MQTT and MariaDB 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.

This plugin writes metrics from Telegraf directly into MariaDB using parameterized SQL INSERT statements, offering a flexible way to store metrics in structured, relational tables.

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.

MariaDB

The SQL output plugin in Telegraf enables direct writing of metrics into SQL-compatible databases like MariaDB by executing parameterized SQL statements. With support for the MySQL driver, the plugin seamlessly integrates with MariaDB for reliable, structured metric storage. This setup is ideal for users who prefer SQL-based analytics or want to store metrics alongside business data for unified querying. MariaDB is a community-developed, enterprise-grade fork of MySQL that emphasizes performance, security, and openness. The plugin supports inserting time series metrics into custom schemas, enabling flexible analytics and integrations with BI tools like Metabase or Grafana using SQL connectors.

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

MariaDB

[[outputs.sql]]
  ## Database driver
  ## Valid options: mssql (Microsoft SQL Server), mysql (MySQL), pgx (Postgres),
  ##  sqlite (SQLite3), snowflake (snowflake.com) clickhouse (ClickHouse)
  driver = "mysql"

  ## Data source name
  ## The format of the data source name is different for each database driver.
  ## See the plugin readme for details.
  data_source_name = "username:password@tcp(host:port)/dbname"

  ## Timestamp column name
  timestamp_column = "timestamp"

  ## Table creation template
  ## Available template variables:
  ##  {TABLE} - table name as a quoted identifier
  ##  {TABLELITERAL} - table name as a quoted string literal
  ##  {COLUMNS} - column definitions (list of quoted identifiers and types)
  table_template = "CREATE TABLE {TABLE}({COLUMNS})"

  ## SQL INSERT statement with placeholders. Telegraf will substitute values at runtime.
  ## table_template = "INSERT INTO metrics (timestamp, name, value, tags) VALUES (?, ?, ?, ?)"

  ## Table existence check template
  ## Available template variables:
  ##  {TABLE} - tablename as a quoted identifier
  table_exists_template = "SELECT 1 FROM {TABLE} LIMIT 1"

  ## Initialization SQL
  init_sql = "SET sql_mode='ANSI_QUOTES';"

  ## Maximum amount of time a connection may be idle. "0s" means connections are
  ## never closed due to idle time.
  connection_max_idle_time = "0s"

  ## Maximum amount of time a connection may be reused. "0s" means connections
  ## are never closed due to age.
  connection_max_lifetime = "0s"

  ## Maximum number of connections in the idle connection pool. 0 means unlimited.
  connection_max_idle = 2

  ## Maximum number of open connections to the database. 0 means unlimited.
  connection_max_open = 0

  ## NOTE: Due to the way TOML is parsed, tables must be at the END of the
  ## plugin definition, otherwise additional config options are read as part of the
  ## table

  ## Metric type to SQL type conversion
  ## The values on the left are the data types Telegraf has and the values on
  ## the right are the data types Telegraf will use when sending to a database.
  ##
  ## The database values used must be data types the destination database
  ## understands. It is up to the user to ensure that the selected data type is
  ## available in the database they are using. Refer to your database
  ## documentation for what data types are available and supported.
  #[outputs.sql.convert]
  #  integer              = "INT"
  #  real                 = "DOUBLE"
  #  text                 = "TEXT"
  #  timestamp            = "TIMESTAMP"
  #  defaultvalue         = "TEXT"
  #  unsigned             = "UNSIGNED"
  #  bool                 = "BOOL"
  #  ## This setting controls the behavior of the unsigned value. By default the
  #  ## setting will take the integer value and append the unsigned value to it. The other
  #  ## option is "literal", which will use the actual value the user provides to
  #  ## the unsigned option. This is useful for a database like ClickHouse where
  #  ## the unsigned value should use a value like "uint64".
  #  # conversion_style = "unsigned_suffix"

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.

MariaDB

1. Business Intelligence Integration: Store application performance metrics directly into MariaDB and connect it to BI tools like Metabase or Apache Superset. This setup allows blending of operational data with business KPIs for unified dashboards, enhancing visibility across departments.

2. Compliance Reporting with Historical Metrics: Use this plugin to log metrics into MariaDB for audit and compliance use cases. The relational model enables precise querying of past performance indicators with timestamped entries, supporting regulatory documentation.

3. Custom Alerting Based on SQL Logic: Insert metrics into MariaDB and use custom SQL queries to define alert thresholds or conditions. Combined with cron jobs or scheduled scripts, this enables advanced alerting workflows not possible with traditional metric platforms.

4. IoT Sensor Metrics Storage: Collect sensor data from IoT devices via Telegraf and store it in MariaDB using a normalized schema. This approach is cost-effective and integrates well with existing SQL-based systems for real-time or historical analysis.

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