ctrlX Data Layer and MariaDB 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.
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 plugin writes metrics from Telegraf directly into MariaDB using parameterized SQL INSERT statements, offering a flexible way to store metrics in structured, relational tables.
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.
MariaDB
The SQL output plugin in Telegraf enables direct writing of metrics into SQL-compatible databases like MariaDB by executing parameterized SQL statements. With support for the MySQL driver, the plugin seamlessly integrates with MariaDB for reliable, structured metric storage. This setup is ideal for users who prefer SQL-based analytics or want to store metrics alongside business data for unified querying. MariaDB is a community-developed, enterprise-grade fork of MySQL that emphasizes performance, security, and openness. The plugin supports inserting time series metrics into custom schemas, enabling flexible analytics and integrations with BI tools like Metabase or Grafana using SQL connectors.
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"
MariaDB
[[outputs.sql]]
## Database driver
## Valid options: mssql (Microsoft SQL Server), mysql (MySQL), pgx (Postgres),
## sqlite (SQLite3), snowflake (snowflake.com) clickhouse (ClickHouse)
driver = "mysql"
## Data source name
## The format of the data source name is different for each database driver.
## See the plugin readme for details.
data_source_name = "username:password@tcp(host:port)/dbname"
## 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})"
## SQL INSERT statement with placeholders. Telegraf will substitute values at runtime.
## table_template = "INSERT INTO metrics (timestamp, name, value, tags) VALUES (?, ?, ?, ?)"
## Table existence check template
## Available template variables:
## {TABLE} - tablename as a quoted identifier
table_exists_template = "SELECT 1 FROM {TABLE} LIMIT 1"
## Initialization SQL
init_sql = "SET sql_mode='ANSI_QUOTES';"
## 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
## NOTE: Due to the way TOML is parsed, tables must be at the END of the
## plugin definition, otherwise additional config options are read as part of the
## table
## Metric type to SQL type conversion
## The values on the left are the data types Telegraf has and the values on
## the right are the data types Telegraf will use when sending to a database.
##
## The database values used must be data types the destination database
## understands. It is up to the user to ensure that the selected data type is
## available in the database they are using. Refer to your database
## documentation for what data types are available and supported.
#[outputs.sql.convert]
# integer = "INT"
# real = "DOUBLE"
# text = "TEXT"
# timestamp = "TIMESTAMP"
# defaultvalue = "TEXT"
# unsigned = "UNSIGNED"
# bool = "BOOL"
# ## This setting controls the behavior of the unsigned value. By default the
# ## setting will take the integer value and append the unsigned value to it. The other
# ## option is "literal", which will use the actual value the user provides to
# ## the unsigned option. This is useful for a database like ClickHouse where
# ## the unsigned value should use a value like "uint64".
# # conversion_style = "unsigned_suffix"
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.
MariaDB
-
Business Intelligence Integration: Store application performance metrics directly into MariaDB and connect it to BI tools like Metabase or Apache Superset. This setup allows blending of operational data with business KPIs for unified dashboards, enhancing visibility across departments.
-
Compliance Reporting with Historical Metrics: Use this plugin to log metrics into MariaDB for audit and compliance use cases. The relational model enables precise querying of past performance indicators with timestamped entries, supporting regulatory documentation.
-
Custom Alerting Based on SQL Logic: Insert metrics into MariaDB and use custom SQL queries to define alert thresholds or conditions. Combined with cron jobs or scheduled scripts, this enables advanced alerting workflows not possible with traditional metric platforms.
-
IoT Sensor Metrics Storage: Collect sensor data from IoT devices via Telegraf and store it in MariaDB using a normalized schema. This approach is cost-effective and integrates well with existing SQL-based systems for real-time or historical analysis.
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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