Modbus and Apache Druid Integration

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.

This plugin allows Telegraf to send JSON-formatted metrics to Apache Druid over HTTP, enabling real-time ingestion for analytical queries on high-volume time-series data.

Integration details

Modbus

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

Apache Druid

This configuration uses Telegraf’s HTTP output plugin with json data format to send metrics directly to Apache Druid, a real-time analytics database designed for fast, ad hoc queries on high-ingest time-series data. Druid supports ingestion via HTTP POST to various components like the Tranquility service or native ingestion endpoints. The JSON format is ideal for structuring Telegraf metrics into event-style records for Druid’s columnar and time-partitioned storage engine. Druid excels at powering interactive dashboards and exploratory queries across massive datasets, making it an excellent choice for real-time observability and monitoring analytics when integrated with Telegraf.

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]},
  ]

Apache Druid

[[outputs.http]]
  ## Druid ingestion endpoint (e.g., Tranquility, HTTP Ingest, or Kafka REST Proxy)
  url = "http://druid-ingest.example.com/v1/post"

  ## Use POST method to send events
  method = "POST"

  ## Data format for Druid ingestion (expects JSON format)
  data_format = "json"

  ## Optional headers (may vary depending on Druid setup)
  # [outputs.http.headers]
  #   Content-Type = "application/json"
  #   Authorization = "Bearer YOUR_API_TOKEN"

  ## Optional timeout and TLS settings
  timeout = "10s"
  # tls_ca = "/path/to/ca.pem"
  # tls_cert = "/path/to/cert.pem"
  # tls_key = "/path/to/key.pem"
  # insecure_skip_verify = false

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.

Apache Druid

1. Real-Time Application Monitoring Dashboard: Use Telegraf to collect metrics from application servers and send them to Druid for immediate analysis and visualization in dashboards. Druid’s low-latency querying allows users to interactively explore system behavior in near real-time.

2. Security Event Aggregation: Aggregate and forward security-related metrics such as failed logins, port scans, or process anomalies to Druid. Analysts can build dashboards to monitor threat patterns and investigate incidents with millisecond-level granularity.

3. IoT Device Analytics: Collect telemetry from edge devices via Telegraf and send it to Druid for fast, scalable processing. Druid’s time-partitioned storage and roll-up capabilities are ideal for handling billions of small JSON events from sensors or gateways.

4. Web Traffic Behavior Exploration: Use Telegraf to capture web server metrics (e.g., requests per second, latency, error rates) and forward them to Druid. This enables teams to drill down into user behavior by region, device, or request type with subsecond query performance.

Feedback

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