Fluentd and IoTDB 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.

This plugin saves Telegraf metrics to an Apache IoTDB backend, supporting session connection and data insertion.

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.

IoTDB

Apache IoTDB (Database for Internet of Things) is an IoT native database with high performance for data management and analysis, deployable on the edge and the cloud. Its light-weight architecture, high performance, and rich feature set create a perfect fit for massive data storage, high-speed data ingestion, and complex analytics in the IoT industrial fields. IoTDB deeply integrates with Apache Hadoop, Spark, and Flink, which further enhances its capabilities in handling large scale data and sophisticated processing tasks.

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",
  ]

IoTDB

[[outputs.iotdb]]
  ## Configuration of IoTDB server connection
  host = "127.0.0.1"
  # port = "6667"

  ## Configuration of authentication
  # user = "root"
  # password = "root"

  ## Timeout to open a new session.
  ## A value of zero means no timeout.
  # timeout = "5s"

  ## Configuration of type conversion for 64-bit unsigned int
  ## IoTDB currently DOES NOT support unsigned integers (version 13.x).
  ## 32-bit unsigned integers are safely converted into 64-bit signed integers by the plugin,
  ## however, this is not true for 64-bit values in general as overflows may occur.
  ## The following setting allows to specify the handling of 64-bit unsigned integers.
  ## Available values are:
  ##   - "int64"       --  convert to 64-bit signed integers and accept overflows
  ##   - "int64_clip"  --  convert to 64-bit signed integers and clip the values on overflow to 9,223,372,036,854,775,807
  ##   - "text"        --  convert to the string representation of the value
  # uint64_conversion = "int64_clip"

  ## Configuration of TimeStamp
  ## TimeStamp is always saved in 64bits int. timestamp_precision specifies the unit of timestamp.
  ## Available value:
  ## "second", "millisecond", "microsecond", "nanosecond"(default)
  # timestamp_precision = "nanosecond"

  ## Handling of tags
  ## Tags are not fully supported by IoTDB.
  ## A guide with suggestions on how to handle tags can be found here:
  ##     https://iotdb.apache.org/UserGuide/Master/API/InfluxDB-Protocol.html
  ##
  ## Available values are:
  ##   - "fields"     --  convert tags to fields in the measurement
  ##   - "device_id"  --  attach tags to the device ID
  ##
  ## For Example, a metric named "root.sg.device" with the tags `tag1: "private"`  and  `tag2: "working"` and
  ##  fields `s1: 100`  and `s2: "hello"` will result in the following representations in IoTDB
  ##   - "fields"     --  root.sg.device, s1=100, s2="hello", tag1="private", tag2="working"
  ##   - "device_id"  --  root.sg.device.private.working, s1=100, s2="hello"
  # convert_tags_to = "device_id"

  ## Handling of unsupported characters
  ## Some characters in different versions of IoTDB are not supported in path name
  ## A guide with suggetions on valid paths can be found here:
  ## for iotdb 0.13.x           -> https://iotdb.apache.org/UserGuide/V0.13.x/Reference/Syntax-Conventions.html#identifiers
  ## for iotdb 1.x.x and above  -> https://iotdb.apache.org/UserGuide/V1.3.x/User-Manual/Syntax-Rule.html#identifier
  ##
  ## Available values are:
  ##   - "1.0", "1.1", "1.2", "1.3"  -- enclose in `` the world having forbidden character 
  ##                                    such as @ $ # : [ ] { } ( ) space
  ##   - "0.13"                      -- enclose in `` the world having forbidden character 
  ##                                    such as space
  ##
  ## Keep this section commented if you don't want to sanitize the path
  # sanitize_tag = "1.3"

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.

IoTDB

1. Real-Time IoT Monitoring: Utilize the IoTDB plugin to gather sensor data from various IoT devices and save it in an Apache IoTDB backend, facilitating real-time monitoring of environmental conditions such as temperature and humidity. This use case enables organizations to analyze trends over time and make informed decisions based on historical data, while also utilizing IoTDB’s efficient storage and querying capabilities.

2. Smart Agriculture Data Collection: Use the IoTDB plugin to collect metrics from smart agriculture sensors deployed in fields. By transmitting moisture levels, nutrient content, and atmospheric conditions to IoTDB, farmers can access detailed insights into optimal planting and watering schedules, thus improving crop yields and resource management.

3. Energy Consumption Analytics: Leverage the IoTDB plugin to track energy consumption metrics from smart meters across a utility network. This integration enables analytics to identify peaks in usage and predict future consumption patterns, ultimately supporting energy conservation initiatives and improved utility management.

4. Automated Industrial Equipment Monitoring: Use this plugin to gather operational metrics from machinery in a manufacturing plant and store them in IoTDB for analysis. This setup can help identify inefficiencies, predictive maintenance needs, and operational anomalies, ensuring optimal performance and minimizing unexpected downtimes.

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.