OPC UA and Clarify 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 OPC UA 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 OPC UA plugin provides an interface for retrieving data from OPC UA server devices, facilitating effective data collection and monitoring.

The Clarify plugin allows users to publish Telegraf metrics directly to Clarify, enabling enhanced analysis and monitoring capabilities.

Integration details

OPC UA

The OPC UA Plugin retrieves data from devices that communicate using the OPC UA protocol, allowing you to collect and monitor data from your OPC UA servers.

Clarify

This plugin facilitates the writing of Telegraf metrics to Clarify, a platform for managing and analyzing time series data. By transforming metrics into Clarify signals, this output plugin enables seamless integration of collected telemetry data into the Clarify ecosystem. Users must obtain valid credentials, either through a credentials file or basic authentication, to configure the plugin. The configuration also provides options for fine-tuning how metrics are mapped to signals in Clarify, including the ability to specify unique identifiers using tags. Given that Clarify supports only floating point values, the plugin ensures that any unsupported types are effectively filtered out during the publishing process. This comprehensive connectivity aligns with use cases in monitoring, data analysis, and operational insights.

Configuration

OPC UA


[[inputs.opcua]]
  ## Metric name
  # name = "opcua"
  #
  ## OPC UA Endpoint URL
  # endpoint = "opc.tcp://localhost:4840"
  #
  ## Maximum time allowed to establish a connect to the endpoint.
  # connect_timeout = "10s"
  #
  ## Maximum time allowed for a request over the established connection.
  # request_timeout = "5s"

  # Maximum time that a session shall remain open without activity.
  # session_timeout = "20m"
  #
  ## Security policy, one of "None", "Basic128Rsa15", "Basic256",
  ## "Basic256Sha256", or "auto"
  # security_policy = "auto"
  #
  ## Security mode, one of "None", "Sign", "SignAndEncrypt", or "auto"
  # security_mode = "auto"
  #
  ## Path to cert.pem. Required when security mode or policy isn't "None".
  ## If cert path is not supplied, self-signed cert and key will be generated.
  # certificate = "/etc/telegraf/cert.pem"
  #
  ## Path to private key.pem. Required when security mode or policy isn't "None".
  ## If key path is not supplied, self-signed cert and key will be generated.
  # private_key = "/etc/telegraf/key.pem"
  #
  ## Authentication Method, one of "Certificate", "UserName", or "Anonymous".  To
  ## authenticate using a specific ID, select 'Certificate' or 'UserName'
  # auth_method = "Anonymous"
  #
  ## Username. Required for auth_method = "UserName"
  # username = ""
  #
  ## Password. Required for auth_method = "UserName"
  # password = ""
  #
  ## Option to select the metric timestamp to use. Valid options are:
  ##     "gather" -- uses the time of receiving the data in telegraf
  ##     "server" -- uses the timestamp provided by the server
  ##     "source" -- uses the timestamp provided by the source
  # timestamp = "gather"
  #
  ## Client trace messages
  ## When set to true, and debug mode enabled in the agent settings, the OPCUA
  ## client's messages are included in telegraf logs. These messages are very
  ## noisey, but essential for debugging issues.
  # client_trace = false
  #
  ## Include additional Fields in each metric
  ## Available options are:
  ##   DataType -- OPC-UA Data Type (string)
  # optional_fields = []
  #
  ## Node ID configuration
  ## name              - field name to use in the output
  ## namespace         - OPC UA namespace of the node (integer value 0 thru 3)
  ## identifier_type   - OPC UA ID type (s=string, i=numeric, g=guid, b=opaque)
  ## identifier        - OPC UA ID (tag as shown in opcua browser)
  ## tags              - extra tags to be added to the output metric (optional); deprecated in 1.25.0; use default_tags
  ## default_tags      - extra tags to be added to the output metric (optional)
  ##
  ## Use either the inline notation or the bracketed notation, not both.
  #
  ## Inline notation (default_tags not supported yet)
  # nodes = [
  #   {name="", namespace="", identifier_type="", identifier="", tags=[["tag1", "value1"], ["tag2", "value2"]},
  #   {name="", namespace="", identifier_type="", identifier=""},
  # ]
  #
  ## Bracketed notation
  # [[inputs.opcua.nodes]]
  #   name = "node1"
  #   namespace = ""
  #   identifier_type = ""
  #   identifier = ""
  #   default_tags = { tag1 = "value1", tag2 = "value2" }
  #
  # [[inputs.opcua.nodes]]
  #   name = "node2"
  #   namespace = ""
  #   identifier_type = ""
  #   identifier = ""
  #
  ## Node Group
  ## Sets defaults so they aren't required in every node.
  ## Default values can be set for:
  ## * Metric name
  ## * OPC UA namespace
  ## * Identifier
  ## * Default tags
  ##
  ## Multiple node groups are allowed
  #[[inputs.opcua.group]]
  ## Group Metric name. Overrides the top level name.  If unset, the
  ## top level name is used.
  # name =
  #
  ## Group default namespace. If a node in the group doesn't set its
  ## namespace, this is used.
  # namespace =
  #
  ## Group default identifier type. If a node in the group doesn't set its
  ## namespace, this is used.
  # identifier_type =
  #
  ## Default tags that are applied to every node in this group. Can be
  ## overwritten in a node by setting a different value for the tag name.
  ##   example: default_tags = { tag1 = "value1" }
  # default_tags = {}
  #
  ## Node ID Configuration.  Array of nodes with the same settings as above.
  ## Use either the inline notation or the bracketed notation, not both.
  #
  ## Inline notation (default_tags not supported yet)
  # nodes = [
  #  {name="node1", namespace="", identifier_type="", identifier=""},
  #  {name="node2", namespace="", identifier_type="", identifier=""},
  #]
  #
  ## Bracketed notation
  # [[inputs.opcua.group.nodes]]
  #   name = "node1"
  #   namespace = ""
  #   identifier_type = ""
  #   identifier = ""
  #   default_tags = { tag1 = "override1", tag2 = "value2" }
  #
  # [[inputs.opcua.group.nodes]]
  #   name = "node2"
  #   namespace = ""
  #   identifier_type = ""
  #   identifier = ""

  ## Enable workarounds required by some devices to work correctly
  # [inputs.opcua.workarounds]
    ## Set additional valid status codes, StatusOK (0x0) is always considered valid
  # additional_valid_status_codes = ["0xC0"]

  # [inputs.opcua.request_workarounds]
    ## Use unregistered reads instead of registered reads
  # use_unregistered_reads = false

Clarify

[[outputs.clarify]]
  ## Credentials File (Oauth 2.0 from Clarify integration)
  credentials_file = "/path/to/clarify/credentials.json"

  ## Clarify username password (Basic Auth from Clarify integration)
  username = "i-am-bob"
  password = "secret-password"

  ## Timeout for Clarify operations
  # timeout = "20s"

  ## Optional tags to be included when generating the unique ID for a signal in Clarify
  # id_tags = []
  # clarify_id_tag = 'clarify_input_id'

Input and output integration examples

OPC UA

  1. Basic Configuration: Set up the plugin with your OPC UA server endpoint and desired metrics. This allows Telegraf to start gathering metrics from the configured nodes.

  2. Node ID Setup: Use the configuration to specify specific nodes, such as temperature sensors, to monitor their values in real-time. For example, configure node ns=3;s=Temperature to gather temperature data directly.

  3. Group Configuration: Simplify monitoring multiple nodes by grouping them under a single configuration—this sets defaults for all nodes in that group, thereby reducing redundancy in setup.

Clarify

  1. Automated Data Monitoring: By integrating the Clarify plugin with sensor data collection, organizations can automate the monitoring of environmental conditions, such as temperature and humidity. The plugin processes metrics in real-time, sending updates to Clarify where they can be analyzed for trends, alerts, and historical tracking. This use case makes it easier to maintain optimal conditions in data centers or production environments, reducing the risk of equipment failures.

  2. Performance Metrics Analysis: Companies can leverage this plugin to send application performance metrics to Clarify. By transmitting key indicators such as response times and error rates, developers and operations teams can utilize Clarify’s capabilities to visualize and analyze application performance over time. This insight can drive improvements in user experience and help identify areas in need of optimization.

  3. Sensor Data Aggregation: Utilizing the plugin to push data from multiple sensors to Clarify allows for a comprehensive view of physical environments. This aggregation is particularly beneficial in sectors such as agriculture, where metrics from various sensors can be correlated to decision-making about resource allocations, pest control, and crop management. The plugin ensures the data is accurately mapped and transformed for effective analysis.

  4. Real-Time Alerts and Notifications: Implement the Clarify plugin to trigger real-time alerts based on predefined thresholds within the metrics being sent. For instance, if temperature readings exceed certain levels, alerts can be generated and sent to operational staff. This proactive approach allows for immediate responses to potential issues, enhancing operational reliability and safety.

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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