Fireboard and Apache Hudi Integration

Input and output integration overview

The Fireboard plugin enables users to gather real-time temperature readings from Fireboard thermometers using the Fireboard REST API.

Writes metrics to Parquet files via Telegraf’s Parquet output plugin, preparing them for ingestion into Apache Hudi’s lakehouse architecture.

Integration details

Fireboard

This plugin gathers real-time temperature data from Fireboard thermometers. Fireboard is a smart thermometer system that utilizes a REST API to provide user access to temperature monitoring. This plugin allows users to retrieve temperature readings efficiently, utilizing the provided authentication token. It can be configured with an optional server URL and custom HTTP timeout settings, providing flexibility depending on the user’s network conditions or potential changes to the Fireboard API. The metrics captured are essential for monitoring environments that require precise temperature control, thereby aiding in applications such as cooking, brewing, or any scenario where temperature variations are critical.

Apache Hudi

This configuration leverages Telegraf’s Parquet plugin to serialize metrics into columnar Parquet files suitable for downstream ingestion by Apache Hudi. The plugin writes metrics grouped by metric name into files in a specified directory, buffering writes for efficiency and optionally rotating files on timers. It considers schema compatibility—metrics with incompatible schemas are dropped—ensuring consistency. Apache Hudi can then consume these Parquet files via tools like DeltaStreamer or Spark jobs, enabling transactional ingestion, time-travel queries, and upserts on your time series data.

Configuration

Fireboard

[[inputs.fireboard]]
  ## Specify auth token for your account
  auth_token = "invalidAuthToken"
  ## You can override the fireboard server URL if necessary
  # url = https://fireboard.io/api/v1/devices.json
  ## You can set a different http_timeout if you need to
  ## You should set a string using an number and time indicator
  ## for example "12s" for 12 seconds.
  # http_timeout = "4s"

Apache Hudi

[[outputs.parquet]]
  ## Directory to write parquet files in. If a file already exists the output
  ## will attempt to continue using the existing file.
  directory = "/var/lib/telegraf/hudi_metrics"

  ## File rotation interval (default is no rotation)
  # rotation_interval = "1h"

  ## Buffer size before writing (default is 1000 metrics)
  # buffer_size = 1000

  ## Optional: compression codec (snappy, gzip, etc.)
  # compression_codec = "snappy"

  ## When grouping metrics, each metric name goes to its own file
  ## If a metric’s schema doesn’t match the existing schema, it will be dropped

Input and output integration examples

Fireboard

1. Smart Cooking Assistant: Integrate the Fireboard plugin into a smart kitchen ecosystem to monitor and adjust cooking temperatures in real-time. This setup can leverage the temperature data to automate processes like turning on or off heating elements based on the current cooking stage, ensuring optimal results.

2. Remote Brewing Monitoring: Use this plugin as part of a remote brewing setup for beer production. Brewers can monitor temperatures from multiple fireboards placed in different tanks and receive alerts when temperatures deviate from desired ranges, allowing for timely interventions.

3. Environmental Monitoring System: Incorporate this plugin into a broader environmental monitoring system that tracks temperature changes in various settings, from server rooms to greenhouses. This data can help maintain optimal conditions and can even be tied to automated cooling or heating systems for efficient climate control.

4. Automated Alerting for Temperature Sensitive Products: Employ the Fireboard plugin to monitor temperatures of products requiring specific storage conditions, such as pharmaceuticals or perishables. When temperature thresholds are breached, automated alerts could be sent to management systems to initiate corrective actions, thereby preventing spoilage.

Apache Hudi

1. Transactional Lakehouse Metrics: Buffer and write Web service metrics as Parquet files for DeltaStreamer to ingest into Hudi, enabling upserts, ACID compliance, and time-travel on historical performance data.

2. Edge Device Batch Analytics: Telegraf running on IoT gateways writes metrics to Parquet locally, where periodic Spark jobs ingest them into Hudi for long-term analytics and traceability.

3. Schema-Enforced Abnormal Metric Handling: Use Parquet plugin’s strict schema-dropping behavior to prevent malformed or unexpected metric changes. Hudi ingestion then guarantees consistent schema and data quality in downstream datasets.

4. Data Platform Integration: Store Telegraf metrics as Parquet files in an S3/ADLS landing zone. Hudi’s Spark-based ingestion pipeline then loads them into a unified, queryable lakehouse with business events and logs.

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.