MQTT and InfluxDB 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 InfluxDB plugin writes metrics to the InfluxDB HTTP service, allowing for efficient storage and retrieval of time series data.

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.

InfluxDB

The InfluxDB Telegraf plugin serves to send metrics to the InfluxDB HTTP API, facilitating the storage and query of time series data in a structured manner. Integrating seamlessly with InfluxDB, this plugin provides essential features such as token-based authentication and support for multiple InfluxDB cluster nodes, ensuring reliable and scalable data ingestion. Through its configurability, users can specify options like organization, destination buckets, and HTTP-specific settings, providing flexibility to tailor how data is sent and stored. The plugin also supports secret management for sensitive data, which enhances security in production environments. This plugin is particularly beneficial in modern observability stacks where real-time analytics and storage of time series data are crucial.

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

InfluxDB

[[outputs.influxdb]]
  ## The full HTTP or UDP URL for your InfluxDB instance.
  ##
  ## Multiple URLs can be specified for a single cluster, only ONE of the
  ## urls will be written to each interval.
  # urls = ["unix:///var/run/influxdb.sock"]
  # urls = ["udp://127.0.0.1:8089"]
  # urls = ["http://127.0.0.1:8086"]

  ## Local address to bind when connecting to the server
  ## If empty or not set, the local address is automatically chosen.
  # local_address = ""

  ## The target database for metrics; will be created as needed.
  ## For UDP url endpoint database needs to be configured on server side.
  # database = "telegraf"

  ## The value of this tag will be used to determine the database.  If this
  ## tag is not set the 'database' option is used as the default.
  # database_tag = ""

  ## If true, the 'database_tag' will not be included in the written metric.
  # exclude_database_tag = false

  ## If true, no CREATE DATABASE queries will be sent.  Set to true when using
  ## Telegraf with a user without permissions to create databases or when the
  ## database already exists.
  # skip_database_creation = false

  ## Name of existing retention policy to write to.  Empty string writes to
  ## the default retention policy.  Only takes effect when using HTTP.
  # retention_policy = ""

  ## The value of this tag will be used to determine the retention policy.  If this
  ## tag is not set the 'retention_policy' option is used as the default.
  # retention_policy_tag = ""

  ## If true, the 'retention_policy_tag' will not be included in the written metric.
  # exclude_retention_policy_tag = false

  ## Write consistency (clusters only), can be: "any", "one", "quorum", "all".
  ## Only takes effect when using HTTP.
  # write_consistency = "any"

  ## Timeout for HTTP messages.
  # timeout = "5s"

  ## HTTP Basic Auth
  # username = "telegraf"
  # password = "metricsmetricsmetricsmetrics"

  ## HTTP User-Agent
  # user_agent = "telegraf"

  ## UDP payload size is the maximum packet size to send.
  # udp_payload = "512B"

  ## Optional TLS Config for use on HTTP connections.
  # tls_ca = "/etc/telegraf/ca.pem"
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key = "/etc/telegraf/key.pem"
  ## Use TLS but skip chain & host verification
  # insecure_skip_verify = false

  ## HTTP Proxy override, if unset values the standard proxy environment
  ## variables are consulted to determine which proxy, if any, should be used.
  # http_proxy = "http://corporate.proxy:3128"

  ## Additional HTTP headers
  # http_headers = {"X-Special-Header" = "Special-Value"}

  ## HTTP Content-Encoding for write request body, can be set to "gzip" to
  ## compress body or "identity" to apply no encoding.
  # content_encoding = "gzip"

  ## When true, Telegraf will output unsigned integers as unsigned values,
  ## i.e.: "42u".  You will need a version of InfluxDB supporting unsigned
  ## integer values.  Enabling this option will result in field type errors if
  ## existing data has been written.
  # influx_uint_support = false

  ## When true, Telegraf will omit the timestamp on data to allow InfluxDB
  ## to set the timestamp of the data during ingestion. This is generally NOT
  ## what you want as it can lead to data points captured at different times
  ## getting omitted due to similar data.
  # influx_omit_timestamp = false

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.

InfluxDB

1. Real-Time System Monitoring: Utilize the InfluxDB plugin to capture and store metrics from a range of system components, such as CPU usage, memory consumption, and disk I/O. By pushing these metrics into InfluxDB, you can create a live dashboard that visualizes system performance in real time. This setup not only helps in identifying performance bottlenecks but also assists in proactive capacity planning by analyzing trends over time.

2. Performance Tracking for Web Applications: Automatically gather and push metrics related to web application performance, such as request durations, error rates, and user interactions, to InfluxDB. By employing this plugin in your monitoring stack, you can use the stored metrics to generate reports and analyses that help understand user behavior and application efficiency, thus guiding development and optimization efforts.

3. IoT Data Aggregation: Leverage the InfluxDB Telegraf plugin to collect sensor data from various IoT devices and store it in a centralized InfluxDB instance. This use case enables you to analyze trends and patterns in environmental or machine data over time, facilitating smarter decisions and predictive maintenance strategies. By integrating IoT data into InfluxDB, organizations can harness the power of historical data analysis to drive innovation and operational efficiency.

4. Analyzing Historical Metrics for Forecasting: Set up the InfluxDB plugin to send historical metric data into InfluxDB and use it to drive forecasting models. By analyzing past performance metrics, you can create predictive models that forecast future trends and demands. This application is particularly useful for business intelligence purposes, helping organizations prepare for fluctuations in resource needs based on historical usage patterns.

Feedback

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