gNMI and DuckDB Integration
Powerful performance with an easy integration, powered by Telegraf, the open source data connector built by InfluxData.
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Time series database
Source: DB Engines
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Table of Contents
Powerful Performance, Limitless Scale
Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.
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Input and output integration overview
<p>The gNMI (gRPC Network Management Interface) Input Plugin collects telemetry data from network devices using the gNMI Subscribe method. It supports TLS for secure authentication and data transmission.</p>
<p>This plugin enables Telegraf to write structured metrics into DuckDB using SQLite-compatible SQL connections, supporting lightweight local analytics and offline metric analysis.</p>
Integration details
gNMI
<p>This input plugin is vendor-agnostic and can be used with any platform that supports the gNMI specification. It consumes telemetry data based on the gNMI Subscribe method, allowing for real-time monitoring of network devices.</p>
DuckDB
<p>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.</p>
Configuration
gNMI
DuckDB
Input and output integration examples
gNMI
<ol> <li> <p><strong>Monitoring Cisco Devices</strong>: Use the gNMI plugin to collect telemetry data from Cisco IOS XR, NX-OS, or IOS XE devices for performance monitoring.</p> </li> <li> <p><strong>Real-time Network Insights</strong>: With the gNMI plugin, network administrators can gain insights into real-time metrics such as interface statistics and CPU usage.</p> </li> <li> <p><strong>Secure Data Collection</strong>: Configure the gNMI plugin with TLS settings to ensure secure communication while collecting sensitive telemetry data from devices.</p> </li> <li> <p><strong>Flexible Data Handling</strong>: Use the subscription options to customize which telemetry data you want to collect based on specific needs or requirements.</p> </li> <li> <p><strong>Error Handling</strong>: The plugin includes troubleshooting options to handle common issues like missing metric names or TLS handshake failures.</p> </li> </ol>
DuckDB
<ol> <li> <p><strong>Embedded Metric Warehousing for Notebooks</strong>: 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.</p> </li> <li> <p><strong>Batch Time-Series Processing on the Edge</strong>: 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.</p> </li> <li> <p><strong>Exploratory Querying of Historical Metrics</strong>: 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.</p> </li> <li> <p><strong>Self-Contained Metric Snapshots</strong>: Use DuckDB as a portable metrics archive by shipping <code>.duckdb</code> 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.</p> </li> </ol>
Feedback
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Powerful Performance, Limitless Scale
Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.
See Ways to Get Started
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