ctrlX Data Layer and Snowflake 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.

Telegraf’s SQL plugin allows seamless metric storage in SQL databases. When configured for Snowflake, it employs a specialized DSN format and dynamic table creation to map metrics to the appropriate schema.

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.

Snowflake

Telegraf’s SQL plugin is engineered to dynamically write metrics into an SQL database by creating tables and columns based on the incoming data. When configured for Snowflake, it employs the gosnowflake driver, which uses a DSN that encapsulates credentials, account details, and database configuration in a compact format. This setup allows for the automatic generation of tables where each metric is recorded with precise timestamps, thereby ensuring detailed historical tracking. Although the integration is considered experimental, it leverages Snowflake’s powerful data warehousing capabilities, making it suitable for scalable, cloud-based analytics and reporting solutions.

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"

Snowflake

[[outputs.sql]]
  ## Database driver
  ## Valid options: mssql (Microsoft SQL Server), mysql (MySQL), pgx (Postgres),
  ## sqlite (SQLite3), snowflake (snowflake.com), clickhouse (ClickHouse)
  driver = "snowflake"

  ## Data source name
  ## For Snowflake, the DSN format typically includes the username, password, account identifier, and optional warehouse, database, and schema.
  ## Example DSN: "username:password@account/warehouse/db/schema"
  data_source_name = "username:password@account/warehouse/db/schema"

  ## Timestamp column name
  timestamp_column = "timestamp"

  ## Table creation template
  ## Available template variables:
  ##  {TABLE}        - table name as a quoted identifier
  ##  {TABLELITERAL} - table name as a quoted string literal
  ##  {COLUMNS}      - column definitions (list of quoted identifiers and types)
  table_template = "CREATE TABLE {TABLE} ({COLUMNS})"

  ## Table existence check template
  ## Available template variables:
  ##  {TABLE} - table name as a quoted identifier
  table_exists_template = "SELECT 1 FROM {TABLE} LIMIT 1"

  ## Initialization SQL (optional)
  init_sql = ""

  ## Maximum amount of time a connection may be idle. "0s" means connections are never closed due to idle time.
  connection_max_idle_time = "0s"

  ## Maximum amount of time a connection may be reused. "0s" means connections are never closed due to age.
  connection_max_lifetime = "0s"

  ## Maximum number of connections in the idle connection pool. 0 means unlimited.
  connection_max_idle = 2

  ## Maximum number of open connections to the database. 0 means unlimited.
  connection_max_open = 0

  ## Metric type to SQL type conversion
  ## Defaults to ANSI/ISO SQL types unless overridden. Adjust if needed for Snowflake compatibility.
  #[outputs.sql.convert]
  #  integer       = "INT"
  #  real          = "DOUBLE"
  #  text          = "TEXT"
  #  timestamp     = "TIMESTAMP"
  #  defaultvalue  = "TEXT"
  #  unsigned      = "UNSIGNED"
  #  bool          = "BOOL"

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.

Snowflake

  1. Cloud-Based Data Lake Integration: Utilize the plugin to stream real-time metrics from various sources into Snowflake, enabling the creation of a centralized data lake. This integration supports complex analytics and machine learning workflows on cloud data.

  2. Dynamic Business Intelligence Dashboards: Leverage the plugin to automatically generate tables from incoming metrics and feed them into BI tools. This allows businesses to create dynamic dashboards that visualize performance trends and operational insights without manual schema management.

  3. Scalable IoT Analytics: Deploy the plugin to capture high-frequency data from IoT devices into Snowflake. This use case facilitates the aggregation and analysis of sensor data, enabling predictive maintenance and real-time monitoring at scale.

  4. Historical Trend Analysis for Compliance: Use the plugin to log and archive detailed metric data in Snowflake, which can then be queried for long-term trend analysis and compliance reporting. This setup ensures that organizations can maintain a robust audit trail and perform forensic analysis if needed.

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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