Fluentd and CrateDB 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.

The CrateDB plugin facilitates the writing of metrics to a CrateDB database, leveraging its PostgreSQL-compatible protocol to ensure a seamless experience for users.

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.

CrateDB

This plugin writes to CrateDB via its PostgreSQL protocol, allowing for metrics to be efficiently stored in a scalable database. CrateDB is designed for high-speed analytics, supporting time-series data and complicated queries, making it ideal for applications that require fast ingestion and analysis of large datasets. By utilizing the PostgreSQL protocol, the CrateDB output plugin ensures compatibility with existing PostgreSQL client libraries and tools, enabling a smooth integration for users who are already familiar with PostgreSQL’s ecosystem. The plugin provides options such as automatic table creation, connection parameters, and query timeouts, offering flexibility in how metrics are handled and stored within the database.

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",
  ]

CrateDB

[[outputs.cratedb]]
  ## Connection parameters for accessing the database see
  ##   https://pkg.go.dev/github.com/jackc/pgx/v4#ParseConfig
  ## for available options
  url = "postgres://user:password@localhost/schema?sslmode=disable"

  ## Timeout for all CrateDB queries.
  # timeout = "5s"

  ## Name of the table to store metrics in.
  # table = "metrics"

  ## If true, and the metrics table does not exist, create it automatically.
  # table_create = false

  ## The character(s) to replace any '.' in an object key with
  # key_separator = "_"

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.

CrateDB

1. Real-Time Analytics for IoT Devices: Collect and store metrics from thousands of IoT devices. By setting up a dynamic metrics table for each device, users can perform real-time analytics on the collected data, enabling quick insights into device performance, patterns, and potential failures. This setup benefits from CrateDB’s ability to handle high-throughput data ingestion while providing the necessary analytics capabilities to derive actionable insights.

2. Website Performance Monitoring: Track key performance metrics from web applications, such as request latency and user activity. By storing metrics in CrateDB, teams can leverage the power of SQL-like queries to analyze traffic patterns, user engagement, and server performance over time, leading to optimized application performance and enhanced user experiences.

3. Financial Transaction Analysis: Manage large volumes of financial transaction data for real-time fraud detection and analysis. With CrateDB’s scalable infrastructure, users can store, query, and analyze transaction metrics efficiently, allowing for the detection of anomalies and illicit activities based on transaction patterns and trends.

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.