OPC UA and Librato Integration
Powerful performance with an easy integration, powered by Telegraf, the open source data connector built by InfluxData.
5B+
Telegraf downloads
#1
Time series database
Source: DB Engines
1B+
Downloads of InfluxDB
2,800+
Contributors
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.
The Librato plugin for Telegraf is designed to facilitate seamless integration with the Librato Metrics API, allowing for efficient metric reporting and monitoring.
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.
Librato
The Librato plugin enables Telegraf to send metrics to the Librato Metrics API. To authenticate, users must provide an api_user
and api_token
, which can be acquired from the Librato account settings. This integration allows for efficient monitoring and reporting of custom metrics within the Librato platform. The plugin also utilizes a source_tag
option that can enrich the metrics with contextual information from Point Tags; however, it does not currently support sending associated Point Tags. It is essential to note that any point value sent that cannot be converted to a float64 type will be skipped, ensuring that only valid metrics are processed and sent to Librato. The plugin also supports secret-store options for managing sensitive authentication credentials securely, facilitating best practices in credential management.
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
Librato
[[outputs.librato]]
## Librato API Docs
## http://dev.librato.com/v1/metrics-authentication
## Librato API user
api_user = "[email protected]" # required.
## Librato API token
api_token = "my-secret-token" # required.
## Debug
# debug = false
## Connection timeout.
# timeout = "5s"
## Output source Template (same as graphite buckets)
## see https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_OUTPUT.md#graphite
## This template is used in librato's source (not metric's name)
template = "host"
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.
Librato
-
Real-time Application Monitoring: Utilize Librato to collect performance metrics from a web application in real-time. This setup involves sending response times, error rates, and user interactions to Librato, allowing developers to monitor the application’s health and performance metrics closely. By analyzing these metrics, teams can quickly identify and address performance bottlenecks or application failures before they impact end users.
-
Infrastructure Metrics Aggregation: Leverage this plugin to gather and send metrics from various infrastructure components, such as servers or containers, to Librato for centralized monitoring. Configuring the plugin to send CPU, memory usage, and disk I/O metrics enables system administrators to have a comprehensive view of infrastructure performance, assisting in capacity planning and resource optimization strategies.
-
Custom Metrics for Business Operations: Feed business-specific metrics, such as sales transactions or user sign-ups, to the Librato service using this plugin. By tracking these custom metrics, businesses can gain insights into their operational performance and make data-driven decisions to enhance their strategies, marketing efforts, or product development initiatives.
-
Anomaly Detection in Metrics: Implement monitoring tools that utilize machine learning for anomaly detection. By continuously sending real-time metrics to Librato, teams can analyze trends and automatically flag unusual behavior, such as sudden spikes in latency or unusual traffic patterns, enabling timely intervention and troubleshooting.
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
Related Integrations
Related Integrations
HTTP and InfluxDB Integration
The HTTP plugin collects metrics from one or more HTTP(S) endpoints. It supports various authentication methods and configuration options for data formats.
View IntegrationKafka and InfluxDB Integration
This plugin reads messages from Kafka and allows the creation of metrics based on those messages. It supports various configurations including different Kafka settings and message processing options.
View IntegrationKinesis and InfluxDB Integration
The Kinesis plugin allows for reading metrics from AWS Kinesis streams. It supports multiple input data formats and offers checkpointing features with DynamoDB for reliable message processing.
View Integration