OpenTelemetry is a new open-source telemetry framework built on top of Prometheus. It allows developers to create custom metrics and publish them to any monitoring system.

OpenTelemetry is also a toolkit for building distributed systems. It provides a standard interface for collecting data from multiple sources and publishing it to various destinations. This includes both internal and external services.

It supports many types of data collection, including metrics, traces, logs, events, and even structured data.

OpenTelemetry aims to provide a simple way to collect and expose application performance data without writing application code. This post will discuss OpenTelemetry’s components and operation.


A set of guidelines known as an API (Application Programming Interface) enables software applications to communicate with one another. APIs are used in most modern applications.

The OpenTelemetry API is designed to be flexible enough to support a wide variety of use cases.

APIs are essential because they enable users to develop their own solutions rather than relying on the technologies provided by a third party.

For example, if you want to track the number of requests made by an endpoint, you can do so using the OpenTelemetry API.

You can do this without resorting to an outside service or library. Instead, you can simply implement it yourself.

This means that you can easily integrate OpenTelemetry into existing projects.

This also makes it easier to switch between different providers. For example, if you decide to move from Google Analytics to OpenTelemetry, you won’t need to rewrite all of your tracking logic.

The APIs in OpenTelemetry are designed to be easy to use and understand. Additionally, they are highly versatile, working with a broad range of frameworks and languages.

The APIs in Opentelemetry are divided into three categories:

  • The core API is the main entry point for all other APIs.
  • The client API is used by applications to send requests to the server.
  • The server API is used by servers to receive requests from clients.

Core API

The Core API has two parts: one part defines the structure of the messages sent between the client and the server, and the second part defines the format of the messages.

The function of Core API is to define the protocol that should be followed when sending or receiving messages. The Core API is written in Go.

Client API

The client application programming interface is used for communication between applications and servers. It consists of four parts:

  • A request handler receives requests from an application and sends them to the server.
  • A response handler receives responses from the server and returns them to the application.
  • A transport layer handles communication between the client and the service.
  • A client-side message generator generates messages from scratch using the data the client supplies.

Server API

The Server API is used by servers (e.g., Kubernetes) to receive requests from clients (e.g., apps). The server API consists of five parts:

  • A request receiver that receives requests from clients
  • An event sink that stores events received from the server
  • A trace collector that collects traces from the server
  • An HTTP server that serves requests from clients
  • A configuration file that contains settings such as the address of the server


If you’re not familiar with SDKs (Software Development Kits), they are basically libraries that provide functionality to application developers. They usually come with helpful classes and methods that streamline the coding process.

In addition, SDKs often include prebuilt binaries that simplify development. These features render SDKs indispensable for every programmer who values efficiency.

However, SDKs aren’t just useful for beginners. Advanced programmers can utilize them to create their own unique source solutions.

You might believe, for example, that the SDK is missing a number of important features. Or perhaps you’d like to change how the SDK works. In both cases, you’ll need to modify the source code. This requires experience with C++.

However, there’s no reason why you couldn’t do this using the SDK. All you have to do is download the latest version of the SDK and follow the instructions.

An SDK is built around the following concepts:

  • Components
  • Libraries


The parts of the SDK are organized into groups called components. Each component provides a set of functions and data structures.

Each component is made up of multiple libraries. A library is a collection of related functions and structures.

Libraries are grouped into components because they share similar characteristics and dependencies. For example, most libraries implement common algorithms.

When you use the SDK, you’ll see that it uses many different components. However, these components are designed so that you can easily replace them with your own implementations.

This means that if you want to create your own implementation of a particular component, you only need to understand its interface.


The libraries in the SDK are divided into two categories: core libraries and third-party libraries.

Core libraries are the ones that come bundled with the SDK. These libraries are essential for building applications.

Third-party libraries are those that are available separately. These are optional. You can skip them if you don’t need them when installing the SDK.

You can find out which libraries are included in the SDK by looking at the list of components.

Note that the SDK includes several core libraries. These libraries are listed under the “core” section of the components list.


The next component of OpenTelemetry is the exporter. The exporter is responsible for exporting telemetry data from an application or device. It allows you to export telemetry data as text files, binary files, or JSON objects.

Telemetry data is collected by devices and applications to provide information about their performance.

The SDK needs to communicate with the device or application to collect this data. To do this, the SDK must first send a request to the device or application. Then, the device or application sends back a response.

If the device or application doesn’t respond, then the SDK may not receive the requested data. This could happen if the device or application crashes, loses power, or has other problems.

If the device or application responds, but the data isn’t sent correctly, then the SDK might not be able to interpret the data properly. This could happen if a network connection fails or if the data was corrupted during transmission.

To avoid these problems, the SDK exports telemetry data to a file. This way, the SDK can store the data locally and retrieve it later.

How does the exporter work?

The exporter works by sending requests to the device or application using HTTP POSTs.

For example, suppose that you want to export telemetry data from your application. You would start by creating an instance of the Exporter class.

Then, you’d call the Start method to begin collecting telemetry data.

Finally, you’d call the Export method to save the telemetry data to a local file.

The exporter supports three types of exports:

  • Text file
  • Binary file
  • JSON object

Text file exports

The exporter creates text files containing telemetry data. They are formatted in the following way:

Format Name Description

Example: TextFile.txt

Contains one line per event. Each line contains a timestamp followed by the name of the event.

Example: 2019-01-23T12:00:00Z Event1 2019-01-23 12:00:00+0000 UTC

Binary files

The exporter generates binary files containing telemetry data in the form of a byte array. This type of file is useful for sending telemetry data over a network connection.

Sending telemetry data over a network connection is useful as it reduces bandwidth usage and improves performance.

The exporter also supports compressed binary files. Compressed binary files contain telemetry data in the same format as uncompressed binary files but are smaller than their uncompressed counterparts.

For more information about how to generate compressed binary files, please refer to the documentation for the ZipArchive class.

JSON object exports

The exporters also create JSON objects containing telemetry data. This file format can be used to transmit telemetry information via RESTful interfaces. This comes in handy for mobile apps and web services.


The last component of OpenTelemetry is the collector. The collector collects telemetry data from the devices and applications that are connected to the SDK. It stores the collected data in memory until it’s exported to a file.

When exporting telemetry data, the collector uses the exporter to write the data into a file.

The collector is a critical part of the SDK because it manages all of the connections between the SDK and the devices and applications that collect telemetry data.

This means that the collector must maintain a list of all of the devices and applications that have been registered with the SDK.

When the SDK starts up, it registers itself with the collector. Then, when the collector receives a request to export application telemetry data, it sends the request to the SDK.

If the SDK cannot find any devices or applications, it will register itself with the collector again. The SDK will keep searching until it locates at least one compatible device or app.

Once the SDK finds at least one device or app, it exports telemetry data. As long as devices or applications are connected to the SDK, they continue to receive requests to export telemetry data.

How the collector works in OpenTelemetry

The collector maintains a list of all of your devices and applications.

When the collector receives a request from an application or device to export telemetry data to the SDK, it first checks if the application or device exists in the collection.

If so, then the collector sends the request to the application or device. If not, then the collector adds the application or device to the collection.

After adding the application or device to its collection, the collector sends a message to the application or device telling it to start collecting telemetry data.

Once the application or device starts collecting telemetry data, the SDK sends messages to the collector, asking it to export telemetry data every few seconds.

The collector keeps track of which devices and applications are currently collecting telemetry data.

When the collector receives the next request to export telemetry, it looks through its collection to see if any of the devices or applications still exist.

If so, then it sends the request to those devices and applications. Otherwise, it deletes the devices and applications from its collection.

In addition to maintaining a list of devices and applications, the collector maintains a list of all the files that contain telemetry data. Each time the collector component exports telemetry data, it writes the data to a file.


To sum it up, OpenTelemetry is a brand-new open-source project developed by Google with the intention of delivering a standardized infrastructure for the instrumentation and telemetry of a wide variety of computing platforms.

It provides a standard interface for collecting data from various sources, such as sensors, logs, and traces. This makes collecting and analyzing data from multiple systems easier without writing source code specific to each platform.

The four core components of OpenTelemetry are APIs, SDK, Exporter, and Collector.

APIs allow you to use the SDK to connect to various types of instruments (sensors, loggers, etc.) and collect data from them.

The Software Development Kit (SDK) can be used to provide commands to these instruments and obtain the replies they provide.

The exporter component allows you to send this collected data to other services like BigQuery, Datastore, Cloud Storage, and more.

Managing the connections between the instruments and the exporter falls within the purview of the collector component, which is the final component.

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