Modbus and SigNoz Integration
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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.
This configuration turns any Telegraf agent into a Remote Write publisher for SigNoz, streaming rich metrics straight into the SigNoz backend with a single URL change.
Integration details
Modbus
The Modbus plugin collects discrete inputs, coils, input registers, and holding registers via Modbus TCP or Modbus RTU/ASCII.
SigNoz
SigNoz is an open source observability platform that stores metrics, traces, and logs. When you deploy SigNoz, its signoz-otel-collector-metrics service exposes a Prometheus Remote Write receiver (default :13133/api/v1/write). By configuring Telegraf’s Prometheus plugin to point at this endpoint, you can push any Telegraf collected metrics, SNMP counters, cloud services, or business KPIs—directly into SigNoz. The plugin natively serializes metrics in the Remote Write protobuf format, supports external labels, metadata export, retries, and TLS or bearer-token auth, so it fits zero-trust and multi-tenant SigNoz clusters. Inside SigNoz, the data lands in ClickHouse tables that back Metrics Explorer, alert rules, and unified dashboards. This approach lets organizations unify Prometheus and OTLP pipelines, enables long-term retention powered by ClickHouse compression, and avoids vendor lock-in while retaining PromQL-style queries.
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]},
]
SigNoz
[[outputs.prometheusremotewrite]]
## SigNoz OTEL-Collector metrics endpoint (Prometheus Remote Write receiver)
## Default port is 13133 when you install SigNoz with the Helm chart
url = "http://signoz-otel-collector-metrics.monitoring.svc.cluster.local:13133/api/v1/write"
## Add identifying labels so you can slice & dice the data later
external_labels = { host = "${HOSTNAME}", agent = "telegraf" }
## Forward host metadata for richer dashboards (SigNoz maps these to ClickHouse columns)
send_metadata = true
## ----- Authentication (comment out what you don’t need) -----
# bearer_token = "$SIGNOZ_TOKEN" # SaaS tenant token
# basic_username = "signoz" # Basic auth (self-hosted)
# basic_password = "secret"
## ----- TLS options (for SaaS or HTTPS self-hosted) -----
# tls_ca = "/etc/ssl/certs/ca.crt"
# tls_cert = "/etc/telegraf/certs/telegraf.crt"
# tls_key = "/etc/telegraf/certs/telegraf.key"
# insecure_skip_verify = false
## ----- Performance tuning -----
max_batch_size = 10000 # samples per POST
timeout = "10s"
retry_max = 3
Input and output integration examples
Modbus
- 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.
- 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. - Data Processing: Utilize the scaling features to convert raw Modbus readings into useful metrics, adjusting for unit conversions as needed.
SigNoz
-
Multi-Cluster Federated Monitoring: Drop a Telegraf DaemonSet into each Kubernetes cluster, tag metrics with
cluster=<name>
, and Remote Write them to a central SigNoz instance. Ops teams get a single PromQL window across prod, staging, and edge clusters without running Thanos sidecars. -
Factory-Floor Edge Gateway: A rugged Intel NUC on the shop floor runs Telegraf to scrape Modbus PLCs and environmental sensors. It batches readings every 5 seconds and pushes them over an intermittent 4G link to SigNoz SaaS. ClickHouse compression keeps costs low while AI-based outlier detection in SigNoz flags overheating motors before failure.
-
SaaS Usage Metering: Telegraf runs alongside each micro-service, exporting per-tenant counters (
api_calls
,gigabytes_processed
). Remote Write streams the data to SigNoz where a scheduled ClickHouse materialized view aggregates usage for monthly billing—no separate metering stack required. -
Autoscaling Feedback Loop: Combine Telegraf’s Kubernetes input with the Remote Write output to publish granular pod CPU and queue-length metrics into SigNoz. A custom SigNoz alert fires when P95 latency breaches 200 ms and a GitOps controller reads that alert to trigger a HorizontalPodAutoscaler tweak—closing the loop between observability and automation.
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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