Response time monitoring is the process of measuring the time it takes for a system or application to respond to a request made by a user. This measurement can help identify performance bottlenecks and potential issues with the system, and can be used to optimize its performance.
Response time monitoring is a critical aspect of performance monitoring that can benefit a wide range of systems and applications including web applications, such as e-commerce sites, social media platforms, enterprise web applications, mobile applications, such as gaming apps, social media apps, productivity apps, API-based systems, especially microservice architectures, database systems, including relational and NoSQL databases.
The goals of response time monitoring may include:
Identifying performance bottlenecks in systems and applications that may prevent them from utilizing all the resources available. When services can use all the resources available their overall cost of ownership can be reduced, since for any given workload less resources will be required.
Improving user experience by optimizing delivery and providing fast and reliable experiences to users, improving user satisfaction and engagement.
Monitoring compliance with SLAs, ensuring that services are delivering within acceptable response parameters.
Identifying security issues and potential vulnerabilities, and addressing them.
Response time is typically measured from the time a request is sent to the time a response is received, and is expressed in milliseconds. The goal of response time monitoring is to ensure that the response time remains within acceptable limits, and to quickly identify and resolve any issues that result in an increase in response time.
There are a variety of tools and techniques that can be used for response time monitoring. The best practices are:
Monitor end-to-end response time, as observed by users. A common pitfall is to measure latency at component level and assume that because the individual components are responding fast, user-facing latency is low. To ensure your users are not experiencing high latency, it needs to be measured as close to the end user as possible, at the edge of your infrastructure, so that all latencies within your infrastructure are accumulated into it.
If you use a TLS / HTTPS / accelerator, a HTTP border gateway, a Web Application Firewall (WAF), a load balancer, or a reverse proxy, these are the best components to measure overall response time of your service. Measuring latency and response time at the edge can be an ideal service level indicator (SLI), on which you can set service level objectives (SLO) for the user experience you want to offer, and then keep track of the % of time your service meets this objective with an SLA.
Monitor for trends, by keeping a record of past response times, so that it will be easier to track and identify the impact of changes to the infrastructure.
Monitor the response time of as many responses as possible, by avoiding sampling of these data. For best results you should monitor the latency of all responses. If the service you monitor is a web API, a web server, or an application that logs all responses together with response times, use that as the primary source for tracking latency and response times.
Response times may vary significantly under different workloads. Monitoring the workload can provide great insights in understanding how the load on a service affects its latency and response time. Ideally, you should identify different workload types and measure workload and latency independently for each of them.
Monitor for errors or exceptions. Errors can severely influence latency and response times, and in many cases the effect of errors in latency are reversed: the service seems faster while behaving erratically.
Use synthetic monitoring. Having fast responses is useless if your service does not give the right responses. So, latency and response time monitoring should be combined with synthetics checks, real-life test scenarios, that run on your service to ensure that the service is responding properly to the requests it receives.
Use machine learning to analyze performance data and identify outliers or anomalies in them. Machine learning is able to learn from past performance data and identify trends and patterns that are usual or unusual for your specific service and infrastructure.
Use health checks and alerts to automatically get notified for changes in workload, latency and response times and the overall health of your service. Having robust alerts in place, means you can carry on with other tasks, while having the monitoring tools watch for issues and notify you when something looks abnormal.
To troubleshoot and fix response time issues we have to go the other way around. So, while we need to go at the edge of the infrastructure to measure the overall user experience, we have to deep dive into the infrastructure and carefully inspect every element in it to understand where the problem is and what can be done to improve it. Here are a few guidelines that may help:
Monitor everything, every application, every resource, every component that contributes to your service. When trying to identify the root cause of an issue, the more information that is available, the easier it will eventually be to solve the problem. The worst case scenario while trying to solve a performance issue is missing data. Either you will end-up with an assumption you can’t validate, or you will delay resolution by gathering new data and waiting for the issue to happen again to validate it. So, it is better to gather as much information as possible beforehand.
Monitor in real-time. Especially in the cloud world, infrastructure latencies are neither linear, nor predictable. Real-time monitoring is required to have clear visibility, since higher resolution data makes it a lot easier to identify inefficiencies in the micro-world. Per second data collection and visualization is a must for cloud environments: a service that sends 10k responses in a second and then stops for 9 seconds, is significantly different from a service that steadily sends 1k responses per second for 10 seconds.
Invest time in understanding your infrastructure. Each infrastructure is unique and by the way it has been installed and configured it may be affected by different factors. Observing and understanding the interdependencies within your infrastructure, will make it much easier to evaluate the performance data and take the right actions to improve it.
Netdata is a comprehensive monitoring solution that can be used to monitor and troubleshoot response time related issues. Here are some of the mechanisms using which Netdata ensures effective monitoring of latency and response time for troubleshooting performance issues.
Netdata can automatically gather response time data (and other performance metrics) from almost every packaged application available including popular web servers such as Apache, Nginx, and HAProxy, databases like Redis, MongoDB, PostgreSQL, and MySQL/MariaDB etc.
Netdata can collect custom application metrics using various methods, depending on your development stack you use, including scraping of Prometheus / OpenMetrics endpoints, and listening for StatsD metrics.
Netdata can also monitor log files and extract performance data from them, including response times, in real-time. If these log files are standard web server logs (apache, nginx formats), it will also auto-detect the format and set everything up for you automatically.
Synthetic checks are also possible. Netdata can be configured to query web API endpoints, TCP ports, ping servers and do SQL database queries, not only to check if they are alive and respond properly, but also for collecting response timing information.
And keep in mind that Netdata will also automatically collect operating system data, container data, network data, storage data, even processes data, including the system calls these processes (with eBPF) do and it will automatically organize and correlate all the information in ready to use dashboards.
Netdata maintains a long history of all this data, automatically applying tiering (recent data are high-fidelity - per second - losing granularity as time passes) to keep storage costs low.
Netdata can also be used to build a network of servers streaming metrics, to store them with different retentions at different places, or export them to third party databases.
Netdata trains a machine learning model for every single metric it collects, this allows Netdata to predict the expected range of response time values in the next data collection.
Training each metric individually allows Netdata to learn how metrics behave on each server independently of the others. Even if two servers are exactly the same, Netdata will train different models based on the workload that is offered on each of them, making prediction a lot more accurate.
When a collected value of a metric is considered an outlier (an anomaly) based on the trained model, Netdata stores the anomaly rate of the metric together with the collected values in its database, allowing us to query the past not only for metric values but also for the anomaly rates they had when they were collected.
Netdata uses a distributed health engine to monitor the health of response time and other performance metrics.This enables Netdata to run health checks as close to each service as possible.
When a network of Netdata agents are set up to stream the metrics around in your infrastructure, Netdata is smart enough to deduplicate alerts generated by different Netdata agents for the same metrics.
The health engine of Netdata allows both fixed threshold alerts (above/below X), dynamic threshold alerts to avoid alert flapping, but also supports rolling windows (e.g. compare the last minute vs the last 10 minutes), or even take into account anomaly rate information (is this metric behaving abnormally?).
Dozens of alert notification methods are available to help you get notified in real-time of important changes in your infrastructure, including PagerDuty, Slack, Email, etc.
Netdata offers powerful tools to optimize your troubleshooting and solve your response time issues faster than ever. Finding the proverbial needle in the haystack is a lot easier with Netdata.
Metrics Correlations, a tool that scans all metrics to find how they correlate in a given time-frame. This allows you to highlight an area with a spike or a dive on a chart and let Netdata find which other metrics have changed similarly at the same time.
Anomaly Advisor, a tool that scans all metrics for anomalies during a given time-frame. This allows you to highlight an area with a spike or a dive on a chart and let Netdata find which anomalies were detected during that time-frame, across your infrastructure.
These tools can be of great help identifying and revealing anomalies and interdependencies among infrastructure components.
By using Netdata for response time 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.
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