ctrlX Data Layer and PostgreSQL Integration

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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 Telegraf PostgreSQL plugin allows you to efficiently write metrics to a PostgreSQL database while automatically managing the database 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.

PostgreSQL

The PostgreSQL plugin enables users to write metrics to a PostgreSQL database or a compatible database, providing robust support for schema management by automatically updating missing columns. The plugin is designed to facilitate integration with monitoring solutions, allowing users to efficiently store and manage time series data. It offers configurable options for connection settings, concurrency, and error handling, and supports advanced features such as JSONB storage for tags and fields, foreign key tagging, templated schema modifications, and support for unsigned integer data types through the pguint extension.

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"

PostgreSQL

# Publishes metrics to a postgresql database
[[outputs.postgresql]]
  ## Specify connection address via the standard libpq connection string:
  ##   host=... user=... password=... sslmode=... dbname=...
  ## Or a URL:
  ##   postgres://[user[:password]]@localhost[/dbname]?sslmode=[disable|verify-ca|verify-full]
  ## See https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING
  ##
  ## All connection parameters are optional. Environment vars are also supported.
  ## e.g. PGPASSWORD, PGHOST, PGUSER, PGDATABASE
  ## All supported vars can be found here:
  ##  https://www.postgresql.org/docs/current/libpq-envars.html
  ##
  ## Non-standard parameters:
  ##   pool_max_conns (default: 1) - Maximum size of connection pool for parallel (per-batch per-table) inserts.
  ##   pool_min_conns (default: 0) - Minimum size of connection pool.
  ##   pool_max_conn_lifetime (default: 0s) - Maximum age of a connection before closing.
  ##   pool_max_conn_idle_time (default: 0s) - Maximum idle time of a connection before closing.
  ##   pool_health_check_period (default: 0s) - Duration between health checks on idle connections.
  # connection = ""

  ## Postgres schema to use.
  # schema = "public"

  ## Store tags as foreign keys in the metrics table. Default is false.
  # tags_as_foreign_keys = false

  ## Suffix to append to table name (measurement name) for the foreign tag table.
  # tag_table_suffix = "_tag"

  ## Deny inserting metrics if the foreign tag can't be inserted.
  # foreign_tag_constraint = false

  ## Store all tags as a JSONB object in a single 'tags' column.
  # tags_as_jsonb = false

  ## Store all fields as a JSONB object in a single 'fields' column.
  # fields_as_jsonb = false

  ## Name of the timestamp column
  ## NOTE: Some tools (e.g. Grafana) require the default name so be careful!
  # timestamp_column_name = "time"

  ## Type of the timestamp column
  ## Currently, "timestamp without time zone" and "timestamp with time zone"
  ## are supported
  # timestamp_column_type = "timestamp without time zone"

  ## Templated statements to execute when creating a new table.
  # create_templates = [
  #   '''CREATE TABLE {{ .table }} ({{ .columns }})''',
  # ]

  ## Templated statements to execute when adding columns to a table.
  ## Set to an empty list to disable. Points containing tags for which there is no column will be skipped. Points
  ## containing fields for which there is no column will have the field omitted.
  # add_column_templates = [
  #   '''ALTER TABLE {{ .table }} ADD COLUMN IF NOT EXISTS {{ .columns|join ", ADD COLUMN IF NOT EXISTS " }}''',
  # ]

  ## Templated statements to execute when creating a new tag table.
  # tag_table_create_templates = [
  #   '''CREATE TABLE {{ .table }} ({{ .columns }}, PRIMARY KEY (tag_id))''',
  # ]

  ## Templated statements to execute when adding columns to a tag table.
  ## Set to an empty list to disable. Points containing tags for which there is no column will be skipped.
  # tag_table_add_column_templates = [
  #   '''ALTER TABLE {{ .table }} ADD COLUMN IF NOT EXISTS {{ .columns|join ", ADD COLUMN IF NOT EXISTS " }}''',
  # ]

  ## The postgres data type to use for storing unsigned 64-bit integer values (Postgres does not have a native
  ## unsigned 64-bit integer type).
  ## The value can be one of:
  ##   numeric - Uses the PostgreSQL "numeric" data type.
  ##   uint8 - Requires pguint extension (https://github.com/petere/pguint)
  # uint64_type = "numeric"

  ## When using pool_max_conns>1, and a temporary error occurs, the query is retried with an incremental backoff. This
  ## controls the maximum backoff duration.
  # retry_max_backoff = "15s"

  ## Approximate number of tag IDs to store in in-memory cache (when using tags_as_foreign_keys).
  ## This is an optimization to skip inserting known tag IDs.
  ## Each entry consumes approximately 34 bytes of memory.
  # tag_cache_size = 100000

  ## Enable & set the log level for the Postgres driver.
  # log_level = "warn" # trace, debug, info, warn, error, none

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.

PostgreSQL

  1. Real-Time Analytics with Complex Queries: Leverage the PostgreSQL plugin to store metrics from various sources in a PostgreSQL database, enabling real-time analytics using complex queries. This setup can help data scientists and analysts uncover patterns and trends, as they manipulate relational data across multiple tables while utilizing PostgreSQL’s robust query optimization features. Specifically, users can create sophisticated reports with JOIN operations across different metric tables, revealing insights that would typically remain hidden in embedded systems.

  2. Integrating with TimescaleDB for Time-Series Data: Utilize the PostgreSQL plugin within a TimescaleDB instance to efficiently handle and analyze time-series data. By implementing hypertables, users can achieve greater performance and partitioning of topics over the time dimension. This integration allows users to run analytical queries over large amounts of time-series data while retaining the full power of PostgreSQL’s SQL queries, ensuring reliability and efficiency in metrics analysis.

  3. Data Versioning and Historical Analysis: Implement a strategy using the PostgreSQL plugin to maintain different versions of metrics over time. Users can set up an immutable data table structure where older versions of tables are retained, enabling easy historical analysis. This approach not only provides insights into data evolution but also aids compliance with data retention policies, ensuring that the historical integrity of the datasets remains intact.

  4. Dynamic Schema Management for Evolving Metrics: Use the plugin’s templating capabilities to create a dynamically changing schema that responds to metric variations. This use case allows organizations to adapt their data structure as metrics evolve, adding necessary fields and ensuring adherence to data integrity policies. By leveraging templated SQL commands, users can extend their database without manual intervention, facilitating agile data management practices.

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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.

See Ways to Get Started

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