Modbus and Mimir 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 sends Telegraf metrics directly to Grafana’s Mimir database using HTTP, providing scalable and efficient long-term storage and analysis for Prometheus-compatible metrics.

Integration details

Modbus

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

Mimir

Grafana Mimir supports the Prometheus Remote Write protocol, enabling Telegraf collected metrics to be efficiently ingested into Mimir clusters for large-scale, long-term storage. This integration leverages Prometheus’s well-established standards, allowing users to combine Telegraf’s extensive data collection capabilities with Mimir’s advanced features, such as query federation, multi-tenancy, high availability, and cost-efficient storage. Grafana Mimir’s architecture is optimized for handling high volumes of metric data and delivering fast query responses, making it ideal for complex monitoring environments and distributed systems.

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]},
  ]

Mimir

[[outputs.http]]
  url = "http://data-load-balancer-backend-1:9009/api/v1/push"
  data_format = "prometheusremotewrite"
  username = "*****"
  password = "******"
  [outputs.http.headers]
     Content-Type = "application/x-protobuf"
     Content-Encoding = "snappy"
     X-Scope-OrgID = "****"

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.

Mimir

1. Enterprise-Scale Kubernetes Monitoring: Integrate Telegraf with Grafana Mimir to stream metrics from Kubernetes clusters at enterprise scale. This enables comprehensive visibility, improved resource allocation, and proactive troubleshooting across hundreds of clusters, leveraging Mimir’s horizontal scalability and high availability.

2. Multi-tenant SaaS Application Observability: Use this plugin to centralize metrics from diverse SaaS tenants into Grafana Mimir, enabling tenant isolation and accurate billing based on resource usage. This approach provides reliable observability, efficient cost management, and secure multi-tenancy support.

3. Global Edge Network Performance Tracking: Stream latency and availability metrics from globally distributed edge servers into Grafana Mimir. Organizations can quickly identify performance degradation or outages, leveraging Mimir’s fast querying capabilities to ensure optimal service reliability and user experience.

4. Real-Time Analytics for High-Volume Microservices: Implement Telegraf metrics collection in high-volume microservices architectures, feeding data into Grafana Mimir for real-time analytics and anomaly detection. Mimir’s powerful querying enables teams to detect anomalies and quickly respond, maintaining high service availability and performance.

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