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Synthetic monitoring is one holistic technique within the wide world of IT monitoring and application performance monitoring (APM) and it’s focused on web performance. Synthetic monitoring emulates the transaction paths between a client and application server and monitors what happens.
The goal of synthetic monitoring is to understand how a real user might experience an app or website.
In this article, let’s go deep with this topic. You probably will learn a thing or two about how to get more value from your synthetic monitoring tools and strategy.
Synthetic monitoring can be used to collect a range of performance information and answer questions like:
Explore Splunk Synthetic Monitoring, a leading tool, or watch this Tech Talk about real-world uses of this solution.
Synthetic monitoring checks are performed at regular intervals, and the frequency of these checks is typically determined by what is being checked. Availability, for example, might be checked once every minute. You can set up all sorts of monitors:
(See how synthetic monitoring & web analytics work together.)
Similar to real user monitoring (RUM), synthetic monitoring differs in how it’s done: it is synthetically generated by a programmable script that emulates user behavior and actions. In fact, the key benefit versus real-user monitoring is that synthetic monitoring can emulate certain client-server interactions that occur rarely or never in the real world.
Indeed, it is this unique capability that extends performance monitoring and testing coverage across all features and functions of your Web service, identifying performance issues and opportunities that you would otherwise not notice.
Furthermore, user-specific scenarios allow IT teams to proactively identify performance issues and introduce performance improvements. The monitoring process can be modular and designed to generate insightful performance monitoring data on unique usage scenarios.
(Go deep into the differences in synthetic vs. real user monitoring.)
A simple synthetic monitoring simulation design includes three components:
In synthetic monitoring, you can program a script to generate client-server transaction paths for a variety of scenarios, object types and environment variables. The synthetic monitoring tool then collects and analyzes application performance data along the customer’s journey of interacting with your application or web server:
(Get examples, screenshots & more details on must-have features in synthetic monitoring.)
Let’s consider various delay scenarios for the client-server communications.
A global delay that may be caused by a network outage or dependency issue. It creates a significant delay in response time for all users. An example of the global delay scenario is a new feature release that introduces a slow database query that is overlooked by the QA.
A partial delay that may only affect some part of the web service infrastructure. An example is a hardware issue that affects the load balancer, which means that all servers now share a partial delay due to the issue on one server.
A periodic delay that occurs repetitively – and perhaps at irregular intervals. An example is periodic data backups that occupy the network bandwidth and introduce delays during peak usage intervals.
The synthetic monitoring agent emulates the behavior of a real-user and allows the synthetic monitoring tool to collect data on predefined metrics such as availability and response time. This agent follows a programmable test routine. The configurations of this routine may include:
The scenario generation component injects a variety of testing scenarios that reflect the performance degradation or network outages. It may also specify how various agents are distributed to simulate a global user base accessing a web service through different data centers, as well as developments and changes in these circumstances.
The output of synthetic monitoring reports may be visual or time series data observations. You can then further analyze these outputs using a variety of statistical analysis and machine learning methods. Analysts typically look for:
Active agent probing can be used for a variety of monitoring types, including API monitoring, component monitoring, performance monitoring and load testing, among others.
The key idea is to emulate a real-world usage scenario on-demand. These interactions may be infrequent and available sporadically.
A major challenge facing synthetic monitoring is the validity of assumptions that go into producing a usage scenario. That is — we cannot assume we know what a user might do. In a real-world setting, users may behave unexpectedly. The scenario generating component, described above, may not be able to emulate an exhaustive set of complex real-world scenarios.
Still, you can mitigate these limitations. By combining synthetic monitoring with real-user monitoring, you’ll get the ideal view: you’ll get to look at the data produced by synthetic monitoring alongside information from real user monitoring — to make a well-informed and realistic statistical analysis.
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