ctrlX Data Layer and Datadog Integration
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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 ctrlX plugin is designed to gather data seamlessly from the ctrlX Data Layer middleware, widely used in industrial automation.
The Datadog Telegraf Plugin enables the submission of metrics to the Datadog Metrics API, facilitating efficient monitoring and data analysis through a reliable metric ingestion process.
Integration details
ctrlX Data Layer
The ctrlX Telegraf plugin provides a means to gather data from the ctrlX Data Layer, a communication middleware designed for professional automation applications. This plugin allows users to connect to ctrlX CORE devices, enabling the collection and monitoring of various metrics related to industrial and building automation, robotics, and IoT. The configuration options allow for detailed specifications of connection settings, subscription properties, and sampling rates, facilitating effective integration with the ctrlX Data Layer to meet customized monitoring needs, while leveraging the unique capabilities of the ctrlX platform.
Datadog
This plugin writes to the Datadog Metrics API, enabling users to send metrics for monitoring and performance analysis. By utilizing the Datadog API key, users can configure the plugin to establish a connection with Datadog’s v1 API. The plugin supports various configuration options including connection timeouts, HTTP proxy settings, and data compression methods, ensuring adaptability to different deployment environments. The ability to transform count metrics into rates enhances the integration of Telegraf with Datadog agents, particularly beneficial for applications that rely on real-time performance metrics.
Configuration
ctrlX Data Layer
[[inputs.ctrlx_datalayer]]
## Hostname or IP address of the ctrlX CORE Data Layer server
## example: server = "localhost" # Telegraf is running directly on the device
## server = "192.168.1.1" # Connect to ctrlX CORE remote via IP
## server = "host.example.com" # Connect to ctrlX CORE remote via hostname
## server = "10.0.2.2:8443" # Connect to ctrlX CORE Virtual from development environment
server = "localhost"
## Authentication credentials
username = "boschrexroth"
password = "boschrexroth"
## Use TLS but skip chain & host verification
# insecure_skip_verify = false
## Timeout for HTTP requests. (default: "10s")
# timeout = "10s"
## Create a ctrlX Data Layer subscription.
## It is possible to define multiple subscriptions per host. Each subscription can have its own
## sampling properties and a list of nodes to subscribe to.
## All subscriptions share the same credentials.
[[inputs.ctrlx_datalayer.subscription]]
## The name of the measurement. (default: "ctrlx")
measurement = "memory"
## Configure the ctrlX Data Layer nodes which should be subscribed.
## address - node address in ctrlX Data Layer (mandatory)
## name - field name to use in the output (optional, default: base name of address)
## tags - extra node tags to be added to the output metric (optional)
## Note:
## Use either the inline notation or the bracketed notation, not both.
## The tags property is only supported in bracketed notation due to toml parser restrictions
## Examples:
## Inline notation
nodes=[
{name="available", address="framework/metrics/system/memavailable-mb"},
{name="used", address="framework/metrics/system/memused-mb"},
]
## Bracketed notation
# [[inputs.ctrlx_datalayer.subscription.nodes]]
# name ="available"
# address="framework/metrics/system/memavailable-mb"
# ## Define extra tags related to node to be added to the output metric (optional)
# [inputs.ctrlx_datalayer.subscription.nodes.tags]
# node_tag1="node_tag1"
# node_tag2="node_tag2"
# [[inputs.ctrlx_datalayer.subscription.nodes]]
# name ="used"
# address="framework/metrics/system/memused-mb"
## The switch "output_json_string" enables output of the measurement as json.
## That way it can be used in in a subsequent processor plugin, e.g. "Starlark Processor Plugin".
# output_json_string = false
## Define extra tags related to subscription to be added to the output metric (optional)
# [inputs.ctrlx_datalayer.subscription.tags]
# subscription_tag1 = "subscription_tag1"
# subscription_tag2 = "subscription_tag2"
## The interval in which messages shall be sent by the ctrlX Data Layer to this plugin. (default: 1s)
## Higher values reduce load on network by queuing samples on server side and sending as a single TCP packet.
# publish_interval = "1s"
## The interval a "keepalive" message is sent if no change of data occurs. (default: 60s)
## Only used internally to detect broken network connections.
# keep_alive_interval = "60s"
## The interval an "error" message is sent if an error was received from a node. (default: 10s)
## Higher values reduce load on output target and network in case of errors by limiting frequency of error messages.
# error_interval = "10s"
## The interval that defines the fastest rate at which the node values should be sampled and values captured. (default: 1s)
## The sampling frequency should be adjusted to the dynamics of the signal to be sampled.
## Higher sampling frequencies increases load on ctrlX Data Layer.
## The sampling frequency can be higher, than the publish interval. Captured samples are put in a queue and sent in publish interval.
## Note: The minimum sampling interval can be overruled by a global setting in the ctrlX Data Layer configuration ('datalayer/subscriptions/settings').
# sampling_interval = "1s"
## The requested size of the node value queue. (default: 10)
## Relevant if more values are captured than can be sent.
# queue_size = 10
## The behaviour of the queue if it is full. (default: "DiscardOldest")
## Possible values:
## - "DiscardOldest"
## The oldest value gets deleted from the queue when it is full.
## - "DiscardNewest"
## The newest value gets deleted from the queue when it is full.
# queue_behaviour = "DiscardOldest"
## The filter when a new value will be sampled. (default: 0.0)
## Calculation rule: If (abs(lastCapturedValue - newValue) > dead_band_value) capture(newValue).
# dead_band_value = 0.0
## The conditions on which a sample should be captured and thus will be sent as a message. (default: "StatusValue")
## Possible values:
## - "Status"
## Capture the value only, when the state of the node changes from or to error state. Value changes are ignored.
## - "StatusValue"
## Capture when the value changes or the node changes from or to error state.
## See also 'dead_band_value' for what is considered as a value change.
## - "StatusValueTimestamp":
## Capture even if the value is the same, but the timestamp of the value is newer.
## Note: This might lead to high load on the network because every sample will be sent as a message
## even if the value of the node did not change.
# value_change = "StatusValue"
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
ctrlX Data Layer
-
Industrial Automation Monitoring: Utilize this plugin to continuously monitor key performance indicators from a manufacturing system controlled by ctrlX CORE devices. By subscribing to specific data nodes that provide real-time metrics such as resource availability or machine uptime, manufacturers can dynamically adjust their operations for increased efficiency and minimal downtime.
-
Energy Consumption Analysis: Collect energy consumption data from IoT-enabled ctrlX CORE platforms in a smart building setup. By analyzing trends and patterns in energy use, facility managers can optimize operating strategies, reduce energy costs, and support sustainability initiatives, making informed decisions about resource allocation and predictive maintenance.
-
Predictive Maintenance for Robotics: Gather telemetry data from robotics applications deployed in warehousing environments. By monitoring vibration, temperature, and operational parameters in real-time, organizations can predict equipment failures before they occur, leading to reduced maintenance costs and enhanced robotic system uptime through timely interventions.
-
Cross-Platform Data Integration: Connect data gathered from ctrlX CORE devices into a centralized Cloud data warehouse using this plugin. By streaming real-time metrics to other systems, organizations can create a unified view of operational performance across various manufacturing and operational systems, enabling data-driven decision-making across diverse platforms.
Datadog
-
Real-Time Infrastructure Monitoring: Use the Datadog plugin to monitor server metrics in real-time by sending CPU usage and memory statistics directly to Datadog. This integration allows IT teams to visualize and analyze system performance metrics in a centralized dashboard, enabling proactive response to any emerging issues, such as resource bottlenecks or server overloads.
-
Application Performance Tracking: Leverage this plugin to submit application-specific metrics, such as request counts and error rates, to Datadog. By integrating with application monitoring tools, teams can correlate infrastructure metrics with application performance, providing insights that enable them to optimize code performance and improve user experience.
-
Anomaly Detection in Metrics: Configure the Datadog plugin to send metrics that can trigger alerts and notifications based on unusual patterns detected by Datadog’s machine learning features. This proactive monitoring helps teams swiftly react to potential outages or performance degradation before customers are impacted.
-
Integrating with Cloud Services: By utilizing the Datadog plugin to send metrics from cloud resources, IT teams can gain visibility into cloud application performance. Monitoring metrics like latency and error rates helps with ensuring service-level agreements (SLAs) are met and also assists in optimizing resource allocation across cloud 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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