identify areas for optimization, leading to more efficient use of resources and potentially reduced infrastructure costs.
• Improve security: Security-related alerts can help you quickly identify and respond to potential threats, such as unauthorized access, data breaches, or Distributed Denial of Service (DDoS) attacks.
• Streamline incident management: Alerts can help you prioritize incidents and allocate resources effectively, ensuring a faster resolution of issues and minimizing their impact.

Alerting methods and best practices

To set up effective alerts for your web servers and applications, consider the following best practices:

• Define alerting thresholds: Establish thresholds for key performance indicators, such as CPU usage, memory consumption, response times, or error rates. These thresholds should be based on your specific requirements and historical data to ensure meaningful and actionable alerts.
• Choose appropriate alerting methods: Select the most suitable method for delivering alerts, such as email, SMS, push notifications, or integration with incident management tools like PagerDuty or Opsgenie. The chosen method should ensure prompt notifications and facilitate rapid response.
• Create meaningful and actionable alert messages: Design alert messages that provide sufficient context and information to help the recipient understand the issue and take appropriate action.
• Implement alert escalation policies: Set up escalation rules to ensure that critical issues are addressed promptly and by the appropriate team members.
• Regularly review and adjust alert settings: Periodically evaluate the effectiveness of your alerts and adjust thresholds, methods, or messages as needed to maintain their relevance and usefulness.

What Is Downtime

Downtime refers to the periods when a web server or website is unavailable to users, preventing access to online services, content, or applications. This can severely impact user experience, business operations, and revenue, especially for high-traffic websites or critical applications.

Downtime can be planned, such as for server maintenance or upgrades, but unplanned downtime occurs due to technical failures. Unplanned downtime can damage a company’s reputation, affect SEO rankings, and lead to a loss of customer trust, making it essential to monitor and minimize such incidents.

Common Causes Of Server Downtime

• • Hardware failures
• • Software crashes
• • Network issues
• • Outages
• • Cyber-attacks (DDoS, hacking)
• • High traffic overload

Utilize Machine learning

Machine learning has become an increasingly valuable tool in the monitoring and management of web servers and applications. By analyzing historical and real-time performance data, machine learning algorithms can identify patterns and trends, enabling you to detect outliers and anomalous behavior more effectively.

Applying machine learning techniques to web server and application monitoring offers several advantages:

• Early warning for potential issues: By identifying outliers in real-time, machine learning can provide early warning signals for potential problems, allowing you to take corrective action before they escalate and impact users.
• Adaptive thresholds: Traditional monitoring tools rely on static thresholds for triggering alerts, which can result in false positives or missed issues.
• Faster root cause analysis: Machine learning can help pinpoint the root cause of an anomaly by analyzing the relationships between different metrics and events, making it easier to diagnose and resolve issues.

How can Netdata help?

Netdata is a comprehensive monitoring solution that can help you effectively monitor and troubleshoot web server performance, errors, logs, and response times. Here’s how Netdata ensures efficient monitoring and analysis of various aspects of web server operations:

Web Server Metrics

Netdata automatically gathers performance data from a wide range of applications, including popular web servers (Apache, Nginx, HAProxy). Additionally, it collects custom application metrics through methods like scraping Prometheus/OpenMetrics endpoints and listening for StatsD metrics.

To explore how Netdata monitors and visualizes these metrics you can check out the demo space or read the monitoring use-cases that focus on NGINX, Apache or the many other web server metrics that Netdata collects.

Web Server Log Monitoring

Netdata can monitor log files in real-time and extract performance data, such as response times. It auto-detects standard web server log formats (e.g. Apache, Nginx) and sets up monitoring automatically.

Visit the Netdata demo space to interact with dashboards and charts representing log metrics.

Synthetic Monitoring

Netdata can be configured to query web API endpoints, TCP ports, ping servers, collecting response timing information and checking for proper responses.

Comprehensive System Resource Monitoring

Netdata collects operating system data, container data, network data, storage data, and process data, organizing and correlating all information in ready-to-use dashboards.

Health Monitoring and Alerts

Netdata uses a distributed health engine to monitor the health of performance metrics, running health checks close to each service. The health engine supports fixed threshold alerts, dynamic threshold alerts, rolling windows, and anomaly rate information. Numerous alert notification methods are available, including PagerDuty, Slack, Email, etc.

Machine Learning

Netdata trains a machine learning model for every collected metric, predicting the expected range of values in the next data collection. This allows for anomaly detection based on the trained model and stores the anomaly rate alongside collected metric values.

Faster Troubleshooting

Netdata offers powerful tools to optimize troubleshooting and resolve issues faster:

• Metrics Correlations: This tool scans all metrics to find correlations within a specific time-frame. Highlight an area with a spike or dive on a chart, and Netdata will find other metrics that changed similarly at the same time.
• Anomaly Advisor: This tool scans all metrics for anomalies during a specific time-frame. Highlight an area with a spike or dive on a chart, and Netdata will find detected anomalies across your infrastructure during that time-frame.

By using Netdata for web server monitoring and troubleshooting, you can quickly identify and resolve issues, optimize your application’s performance, and ensure that your users have a fast and reliable experience.