OPC UA and Apache Hudi 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.

See Ways to Get Started

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.

Writes metrics to Parquet files via Telegraf’s Parquet output plugin, preparing them for ingestion into Apache Hudi’s lakehouse architecture.

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.

Apache Hudi

This configuration leverages Telegraf’s Parquet plugin to serialize metrics into columnar Parquet files suitable for downstream ingestion by Apache Hudi. The plugin writes metrics grouped by metric name into files in a specified directory, buffering writes for efficiency and optionally rotating files on timers. It considers schema compatibility—metrics with incompatible schemas are dropped—ensuring consistency. Apache Hudi can then consume these Parquet files via tools like DeltaStreamer or Spark jobs, enabling transactional ingestion, time-travel queries, and upserts on your time series data.

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

Apache Hudi

[[outputs.parquet]]
  ## Directory to write parquet files in. If a file already exists the output
  ## will attempt to continue using the existing file.
  directory = "/var/lib/telegraf/hudi_metrics"

  ## File rotation interval (default is no rotation)
  # rotation_interval = "1h"

  ## Buffer size before writing (default is 1000 metrics)
  # buffer_size = 1000

  ## Optional: compression codec (snappy, gzip, etc.)
  # compression_codec = "snappy"

  ## When grouping metrics, each metric name goes to its own file
  ## If a metric’s schema doesn’t match the existing schema, it will be dropped

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.

Apache Hudi

  1. Transactional Lakehouse Metrics: Buffer and write Web service metrics as Parquet files for DeltaStreamer to ingest into Hudi, enabling upserts, ACID compliance, and time-travel on historical performance data.

  2. Edge Device Batch Analytics: Telegraf running on IoT gateways writes metrics to Parquet locally, where periodic Spark jobs ingest them into Hudi for long-term analytics and traceability.

  3. Schema-Enforced Abnormal Metric Handling: Use Parquet plugin’s strict schema-dropping behavior to prevent malformed or unexpected metric changes. Hudi ingestion then guarantees consistent schema and data quality in downstream datasets.

  4. Data Platform Integration: Store Telegraf metrics as Parquet files in an S3/ADLS landing zone. Hudi’s Spark-based ingestion pipeline then loads them into a unified, queryable lakehouse with business events and logs.

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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