ctrlX Data Layer and Loki 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 ctrlX data layer 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 ctrlX plugin is designed to gather data seamlessly from the ctrlX Data Layer middleware, widely used in industrial automation.

The Loki plugin allows users to send logs to Loki for aggregation and querying, leveraging Loki’s efficient storage capabilities.

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.

Loki

This Loki plugin integrates with Grafana Loki, a powerful log aggregation system. By sending logs in a format compatible with Loki, this plugin allows for efficient storage and querying of logs. Each log entry is structured in a key-value format where keys represent the field names and values represent the corresponding log information. The sorting of logs by timestamp ensures that the log streams maintain chronological order when queried through Loki. This plugin’s support for secrets makes it easier to manage authentication parameters securely, while options for HTTP headers, gzip encoding, and TLS configuration enhance the adaptability and security of log transmission, fitting various deployment needs.

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"

Loki

[[outputs.loki]]
  ## The domain of Loki
  domain = "https://loki.domain.tld"

  ## Endpoint to write api
  # endpoint = "/loki/api/v1/push"

  ## Connection timeout, defaults to "5s" if not set.
  # timeout = "5s"

  ## Basic auth credential
  # username = "loki"
  # password = "pass"

  ## Additional HTTP headers
  # http_headers = {"X-Scope-OrgID" = "1"}

  ## If the request must be gzip encoded
  # gzip_request = false

  ## Optional TLS Config
  # tls_ca = "/etc/telegraf/ca.pem"
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key = "/etc/telegraf/key.pem"

  ## Sanitize Tag Names
  ## If true, all tag names will have invalid characters replaced with
  ## underscores that do not match the regex: ^[a-zA-Z_:][a-zA-Z0-9_:]*.
  # sanitize_label_names = false

  ## Metric Name Label
  ## Label to use for the metric name to when sending metrics. If set to an
  ## empty string, this will not add the label. This is NOT suggested as there
  ## is no way to differentiate between multiple metrics.
  # metric_name_label = "__name"

Input and output integration examples

ctrlX Data Layer

  1. 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.

  2. 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.

  3. 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.

  4. 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.

Loki

  1. Centralized Logging for Microservices: Utilize the Loki plugin to gather logs from multiple microservices running in a Kubernetes cluster. By directing logs to a centralized Loki instance, developers can monitor, search, and analyze logs from all services in one place, facilitating easier troubleshooting and performance monitoring. This setup streamlines operations and supports rapid response to issues across distributed applications.

  2. Real-Time Log Anomaly Detection: Combine Loki with monitoring tools to analyze log outputs in real-time for unusual patterns that could indicate system errors or security threats. Implementing anomaly detection on log streams enables teams to proactively identify and respond to incidents, thereby improving system reliability and enhancing security postures.

  3. Enhanced Log Processing with Gzip Compression: Configure the Loki plugin to utilize gzip compression for log transmission. This approach can reduce bandwidth usage and improve transmission speeds, especially beneficial in environments where network bandwidth may be a constraint. It’s particularly useful for high-volume logging applications where every byte counts and performance is critical.

  4. Multi-Tenancy Support with Custom Headers: Leverage the ability to add custom HTTP headers to segregate logs from different tenants in a multi-tenant application environment. By using the Loki plugin to send different headers for each tenant, operators can ensure proper log management and compliance with data isolation requirements, making it a versatile solution for SaaS applications.

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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