Phillips Hue Bridge and PostgreSQL Integration

Input and output integration overview

This plugin gathers status from Hue Bridge devices using the CLIP API interface.

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

Integration details

Phillips Hue Bridge

The Hue Bridge plugin allows users to gather real-time status from Philips Hue Bridge devices utilizing the CLIP API interface. By communicating with Hue Bridges, this plugin is capable of retrieving various metrics related to home lighting and environmental conditions. It offers multiple schemes for accessing the bridges, such as local LAN, cloud, and mDNS, ensuring flexibility in deployment scenarios. The plugin can handle diverse configurations such as room assignments for devices, which optimizes the evaluation of statuses, especially in environments with many devices. Furthermore, it provides various monitoring metrics applicable to lights, temperature sensors, motion sensors, and device power status, thereby enabling comprehensive insights into a smart home setup. The configuration options allow users to tailor their connections to optimize performance and security, including optional TLS configurations for secure communication.

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

Phillips Hue Bridge

[[inputs.huebridge]]
  ## URL of bridges to query in the form ://:@
/
  ## See documentation for available schemes.
  bridges = [ "address://:@/" ]

  ## Manual device to room assignments to apply during status evaluation.
  ## E.g. for motion sensors which are reported without a room assignment.
  # room_assignments = { "Motion sensor 1" = "Living room", "Motion sensor 2" = "Corridor" }

  ## Timeout for gathering information
  # timeout = "10s"

  ## Optional TLS Config
  # tls_ca = "/etc/telegraf/ca.pem"
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key = "/etc/telegraf/key.pem"
  # tls_key_pwd = "secret"
  ## Use TLS but skip chain & host verification
  # insecure_skip_verify = false
</code></pre>

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

Phillips Hue Bridge

1. Automated Lighting Control Based on Room Occupancy: Utilize the Hue Bridge plugin to monitor motion sensors within various rooms of a home. When motion is detected, the system can automatically trigger the lights to turn on, providing convenience and energy efficiency. This integration could significantly enhance user experience and preferences, adapting the lighting to occupancy levels without manual intervention.

2. Environmental Monitoring in Smart Homes: Implement the Hue Bridge plugin to track temperature and light levels within the house. By continuously monitoring these metrics, users can create a comfortable indoor climate, adjusting heating and cooling systems based on temperature trends or activating lights based on light levels detected. This data-driven approach leads to smart home automation that responds to actual environmental conditions.

3. Integration with Home Automation Systems: Leverage this plugin to integrate Philips Hue Bridge statistics into broader home automation frameworks. For example, collecting light and temperature data can feed into a centralized dashboard that provides homeowners with insights about their energy usage patterns. Environments can be programmed to respond proactively to user habits, promoting efficiency and energy conservation.

4. Battery Monitoring for Smart Devices: Use the Hue Bridge plugin to monitor battery levels across various connected smart devices. By being alerted about low battery states, homeowners can take timely actions to replace or recharge devices, preventing outages and ensuring smooth operation of their smart home systems.

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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