Most Popular Telegraf Input Plugins and Integrations With InfluxDB

What is Telegraf, and which plugins should you start with? Learn about the open-source collection agent and favorite plugins in this post.

Telegraf is an open-source server agent designed for collecting, processing, aggregating, and writing metrics. Developed by InfluxData, it is part of the InfluxDB time series database platform. Known for its simplicity and ease of use, Telegraf offers a plug-and-play architecture with an array of input, output, and processor plugins. This flexibility allows Telegraf to seamlessly gather metrics from a diverse set of sources and write them into InfluxDB, serving as a vital component in monitoring and observability stacks.

One of Telegraf’s strengths is its ability to integrate with a wide range of tools and services, enhancing the capabilities of InfluxDB. It supports various data formats and network protocols, enabling integrations with common systems like databases, message queues, and web services. Some notable integrations include popular databases like MySQL, PostgreSQL, and MongoDB, messaging systems like Kafka and MQTT, and cloud services from providers such as AWS, Google Cloud, and Microsoft Azure. Additionally, Telegraf can collect data from various sources like sensors, IoT devices, and virtual systems and applications, making it a versatile tool for comprehensive monitoring solutions in diverse IT environments. This post covers the top types of Telegraf input plugins.

1. System Monitoring

System monitoring is a crucial aspect of IT management because it ensures optimal performance and reliability of computing resources. Among all the plugins available for system monitoring, some of the most popular are CPU, memory (mem), disk I/O (diskio), disk, Docker, internet speed, network (net), NVIDIA System Management Interface (nvidia_smi), and SMART (Self-Monitoring, Analysis, and Reporting Technology). The popularity of these plugins is due to their ability to provide comprehensive insights into key performance indicators of a system.

  1. CPU: Monitors the usage and performance of the central processing unit, a critical component in computing power and efficiency.
  2. Memory (mem): Essential for tracking the usage of RAM to help understand memory consumption and potential bottlenecks.
  3. Disk I/O (diskio): Offers insights into the input/output operations of disk drives, crucial for assessing the performance and health of storage systems.
  4. Disk: Tracks disk usage and performance, essential for ensuring adequate storage capacity and preventing disk-related slowdowns.
  5. Docker: Monitors Docker containers, providing vital information on container performance and resource utilization.
  6. Internet Speed: Measures the speed of internet connections, critical for network-dependent applications and services.
  7. Network (net): Monitors data transfer rates and network usage, key for maintaining optimal network performance.
  8. NVIDIA SMI (nvidia_smi): Specific to NVIDIA GPUs, this plugin monitors GPU performance, which is crucial in environments that use GPUs for intensive tasks like deep learning.
  9. SMART: This technology monitors the health and performance of hard disk drives and solid-state drives, predicting potential failures before they occur.

2. Logs

DevOps and log management are critical for maintaining system health and ensuring smooth operations. Among the various plugins available for log monitoring, some of the most widely used are the file, tail, and directory_monitor plugins. These plugins are popular due to their effectiveness in tracking and analyzing log data, which is essential for diagnosing problems, understanding system behavior, and ensuring security compliance.

  1. File: This plugin is designed to read logs from specified files. It’s particularly useful for systems that write logs to static files. Administrators can configure this plugin to monitor specific log files, making it easier to track application or system logs for errors, warnings, or other significant events. This plugin is also popular for writing JSON and CSV data to InfluxDB.
  2. Tail: Similar to the tail command in Unix-like systems, this plugin monitors log files in real time. It ‘tails’ the log file, meaning it reads from the end of the file as it grows. This is particularly useful for keeping a constant watch on log files, allowing for immediate detection and response to new log entries, which is crucial for time-sensitive applications or during troubleshooting.

3. HTTP/InfluxDB Plugins

For monitoring and managing HTTP and InfluxDB data streams, two significant and widely used plugins include:

  1. HTTP Listener V2: This plugin acts as an HTTP server that listens for incoming HTTP POST requests. It accepts data in various formats, making it versatile for different use cases. This plugin is ideal for scenarios where data needs to be pushed to the server via HTTP, such as data from web applications or IoT devices.
  2. InfluxDB V2 Listener: Tailored for InfluxDB v2, this plugin listens for write requests compatible with the InfluxDB 2.0 API. It’s essential for environments using InfluxDB as their time series database, allowing efficient and direct metrics ingestion into InfluxDB.

Both plugins are vital for facilitating real-time data ingestion and handling various streams of time series data. They also provide flexibility and ease of integration in diverse monitoring and data collection scenarios. Their popularity is due to a direct compatibility with common data sources and their ability to streamline the process of data collection and storage.

4. Windows

In the context of Windows system monitoring, two essential plugins are:

  1. win_perf_counters: This plugin gathers performance metrics using Windows Performance Counters, a comprehensive and native Windows monitoring tool.Its value lies in its ability tomonitor various aspects of the Windows operating system and applications, such as CPU, memory, disk, and network usage.
  2. win_wmi: Utilizing Windows Management Instrumentation (WMI), this plugin collects a wide range of system metrics and operational data. WMI is a core Windows feature for managing data and operations on Windows-based systems, making this plugin crucial for detailed system monitoring and management tasks.

Both plugins are crucial in a Windows-based environment for their deep integration with the system, providing comprehensive insights into the performance and health of Windows servers and applications. Their popularity stems from the ability to leverage native Windows tools for advanced monitoring and diagnostics.

5. Monitoring/K8s/Devices

In the diverse landscape of modern IT infrastructure, monitoring Kubernetes environments, Prometheus metrics, and various devices through protocols like OPC-UA and Modbus is crucial. These components represent key areas in system monitoring, catering to container orchestration, application performance, and industrial device communication. Understanding their significance offers insights into their widespread adoption.

  1. Kubernetes: This plugin is essential for monitoring Kubernetes clusters. It provides detailed metrics about the nodes, pods, and containers managed by Kubernetes, which are essential for ensuring the health and performance of containerized applications.
  2. Prometheus: Widely used for its powerful monitoring capabilities, this plugin collects metrics from Prometheus exporters. It’s integral for tracking application performance and system health in environments that use Prometheus for monitoring.
  3. OPC-UA (Open Platform Communications Unified Architecture): This protocol is key in industrial automation. The OPC-UA plugin facilitates industrial equipment monitoring, enabling the integration of machine data with IT systems for real-time analytics and decision-making.
  4. Modbus: A protocol commonly used in industrial environments for communication with various types of equipment. The Modbus plugin collects data from devices that use this protocol, providing insights into the performance and status of industrial systems.

6. Message Passing

Message passing in distributed systems is crucial for effective communication and data flow between different components and services. Key plugins in this domain include Kafka Consumer, MQTT Consumer, and OpenTelemetry. Each technology addresses specific aspects of message passing and telemetry data collection.

  1. Kafka Consumer: This plugin consumes messages from a Kafka cluster, a distributed streaming platform widely used for building real-time data pipelines and streaming applications. The Kafka Consumer plugin is essential for integrating with Kafka to monitor message throughput and performance and to ensure reliable data flow in streaming applications.
  2. MQTT Consumer: Message Queuing Telemetry Transport (MQTT) is a lightweight, publish-subscribe network protocol often used in IoT and mobile applications. The MQTT Consumer plugin allows for the consumption of messages published to MQTT brokers, facilitating real-time monitoring and data collection from a wide range of devices and applications.
  3. OpenTelemetry: An observability framework for cloud-native software, the OpenTelemetry plugin supports collecting telemetry data (metrics, logs, and traces) for analysis and monitoring. It’s particularly useful for understanding the performance and behavior of distributed systems and is integral in application performance monitoring (APM) and observability.

These plugins are critical in modern IT infrastructure because they ensure efficient message passing and data collection across various systems and protocols. Their adoption is driven by the need for robust, scalable, and flexible monitoring solutions in complex, distributed environments.

Next Steps

Telegraf encapsulates InfluxData’s commitment to open-source software. Users can build custom plugins and contribute them back to the project at their discretion. Telegraf has incredibly wide adoption, with millions of active instances. And the fact that many of InfluxData’s competitors rely on Telegraf for data collection validates its utility and effectiveness. As indicated above, organizations can find significant value in leveraging Telegraf to collect metrics from many sources.

Telegraf works hand-in-glove with InfluxDB, and you can get started with InfluxDB Cloud 3.0 here. If you need help with Telegraf or InfluxDB, please reach out using our community site or Slack channel. If you’re just getting started with Telegraf, take a look at these resources to learn more:

Telegraf Documentation: Learn how to get started with Telegraf.

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