Webhooks and MariaDB Integration

Input and output integration overview

The Webhooks plugin allows Telegraf to receive and process HTTP requests from various external services via webhooks. This plugin enables users to collect real-time metrics and events and integrate them into their monitoring solutions.

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

Webhooks

This Telegraf plugin is designed to act as a webhook listener by starting an HTTP server that registers multiple webhook endpoints. It provides a way to collect events from various services by capturing HTTP requests sent to defined paths. Each service can be configured with its specific authentication details and request handling options. The plugin stands out by allowing integration with any Telegraf output plugin, making it versatile for event-driven architectures. By enabling efficient reception of events, it opens possibilities for real-time monitoring and response systems, essential for modern applications that need instantaneous event handling and processing.

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

Webhooks

[[inputs.webhooks]]
  ## Address and port to host Webhook listener on
  service_address = ":1619"

  ## Maximum duration before timing out read of the request
  # read_timeout = "10s"
  ## Maximum duration before timing out write of the response
  # write_timeout = "10s"

  [inputs.webhooks.filestack]
    path = "/filestack"

    ## HTTP basic auth
    #username = ""
    #password = ""

  [inputs.webhooks.github]
    path = "/github"
    # secret = ""

    ## HTTP basic auth
    #username = ""
    #password = ""

  [inputs.webhooks.mandrill]
    path = "/mandrill"

    ## HTTP basic auth
    #username = ""
    #password = ""

  [inputs.webhooks.rollbar]
    path = "/rollbar"

    ## HTTP basic auth
    #username = ""
    #password = ""

  [inputs.webhooks.papertrail]
    path = "/papertrail"

    ## HTTP basic auth
    #username = ""
    #password = ""

  [inputs.webhooks.particle]
    path = "/particle"

    ## HTTP basic auth
    #username = ""
    #password = ""

  [inputs.webhooks.artifactory]
    path = "/artifactory"

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

Webhooks

1. Real-time Notifications from Github: Integrate the Webhooks Input Plugin with Github to receive real-time notifications for events such as pull requests, commits, and issues. This allows development teams to instantly monitor crucial changes and updates in their repositories, improving collaboration and response times.

2. Automated Alerting with Rollbar: Use this plugin to listen for errors reported from Rollbar, enabling teams to react swiftly to bugs and issues in production. By forwarding these alerts into a centralized monitoring system, teams can prioritize their responses based on severity and prevent escalated downtime.

3. Performance Monitoring from Filestack: Capture events from Filestack to track file uploads, transformations, and errors. This setup helps businesses understand user interactions with file management processes, optimize workflow, and ensure high availability of file services.

4. Centralized Logging with Papertrail: Tie in all logs sent to Papertrail through webhooks, allowing you to consolidate your logging strategy. With real-time log forwarding, teams can analyze trends and anomalies efficiently, ensuring they maintain visibility over critical operations.

MariaDB

1. 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.

2. 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.

3. 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.

4. 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.