InfiniBand and DuckDB Integration

Input and output integration overview

The InfiniBand Telegraf plugin collects performance metrics from all InfiniBand devices installed on a Linux system, providing essential insights for monitoring network performance and reliability.

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

InfiniBand

This plugin gathers statistics for all InfiniBand devices and ports on the system. InfiniBand is a high-speed networking technology commonly used in high-performance computing and enterprise data centers. The plugin retrieves various performance counters from the system’s InfiniBand devices located in /sys/class/infiniband/<dev>/port/<port>/counters/. The metrics depend on the specific InfiniBand hardware and include various packet and error statistics that are essential for monitoring network health and performance. By utilizing this plugin, users can gain insights into the operational status of their InfiniBand networks, helping to identify potential issues and optimize performance.

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

InfiniBand

# Gets counters from all InfiniBand cards and ports installed
# This plugin ONLY supports Linux
[[inputs.infiniband]]
  # no configuration

  ## Collect RDMA counters
  # gather_rdma = false

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

InfiniBand

1. Performance Monitoring in High-Performance Computing (HPC): Monitor the performance metrics of InfiniBand interconnects in a high-performance computing cluster. By analyzing metrics such as packet errors and throughput, system administrators can ensure optimal operation and quickly identify any performance degradation. This setup enhances the reliability of computational tasks by allowing timely interventions based on accurate monitoring data.

2. Network Health Audits: Perform routine health checks of InfiniBand networks. The detailed metrics gathered, such as excessive buffer overruns and link integrity errors, provide valuable insights for network audits. By establishing baseline performance and watching for anomalies, IT professionals can ensure the stability and performance of critical infrastructures.

3. Integration with Alerting Systems: Set up the InfiniBand plugin to work in conjunction with alerting systems to trigger notifications based on performance thresholds. For instance, if the number of link errors exceeds a predefined limit, an alert can be sent to the network operations team. This proactive approach ensures that potential issues are addressed before they impact business operations.

4. Data Visualization Dashboards: Feed InfiniBand metrics to a visualization tool to create dashboards that display the real-time performance of the network. This can help stakeholders visualize critical data such as packet transmission rates and errors, facilitating better decision-making regarding network management and capacity planning.

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

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