Modbus and Datadog Integration
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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 plugin writes to the Datadog Metrics API and requires an apikey which can be obtained for the account. This plugin supports the v1 API.
Integration details
Modbus
The Modbus plugin collects discrete inputs, coils, input registers, and holding registers via Modbus TCP or Modbus RTU/ASCII.
Datadog
Datadog metric names are formed by joining the Telegraf metric name and the field key with a .
character.
Field values are converted to floating point numbers. Strings and floats that cannot be sent over JSON, namely NaN and Inf, are ignored.
Setting rate_interval
to non-zero will convert count
metrics to rate
and divide its value by this interval before submitting to Datadog.
This allows Telegraf to submit metrics alongside Datadog agents when their rate
intervals are the same (Datadog defaults to 10s
).
Note that this only supports metrics ingested via inputs.statsd
given the dependency on the metric_type
tag it creates. There is only support for
counter
metrics, and count
values from timing
and histogram
metrics.
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]},
]
Datadog
[[outputs.datadog]]
## Datadog API key
apikey = "my-secret-key"
## Connection timeout.
# timeout = "5s"
## Write URL override; useful for debugging.
## This plugin only supports the v1 API currently due to the authentication
## method used.
# url = "https://app.datadoghq.com/api/v1/series"
## Set http_proxy
# use_system_proxy = false
# http_proxy_url = "http://localhost:8888"
## Override the default (none) compression used to send data.
## Supports: "zlib", "none"
# compression = "none"
## When non-zero, converts count metrics submitted by inputs.statsd
## into rate, while dividing the metric value by this number.
## Note that in order for metrics to be submitted simultaenously alongside
## a Datadog agent, rate_interval has to match the interval used by the
## agent - which defaults to 10s
# rate_interval = 0s
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.
Datadog
- Basic Metric Submission: Utilize the Datadog Output Plugin to transmit metrics from your Telegraf instance to Datadog. By configuring the
apikey
and enabling necessary metrics, you can easily monitor application performance over time. - Debugging Write URL: In cases where you need to troubleshoot your metric submissions, you can override the default write URL with a custom endpoint to debug the metrics being sent, ensuring that they are reaching the correct destination.
- Proxy Configuration: If your network setup requires routing through a proxy for outgoing requests, use the
http_proxy_url
option to set the appropriate proxy. This allows for seamless integration in restrictive network 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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