ctrlX Data Layer and OpenObserve Integration

Powerful performance with an easy integration, powered by Telegraf, the open source data connector built by InfluxData.

info

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.

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

This configuration pairs Telegraf’s HTTP output with OpenObserve’s native JSON ingestion API, turning any Telegraf agent into a first-class OpenObserve collector.

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.

OpenObserve

OpenObserve is an open source observability platform written in Rust that stores data cost-effectively on object storage or local disk. It exposes REST endpoints such as /api/{org}/ingest/metrics/_json that accept batched metric documents conforming to a concise JSON schema, making it an attractive drop-in replacement for Loki or Elasticsearch stacks. The Telegraf HTTP output plugin streams metrics to arbitrary HTTP targets; when the "data_format = "json"" serializer is selected, Telegraf batches its metric objects into a payload that matches OpenObserve’s ingestion contract. The plugin supports configurable batch size, custom headers, TLS, and compression, allowing operators to authenticate with Basic or Bearer tokens and to enforce back-pressure without additional collectors. By reusing existing Telegraf agents already collecting system, application, or SNMP data, organizations can funnel rich telemetry into OpenObserve dashboards and SQL-like analytics with minimal overhead, enabling unified observability, long-term retention, and real-time alerting without vendor lock-in.

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"

OpenObserve

[[outputs.http]]
  ## OpenObserve JSON metrics ingestion endpoint
  url = "https://api.openobserve.ai/api/default/ingest/metrics/_json"

  ## Use POST to push batches
  method = "POST"

  ## Basic auth header (base64 encoded "username:password")
  headers = { Authorization = "Basic dXNlcjpwYXNzd29yZA==" }

  ## Timeout for HTTP requests
  timeout = "10s"

  ## Override Content-Type to match OpenObserve expectation
  content_type = "application/json"

  ## Force Telegraf to batch and serialize metrics as JSON
  data_format = "json"

  ## JSON serializer specific options
  json_timestamp_units = "1ms"

  ## Uncomment to restrict batch size
  # batch_size = 5000

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.

OpenObserve

  1. Edge Device Health Mirror: Deploy Telegraf on thousands of industrial IoT devices to capture temperature, vibration, and power metrics, then use this output to push JSON batches to OpenObserve. Plant operators gain a real-time overview of machine health and can trigger maintenance based on anomalies without relying on heavyweight collectors.

  2. Blue-Green Deployment Canary: Attach a lightweight Telegraf sidecar to each Kubernetes release-candidate pod that scrapes /metrics and forwards container stats to a dedicated “canary” stream in OpenObserve. Continuous comparison of error rates between blue and green versions empowers the CI pipeline to auto-roll back poor performers within seconds.

  3. Multi-Tenant SaaS Billing Pipeline: Emit per-customer usage counters via Telegraf and tag them with tenant_id; the HTTP plugin posts them to OpenObserve where SQL reports aggregate usage into invoices, eliminating separate metering services and simplifying compliance audits.

  4. Security Threat Scoring: Fuse Suricata events and host resource metrics in Telegraf, deliver them to OpenObserve’s analytics engine, and run stream-processing rules that correlate spikes in suspicious traffic with CPU saturation to produce an actionable threat score and automatically open tickets in a SOAR platform.

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

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 Integration

Kafka 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 Integration

Kinesis 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