Hashicorp Vault and DuckDB Integration

Input and output integration overview

The Hashicorp Vault plugin for Telegraf allows for the collection of metrics from Hashicorp Vault services, facilitating monitoring and operational insights.

This plugin enables Telegraf to write structured metrics into DuckDB using SQLite-compatible SQL connections, supporting lightweight local analytics and offline metric analysis.

Integration details

Hashicorp Vault

The Hashicorp Vault plugin is designed to collect metrics from Vault agents running within a cluster. It enables Telegraf, an agent for collecting and reporting metrics, to interface with the Vault services, typically listening on a local address such as http://127.0.0.1:8200. This plugin requires a valid token for authorization, ensuring secure access to the Vault API. Users must configure either a token directly or provide a path to a token file, enhancing flexibility in authentication methods. Proper configuration of the timeout and optional TLS settings further relates to the security and responsiveness of the metrics collection process. As Vault is a critical tool in managing secrets and protecting sensitive data, monitoring its performance and health through this plugin is essential for maintaining operational security and efficiency.

DuckDB

Use the Telegraf SQL plugin to write metrics into a local DuckDB database. DuckDB is an in-process OLAP database designed for efficient analytical queries on columnar data. Although it does not provide a traditional client-server interface, DuckDB can be accessed via SQLite-compatible drivers in embedded mode. This allows Telegraf to store time series metrics in DuckDB using SQL, enabling powerful analytics workflows using familiar SQL syntax, Jupyter notebooks, or integration with data science tools like Python and R. DuckDB’s columnar storage and vectorized execution make it ideal for compact and high-performance metric archives.

Configuration

Hashicorp Vault

[[inputs.vault]]
  ## URL for the Vault agent
  # url = "http://127.0.0.1:8200"

  ## Use Vault token for authorization.
  ## Vault token configuration is mandatory.
  ## If both are empty or both are set, an error is thrown.
  # token_file = "/path/to/auth/token"
  ## OR
  token = "s.CDDrgg5zPv5ssI0Z2P4qxJj2"

  ## Set response_timeout (default 5 seconds)
  # response_timeout = "5s"

  ## Optional TLS Config
  # tls_ca = /path/to/cafile
  # tls_cert = /path/to/certfile
  # tls_key = /path/to/keyfile

DuckDB

[[outputs.sql]]
  ## Use the SQLite driver to connect to DuckDB via Go's database/sql
  driver = "sqlite3"

  ## DSN should point to the DuckDB database file
  dsn = "file:/var/lib/telegraf/metrics.duckdb"

  ## SQL INSERT statement with placeholders for metrics
  table_template = "INSERT INTO metrics (timestamp, name, value, tags) VALUES (?, ?, ?, ?)"

  ## Optional: manage connection pooling
  # max_open_connections = 1
  # max_idle_connections = 1
  # conn_max_lifetime = "0s"

  ## DuckDB does not require TLS or authentication by default

Input and output integration examples

Hashicorp Vault

1. Centralized Secret Management Monitoring: Utilize the Vault plugin to monitor multiple Vault instances across a distributed system, allowing for a unified view of secret access patterns and system health. This setup can help DevOps teams quickly identify any anomalies in secret access, providing essential insights into security postures across different environments.

2. Audit Logging Integration: Configure this plugin to feed monitoring metrics into an audit logging system, enabling organizations to have a comprehensive view of their Vault interactions. By correlating audit logs with metrics, teams can investigate issues, optimize performance, and ensure compliance with security policies more effectively.

3. Performance Benchmarking During Deployments: During application deployments that interact with Vault, use the plugin to monitor the effects of those deployments on Vault performance. This allows engineering teams to understand how changes impact secret management workflows and to proactively address performance bottlenecks, ensuring smooth deployment processes.

4. Alerting for Threshold Exceedance: Integrate this plugin with alerting mechanisms to notify administrators when metrics exceed predefined thresholds. This proactive monitoring can help teams respond swiftly to potential issues, maintaining system reliability and uptime by allowing them to take action before any serious incidents arise.

DuckDB

1. Embedded Metric Warehousing for Notebooks: Write metrics to a local DuckDB file from Telegraf and analyze them in Jupyter notebooks using Python or R. This workflow supports reproducible analytics, ideal for data science experiments or offline troubleshooting.

2. Batch Time-Series Processing on the Edge: Use Telegraf with DuckDB on edge devices to log metrics locally in SQL format. The compact storage and fast analytical capabilities of DuckDB make it ideal for batch processing and low-bandwidth environments.

3. Exploratory Querying of Historical Metrics: Accumulate system metrics over time in DuckDB and perform exploratory data analysis (EDA) using SQL joins, window functions, and aggregates. This enables insights that go beyond what typical time-series dashboards provide.

4. Self-Contained Metric Snapshots: Use DuckDB as a portable metrics archive by shipping .duckdb files between systems. Telegraf can collect and store data in this format, and analysts can later load and query it using the DuckDB CLI or integrations with tools like Tableau and Apache Arrow.

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.