OPC UA and New Relic Integration
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Table of Contents
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.
This plugin allows the sending of metrics to New Relic Insights using the Metrics API, enabling effective monitoring and analysis of application performance.
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.
New Relic
This plugin writes metrics to New Relic Insights utilizing the Metrics API, which provides a robust mechanism for sending time series data to the New Relic platform. Users must first obtain an Insights API Key to authenticate and authorize their data submissions. The plugin is designed to facilitate easy integration with New Relic’s monitoring and analytics capabilities, supporting a variety of metric types and allowing for efficient data handling. Core features include the ability to add prefixes to metrics for better identification, customizable timeouts for API requests, and support for proxy settings to enhance connectivity. It is essential for users to configure these options according to their requirements, enabling seamless data flow into New Relic for comprehensive real-time analytics and 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
New Relic
[[outputs.newrelic]]
## The 'insights_key' parameter requires a NR license key.
## New Relic recommends you create one
## with a convenient name such as TELEGRAF_INSERT_KEY.
## reference: https://docs.newrelic.com/docs/apis/intro-apis/new-relic-api-keys/#ingest-license-key
# insights_key = "New Relic License Key Here"
## Prefix to add to add to metric name for easy identification.
## This is very useful if your metric names are ambiguous.
# metric_prefix = ""
## Timeout for writes to the New Relic API.
# timeout = "15s"
## HTTP Proxy override. If unset use values from the standard
## proxy environment variables to determine proxy, if any.
# http_proxy = "http://corporate.proxy:3128"
## Metric URL override to enable geographic location endpoints.
# If not set use values from the standard
# metric_url = "https://metric-api.newrelic.com/metric/v1"
Input and output integration examples
OPC UA
-
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.
-
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. -
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.
New Relic
-
Application Performance Monitoring: Use the New Relic Telegraf plugin to send application performance metrics from a web service to New Relic Insights. By integrating this plugin, developers can collect data such as response times, error rates, and throughput, enabling teams to monitor application health in real-time and resolve issues quickly before they impact users. This setup promotes proactive management of application performance and user experience.
-
Infrastructure Metrics Aggregation: Leverage this plugin to aggregate and send system-level metrics (CPU usage, memory consumption, etc.) from various servers to New Relic. This helps system administrators maintain an comprehensive view of infrastructure performance, facilitating capacity planning and identifying potential bottlenecks. By centralizing metrics in New Relic, teams can visualize trends over time and make informed decisions regarding resource allocation.
-
Dynamic Metric Naming for Multi-tenant Applications: Implement dynamic prefixing with the metric_prefix option to differentiate between different tenants in a multi-tenant application. By configuring the plugin to include a unique identifier per tenant in the metric names, teams can analyze usage patterns and performance metrics per tenant. This provides valuable insights into tenant behavior, supporting tailored optimizations and enhancing service quality across different customer segments.
-
Real-time Anomaly Detection: Combine the New Relic plugin with alerting mechanisms to trigger notifications based on unusual metric patterns. By sending metrics such as request counts and response times, teams can set thresholds in New Relic that, when breached, will automatically alert responsible parties. This user-driven approach supports immediate responses to potential issues before they escalate into larger incidents.
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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