Modbus and Clickhouse Integration

Powerful performance with an easy integration, powered by Telegraf, the open source data connector built by InfluxData.

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This is not the recommended configuration for real-time query at scale. For query and compression optimization, high-speed ingest, and high availability, you may want to consider Modbus and InfluxDB.

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Powerful Performance, Limitless Scale

Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.

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Input and output integration overview

The Modbus plugin allows you to collect data from Modbus devices using various communication methods, enhancing your ability to monitor and control industrial processes.

Telegraf’s SQL output plugin sends collected metrics to an SQL database using a straightforward table schema and dynamic column generation. When configured for ClickHouse, it adjusts DSN formatting and type conversion settings to ensure seamless data integration.

Integration details

Modbus

The Modbus plugin collects discrete inputs, coils, input registers, and holding registers via Modbus TCP or Modbus RTU/ASCII.

Clickhouse

The SQL output plugin is designed to store Telegraf metrics in an SQL database using a simple, hard-coded schema. Each metric type gets its own table, and columns are generated for every tag and field, with an optional timestamp column. For ClickHouse, the plugin leverages a specialized DSN format as defined by clickhouse-go v1.5.4 and customizes metric type conversion to align with ClickHouse data types. This ensures that integers, texts, timestamps, booleans, and real numbers are mapped to ClickHouse’s native types such as Int64, String, DateTime, UInt8, and Float64 respectively.

Configuration

Modbus

[[inputs.modbus]]
  name = "Device"
  slave_id = 1
  timeout = "1s"
  configuration_type = "register"
  discrete_inputs = [
    { name = "start", address = [0]},
    { name = "stop", address = [1]},
    { name = "reset", address = [2]},
    { name = "emergency_stop", address = [3]},
  ]
  coils = [
    { name = "motor1_run", address = [0]},
    { name = "motor1_jog", address = [1]},
    { name = "motor1_stop", address = [2]},
  ]
  holding_registers = [
    { name = "power_factor", byte_order = "AB", data_type = "FIXED", scale=0.01, address = [8]},
    { name = "voltage", byte_order = "AB", data_type = "FIXED", scale=0.1, address = [0]},
    { name = "energy", byte_order = "ABCD", data_type = "FIXED", scale=0.001, address = [5,6]},
    { name = "current", byte_order = "ABCD", data_type = "FIXED", scale=0.001, address = [1,2]},
    { name = "frequency", byte_order = "AB", data_type = "UFIXED", scale=0.1, address = [7]},
    { name = "power", byte_order = "ABCD", data_type = "UFIXED", scale=0.1, address = [3,4]},
    { name = "firmware", byte_order = "AB", data_type = "STRING", address = [5, 6, 7, 8, 9, 10, 11, 12]},
  ]
  input_registers = [
    { name = "tank_level", byte_order = "AB", data_type = "INT16", scale=1.0, address = [0]},
    { name = "tank_ph", byte_order = "AB", data_type = "INT16", scale=1.0, address = [1]},
    { name = "pump1_speed", byte_order = "ABCD", data_type = "INT32", scale=1.0, address = [3,4]},
  ]

Clickhouse

[[outputs.sql]]
  ## Database driver
  ## Valid options include mssql, mysql, pgx, sqlite, snowflake, clickhouse
  driver = "clickhouse"

  ## Data source name
  ## For ClickHouse, the DSN follows the clickhouse-go v1.5.4 format.
  ## Example DSN: "tcp://localhost:9000?debug=true"
  data_source_name = "tcp://localhost:9000?debug=true"

  ## 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})"

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

  ## Initialization SQL (optional)
  init_sql = ""

  ## 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

  ## Metric type to SQL type conversion for ClickHouse.
  ## The conversion maps Telegraf metric types to ClickHouse native data types.
  [outputs.sql.convert]
    conversion_style = "literal"
    integer          = "Int64"
    text             = "String"
    timestamp        = "DateTime"
    defaultvalue     = "String"
    unsigned         = "UInt64"
    bool             = "UInt8"
    real             = "Float64"

Input and output integration examples

Modbus

  1. Basic Usage: To read from a single device, configure it with the device name and IP address, specifying the slave ID and registers of interest.
  2. Multiple Requests: You can define multiple requests to fetch data from different Modbus slave devices in a single configuration by specifying multiple [[inputs.modbus.request]] sections.
  3. Data Processing: Utilize the scaling features to convert raw Modbus readings into useful metrics, adjusting for unit conversions as needed.

Clickhouse

  1. Basic ClickHouse Integration: Configure the plugin by setting the driver to ‘clickhouse’ and providing the appropriate DSN format as required by clickhouse-go v1.5.4. This ensures that Telegraf can connect and write metrics to your ClickHouse database.

  2. Customized Table Schemas: Leverage the table creation and existence check templates to tailor the database schema. This allows you to predefine column types and even disable automatic table creation if you prefer manual schema management.

  3. Advanced Type Conversion: Utilize the ClickHouse-specific conversion settings to map Telegraf metric types directly to ClickHouse data types (e.g., mapping integers to Int64 and timestamps to DateTime). This ensures data is stored with the correct precision and format.

  4. Initialization and Connection Tuning: Use the init_sql setting to run custom SQL commands upon connection, and adjust connection pool settings (like connection_max_idle_time and connection_max_open) to optimize performance for high-throughput environments.

Feedback

Thank you for being part of our community! If you have any general feedback or found any bugs on these pages, we welcome and encourage your input. Please submit your feedback in the InfluxDB community Slack.

Powerful Performance, Limitless Scale

Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.

See Ways to Get Started

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