What Is OpenTelemetry?

OpenTelemetry (also known as OTel) is a collection of tools, APIs, and SDKs used for instrumenting, generating, collecting, and exporting telemetry data (metrics, logs, and traces) for analysis. The Cloud Native Computing Foundation (CNCF) manages this open-source observability platform, which aims to provide all the necessary components to observe your services in a vendor-neutral manner.

OpenTelemetry enables developers to build standardized and interoperable telemetry data collection pipelines across a wide array of industries. It makes it easy for developers to instrument their software with telemetry data, whether they're working on a small, in-house project or a large-scale distributed system.

Observability is becoming a major focus of software development in many fields, but especially in the Internet of Things (IoT) industry. IoT deployments are hyper-distributed, with as many as millions of connected devices. Because IoT devices have limited computing capabilities, it may not be possible to monitor them using traditional tools. This is where OpenTelemetry comes in, providing flexible ways to collect telemetry from IoT devices and achieve observability even for the most complex IoT environments.

We’ll introduce the basics of OpenTelemetry and then explain how it can help monitor and manage IoT communications, in particular using the MQTT protocol.

3 Core Concepts of OpenTelemetry

Metrics

Metrics in OpenTelemetry are numerical representations of data measured over intervals of time. These could be measurements of system properties like CPU usage, memory consumption, or custom business metrics like the number of items in a shopping cart. Metrics help developers monitor the health of their applications and make informed decisions about resource allocation, performance tuning, and many other aspects of application development and maintenance.

Learn more in our detailed guide to OpenTelemetry metrics (coming soon)

Logs

In OpenTelemetry, logs are timestamped records of discrete events. These events could be anything from an error or exception in your code, a system event, or a user operation. Logs are crucial for understanding the behavior of an application and for debugging purposes. They provide a granular view of the events that occur within an application, making it easier to identify and fix issues.

Tracing

One of the core concepts of OpenTelemetry is tracing. A trace in OpenTelemetry is defined as the representation of a series of causally-related events in a system. These events can be anything from the start and end of a request, a database query, or a call to an external service. Tracing helps developers understand the sequence of events that led to a particular outcome, making it easier to debug and optimize their applications.

Learn more in our detailed guide to OpenTelemetry tracing (coming soon)

Components of OpenTelemetry

Let's break down the components of OpenTelemetry. The diagram below illustrates how they work together.

Components of OpenTelemetry

Source: https://opentelemetry.io

OpenTelemetry Collector

The OpenTelemetry Collector acts as a vendor-agnostic bridge between your applications and the backends that process the data. The Collector can ingest, process, and export telemetry data. It acts as an intermediary, allowing you to reduce the number of points of contact your applications need to make with your telemetry backend. It also standardizes your data so that it can be read by different telemetry backends.

Language SDKs

OpenTelemetry provides Language SDKs in several languages like Java, Python, and Go, among others. The SDKs are necessary for developers to instrument their code to capture telemetry data. They provide APIs for manual instrumentation and also include automatic instrumentation libraries. The SDKs also handle batching and retry logic, making it easier for developers to ensure reliable data delivery.

Agents and Instrumentation

Agents are the components that you install into your services to generate telemetry data. They automatically instrument your code, adding trace and metric data collection with minimal code changes. Instrumentation is the code that is inserted into your applications to collect the data. It can be manual, where developers add it to their code, or automatic, provided by the agents.

Exporters

Exporters are the components that transmit the telemetry data from your services to the backends. They transform the data into the format that your backend can understand. OpenTelemetry provides several exporters for common backends like Jaeger and Prometheus, but you can also write your own custom exporters.

Benefits of OpenTelemetry for IoT Deployments

Benefits of OpenTelemetry for IoT Deployments

OpenTelemetry is increasingly being used to support observability in IoT environments. Here are several ways this versatile platform can benefit organizations managing large-scale IoT deployments:

Using OpenTelemetry with MQTT

MQTT (Message Queuing Telemetry Transport) is a popular lightweight messaging protocol that's widely used in IoT deployments. MQTT's strength lies in its simplicity and efficiency, making it well-suited for scenarios where network bandwidth is at a premium.

When coupled with OpenTelemetry, MQTT gains the power of a comprehensive observability framework. Here's how OpenTelemetry complements MQTT:

Data enrichment: OpenTelemetry can enrich the data packets transmitted via MQTT with additional metadata. This could include information like device identifiers, location tags, and more. This enriched data provides a more contextualized view of operations, thereby making it easier to draw meaningful insights.
Centralized data collection: OpenTelemetry can collect data from multiple MQTT brokers and aggregate it into a centralized data store. This is particularly useful for large-scale IoT deployments that involve multiple brokers disseminating messages to numerous devices.
Real-Time monitoring: Using OpenTelemetry, organizations can enable real-time monitoring of MQTT messages. This feature helps in identifying any delays or bottlenecks in message delivery, which is vital for mission-critical IoT applications where latency can have significant repercussions.
Data export flexibility: With OpenTelemetry's various exporters, you can push your telemetry data to a variety of data backends for further analysis. For example, you can export data from MQTT to cloud-based solutions like Azure Monitor or an on-premises setup like Grafana.
Analytics and insights: By combining MQTT's lightweight data transmission capabilities with OpenTelemetry's robust analytics, organizations can perform deep dives into their data. This pairing makes it possible to optimize device performance, carry out predictive maintenance, and even identify market trends based on user behavior.

MQTT with OpenTelemetry: Key Metrics to Monitor

OpenTelemetry can provide valuable insights into an MQTT environment’s performance. Let's look at the key metrics to monitor.

Client Metrics

Client metrics are crucial as they give insights into how each MQTT client is performing. These include metrics like the number of messages published, the number of messages received, and the number of active connections. Monitoring these metrics can help you identify any clients that are underperforming or causing issues in your system.

Message Metrics

Message metrics give you an overview of the overall message flow in your system. These include metrics like the total number of messages sent, received, and the size of the messages. By monitoring these metrics, you can gain insights into the load on your system and identify any potential bottlenecks or issues.

Broker Metrics

Broker metrics provide insights into the performance of your MQTT broker. These include metrics like the number of connected clients, the number of subscriptions, and the memory usage of the broker. Monitoring these metrics can help you ensure that your broker is performing optimally and identify any potential issues early.

Latency Metrics

Latency metrics are crucial for understanding the performance of your system. These include metrics like the end-to-end latency and the latency of individual operations. High latency can affect the performance and reliability of your system, so monitoring these metrics can help you identify and address any issues early.

Error and Fault Metrics

Error and fault metrics are essential for understanding the reliability of your system. These include metrics like the number of dropped messages, the number of disconnects, and the number of errors thrown by your clients or broker. Monitoring these metrics can help you detect and fix issues early, reducing the impact on your system's performance and reliability.

EMQX MQTT Platform: Supporting OpenTelemetry Integration

EMQX, a leader in the field of MQTT brokers, has integrated the robust capabilities of OpenTelemetry. EMQX 5.2 provides a direct channel to send metrics to the OpenTelemetry Collector, using the gRPC OTEL protocol.

This integration not only optimizes data transmission but also ensures telemetry data can be effectively directed, processed, and adapted to diverse backends, from open-source platforms like Jaeger and Prometheus to specialized commercial solutions.

Key aspects of EMQX's OpenTelemetry Integration:

Universal integration: EMQX capitalizes on OpenTelemetry's platform-neutral design, ensuring adaptability with a wide range of observability backends.
Easy setup: In the EMQX dashboard, you can easily set up OpenTelemetry integration by specifying the Collector's gRPC address and setting metric transmission intervals.
Metrics visualization: After the integration, EMQX metrics can be directly accessed in the Prometheus web console, amplifying system observability.

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