OpenTelemetry is a new open-source telemetry data system designed to help businesses analyze data from their applications. It provides a way to collect application metrics without installing any additional code.
OpenTelemetry is an exciting new tool that will revolutionize how companies monitor their applications.
What is telemetry Data? This article will go over the basic functions and principles of OpenTelemetry data analysis. We’ll cover how to set up OpenTelemetry monitoring, monitor user behavior, and detect software issues with telemetry data.
Why You Should Care about OpenTelemetry
OpenTelemetry is a new open-source project being developed by Google and other contributors.
OpenTelemetry’s purpose is to streamline the process by which programmers can gather telemetry data from their programs.
The data that is collected through telemetry might be helpful in understanding and improving the performance of your apps.
The most common type of telemetry data collected today is timing data. Timing data includes things like CPU usage, memory usage, network traffic, disk activity, etc. These data types are useful because they allow us to see whether our applications are performing well.
For example, we might want to know whether our web server is spending too much time processing requests or whether our database is running out of space.
The problem with timing data is that it only tells us one thing at a time. We have no idea what happened before or after the timing event. If we’re interested in understanding why something happened, we need more than just timing data.
For example, let’s say our DevOps team wanted to find out why our web server was taking so long to process requests.
We could use timing data to determine when the request came in, but we’d still need some way to correlate the timing data with the request itself. This would tell us which part of the request took longer to process.
We also might want to know how many times each user visited our website during a given day. We could record the number of visits per hour, but then we wouldn’t know anything about the users who made those visits.
OpenTelemetry employs a method known as sampling in order to address these issues and find solutions.
Sampling allows us to collect multiple pieces of data at once. Instead of recording every single piece of data, we sample a subset of the data. Then, we can look back later and figure out what happened based on the sampled data.
This means that instead of recording all the timing data associated with a particular request, we would record the timing data associated with 10% of the requests. When we looked back later, we could easily correlate the timing data with specific events within the request.
Compared to more traditional methods of evaluating telemetry data, this strategy excels in several critical respects, making it the preferred option.
First, it lets us collect data faster. Traditional approaches require us to write custom software to collect data. But since OpenTelemetry is built into the Go programming language, we don’t have to worry about writing extra code.
Second, by utilizing this method, it will be much simpler to identify correlations between the various data pieces.
With traditional approaches, we often end up having to write custom software to do this. But with OpenTelemetry, we can simply add tags to the telemetry data as we collect it.
Tags are metadata that describe the data. They can include things like the name of the service generating the data, the version of the service, the operating system, etc. Tags make it very easy to correlate different pieces of telemetry data together.
Lastly, compared to more conventional methods, this strategy allows us access to a substantial amount of additional data.
Since we’re collecting data at a higher rate, we get a better picture of what’s going on in our application. And since we’re collecting data simultaneously, we can compare the performance of two versions of an app side-by-side.
How to Set Up OpenTelemetry Monitoring with TelemetryHub
In order to start using OpenTelemetry monitoring, you’ll first need to install the TelemetryHub library. Once you’ve downloaded the library, you’ll need to build it locally.
Types of data collected by OpenTelemetry
OpenTelemetry supports four types of data: metrics, logs, traces, and counters.
Metrics are numeric values that represent some aspect of your application. For example, if you were tracking the amount of memory used by your application, you’d use a metric called MemoryUsage.
Logs are useful when you want to see exactly what was happening inside your application at a certain point in time. For example, if I wanted to know what was causing my application to crash, I might log the stack trace of the exception.
Traces are similar to logs, except they contain timestamps and other additional data. For example, if we wanted to track the amount of time spent waiting for a network connection, we could create a trace named NetworkConnectionTime.
Counters are simple values that count something. For example, if my application was counting how many times a user logged in, I might set a counter named UserLoginsCount.
Setting up OpenTelemetry metrics
To begin setting up OpenTelemetry metrics, you’ll need to define a new type called “metric.” The OpenTelemetry database will keep the value of this variable.
The metric must then be registered with OpenTelemetry. You must use the OpenTelemetry registry’s Register function to accomplish this.
After registering your metric, OpenTelemetry will need to know where to store the data. You must provide OpenTelemetry with a configuration file in order to accomplish this.
The next step is to build a trace. A trace is a group of actions that took place over time. A timestamp is attached to each occurrence. Traces are helpful because they let you see every event that took place during a certain period of time.
Take into consideration the possibility that you want to keep track of the number of API requests.
You could create a trace named RequestCount. Then, whenever someone makes a request, you would add an entry to the trace. This way, you could later look back through the trace and see how many requests were made.
To create a trace, you’ll need to instantiate a Trace object. You can then add entries to the trace by calling its Add method.
Monitoring User Behavior from Telemetry Data
The greatest advantage of telemetry data is that it allows you to monitor your users’ behavior when using your apps or site.
By looking at the telemetry data collected by OpenTelemetry, you can determine which parts of your application are most popular, where people spend their time while using your app, and much more.
For the sake of this example, let’s pretend that you created a website application that allowed users to upload images. It’s possible that you’re curious about how long it takes to upload each picture individually.
If you found out that uploading a photo takes about 2 seconds, you could then decide whether to increase the size of your servers’ hard drives so that all your users don’t have to wait around for photos to load.
Or maybe you could just make sure that your server has enough RAM available to handle all requests.
OpenTelemetry provides several ways to analyze telemetry data. One of these methods is called Histogram.
Histograms help display how a metric’s values vary widely. For example, suppose you wanted to know the percentage of users who uploaded photos within 5 minutes of starting their session.
You could use histograms to answer this question. First, you’d need to define a metric representing the time between when a user starts their session and uploads a photo.
Then, you’d need to collect some telemetry data. Once you’re done collecting data, you’d need to register the metric with the OpenTelemetry registry, and then you’d need to configure OpenTelemetry to write the data to disk.
Another example would be if you noticed that a particular part of your application took longer to respond to than others. You could then take steps to optimize that section of code.
Analyzing your application using telemetry data
Let’s look at how you can evaluate telemetry data now that you have a basic understanding of what it is, how it operates, and why it is vital.
The easiest way to do this is to use the open-source tool TraceViewer. It’s built into the Telemetry Hub library and allows you to view the telemetry data collected from your applications.
Once you’ve finished examining the telemetry data, you can export it as an Excel spreadsheet. This is advantageous since it makes it simple to communicate your findings to other team members.
Telemetry data can also be used to create graphs that display trends over time. You may, for instance, compare the number of users who visited your website during the week against the weekend. Alternatively, you could assess the proportion of visitors from various nations.
Even better, you can use Google Analytics and your telemetry data to better understand your users.
For instance, using the data acquired from OpenTelemetry and Google Analytics, you can determine which pages on your website are receiving the most traffic.
Detecting Software Issues with Telemetry Data
The fact that you can gain insight into issues that wouldn’t normally be visible, thanks to telemetry data, is undoubtedly one of its most valuable features.
Assume, for instance, that you observe one of your users having problems signing in. You would be able to identify the issue right away if you could gather telemetry data.
This is extremely useful if you are using different versions of your software. In this situation, you can use the telemetry information from earlier versions to find any potential performance problems in more recent versions.
The data can also be used to identify performance bottlenecks. You might observe, for instance, that some of your consumers are getting poor response times.
If you knew exactly which page people were visiting, you could concentrate your efforts on improving that particular section of your website and give it the attention it deserves.
Software issues telemetry data can’t fix.
Telemetry data isn’t always perfect. Sometimes it doesn’t provide enough detail to accurately diagnose a problem. And sometimes, it provides too much detail.
Consider the following scenario: a user has reported a bug to you, stating that they are unable to log in. You receive this report and investigate it.
Though, the only thing you know about the issue is that it happens when the user tries to log in. But there’s no way to tell whether the problem is entirely related to authentication, authorization, or something else.
If you had access to more detailed telemetry data, such as the browser’s HTTP request, you could narrow down the cause of the problem. However, if you don’t have access to this kind of data, you’ll need to rely on other methods to figure out what’s going wrong.
The good news is that you do not necessarily need to wait until you have access to all the details before attempting to repair the problem. Check that the data you are gathering falls into the appropriate categories.
Conclusion
To sum up, telemetry data is a powerful tool for analyzing and troubleshooting web applications. Using data from telemetry for the first time is simple, but it takes a lot of effort to become skilled at analyzing the data.
You can speed up your learning process by choosing TelemetryHub, a telemetry tool that is equipped for quickly identifying observability insights. With the assistance of this program, you will be able to take direct and prompt action to enhance, debug, and secure your software.
