MQTT and Librato Integration

Input and output integration overview

The MQTT Telegraf plugin is designed to read from specified MQTT topics and create metrics, enabling users to leverage MQTT for real-time data collection and monitoring.

The Librato plugin for Telegraf is designed to facilitate seamless integration with the Librato Metrics API, allowing for efficient metric reporting and monitoring.

Integration details

MQTT

The MQTT plugin allows for reading metrics from specified MQTT topics, creating metrics using supported input data formats. This plugin operates as a service input, which listens for incoming metrics or events rather than gathering them at set intervals like normal plugins. The flexibility of the plugin is enhanced with support for various broker URLs, topics, and connection features, including Quality of Service (QoS) levels and persistent sessions. Its configuration options incorporate global settings to modify metrics and handle startup errors effectively. It also supports secret-store configurations for securing username and password options, ensuring secure connections to MQTT servers.

Librato

The Librato plugin enables Telegraf to send metrics to the Librato Metrics API. To authenticate, users must provide an api_user and api_token, which can be acquired from the Librato account settings. This integration allows for efficient monitoring and reporting of custom metrics within the Librato platform. The plugin also utilizes a source_tag option that can enrich the metrics with contextual information from Point Tags; however, it does not currently support sending associated Point Tags. It is essential to note that any point value sent that cannot be converted to a float64 type will be skipped, ensuring that only valid metrics are processed and sent to Librato. The plugin also supports secret-store options for managing sensitive authentication credentials securely, facilitating best practices in credential management.

Configuration

MQTT

[[inputs.mqtt_consumer]]
  servers = ["tcp://127.0.0.1:1883"]
  topics = [
    "telegraf/host01/cpu",
    "telegraf/+/mem",
    "sensors/#",
  ]
  # topic_tag = "topic"
  # qos = 0
  # connection_timeout = "30s"
  # keepalive = "60s"
  # ping_timeout = "10s"
  # max_undelivered_messages = 1000
  # persistent_session = false
  # client_id = ""
  # username = "telegraf"
  # password = "metricsmetricsmetricsmetrics"
  # tls_ca = "/etc/telegraf/ca.pem"
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key = "/etc/telegraf/key.pem"
  # insecure_skip_verify = false
  # client_trace = false
  data_format = "influx"
  # [[inputs.mqtt_consumer.topic_parsing]]
  #   topic = ""
  #   measurement = ""
  #   tags = ""
  #   fields = ""
  #   [inputs.mqtt_consumer.topic_parsing.types]
  #      key = type

Librato

[[outputs.librato]]
  ## Librato API Docs
  ## http://dev.librato.com/v1/metrics-authentication
  ## Librato API user
  api_user = "[email protected]" # required.
  ## Librato API token
  api_token = "my-secret-token" # required.
  ## Debug
  # debug = false
  ## Connection timeout.
  # timeout = "5s"
  ## Output source Template (same as graphite buckets)
  ## see https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_OUTPUT.md#graphite
  ## This template is used in librato's source (not metric's name)
  template = "host"

Input and output integration examples

MQTT

1. Smart Home Monitoring: Use the MQTT Consumer plugin to monitor various sensors in a smart home setup. In this scenario, the plugin can be configured to subscribe to topics for different devices, such as temperature, humidity, and energy consumption. By aggregating this data, homeowners can visualize trends and receive alerts for unusual patterns, enhancing the overall quality and efficiency of home automation systems.

2. IoT Environmental Sensing: Deploy the MQTT Consumer to gather environmental data from sensors distributed across different locations. For instance, this can include readings from air quality sensors, temperature sensors, and noise level meters. The plugin can be configured to extract relevant tags and fields from the MQTT topics which allows for detailed analyses and reporting on environmental conditions at scale, supporting better decision making for urban planning or environmental initiatives.

3. Real-Time Vehicle Tracking and Telemetry: Integrate the MQTT Consumer plugin within a vehicle telemetry system that collects data from various sensors in real-time. With the plugin, metrics related to vehicle performance, location, and fuel consumption can be sent to a centralized monitoring dashboard. This real-time telemetry data enables fleet managers to optimize routes, reduce fuel costs, and improve vehicle maintenance schedules through proactive data analysis.

4. Agricultural Monitoring System: Leverage this plugin to collect data from agricultural sensors that monitor soil moisture, crop health, and weather conditions. The MQTT Consumer can subscribe to multiple topics associated with farming equipment and environmental sensors, allowing farmers to make data-driven decisions to improve crop yields while also conserving resources, enhancing sustainability in agriculture.

Librato

1. Real-time Application Monitoring: Utilize Librato to collect performance metrics from a web application in real-time. This setup involves sending response times, error rates, and user interactions to Librato, allowing developers to monitor the application’s health and performance metrics closely. By analyzing these metrics, teams can quickly identify and address performance bottlenecks or application failures before they impact end users.

2. Infrastructure Metrics Aggregation: Leverage this plugin to gather and send metrics from various infrastructure components, such as servers or containers, to Librato for centralized monitoring. Configuring the plugin to send CPU, memory usage, and disk I/O metrics enables system administrators to have a comprehensive view of infrastructure performance, assisting in capacity planning and resource optimization strategies.

3. Custom Metrics for Business Operations: Feed business-specific metrics, such as sales transactions or user sign-ups, to the Librato service using this plugin. By tracking these custom metrics, businesses can gain insights into their operational performance and make data-driven decisions to enhance their strategies, marketing efforts, or product development initiatives.

4. Anomaly Detection in Metrics: Implement monitoring tools that utilize machine learning for anomaly detection. By continuously sending real-time metrics to Librato, teams can analyze trends and automatically flag unusual behavior, such as sudden spikes in latency or unusual traffic patterns, enabling timely intervention and troubleshooting.

Feedback

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