Fluentd and Apache Hudi Integration

Input and output integration overview

The Fluentd Input Plugin gathers metrics from Fluentd’s in_monitor plugin endpoint. It provides insights into various plugin metrics while allowing for custom configurations to reduce series cardinality.

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

Integration details

Fluentd

This plugin gathers metrics from the Fluentd plugin endpoint provided by the in_monitor plugin. It reads data from the /api/plugin.json resource and allows exclusion of specific plugins based on their type.

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

Fluentd

[[inputs.fluentd]]
  ## This plugin reads information exposed by fluentd (using /api/plugins.json endpoint).
  ##
  ## Endpoint:
  ## - only one URI is allowed
  ## - https is not supported
  endpoint = "http://localhost:24220/api/plugins.json"

  ## Define which plugins have to be excluded (based on "type" field - e.g. monitor_agent)
  exclude = [
    "monitor_agent",
    "dummy",
  ]

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

Fluentd

1. Basic Configuration: Set up the Fluentd Input Plugin to gather metrics from your Fluentd instance’s monitoring endpoint, ensuring you are able to track performance and usage statistics.
2. Excluding Plugins: Use the exclude option to ignore specific plugins’ metrics that are not necessary for your monitoring needs, streamlining data collection and focusing on what matters.
3. Custom Plugin ID: Implement the @id parameter in your Fluentd configuration to maintain a consistent plugin_id, which helps avoid issues with high series cardinality during frequent restarts.

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.