gNMI and Parquet Integration
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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.
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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 writes metrics to parquet files, utilizing a schema based on the metrics grouped by name. It supports file rotation and buffered writing for optimal performance.</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>
Parquet
<p>The Parquet output plugin for Telegraf writes metrics to parquet files, which are columnar storage formats optimized for analytics. By default, this plugin groups metrics by their name, writing them to a single file. If a metric’s schema does not align with existing schemas, those metrics are dropped. The plugin generates an Apache Arrow schema based on all grouped metrics, ensuring that the schema reflects the union of all fields and tags. It operates in a buffered manner, meaning it temporarily holds metrics in memory before writing them to disk for efficiency. Parquet files require proper closure to ensure readability, and this is crucial when using the plugin, as improper closure can lead to unreadable files. Additionally, the plugin supports file rotation after specific time intervals, preventing overwrites of existing files and schema conflicts when a file with the same name already exists.</p>
Configuration
gNMI
Parquet
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>
Parquet
<ol> <li> <p><strong>Data Lake Ingestion</strong>: Utilize the Parquet plugin to store metrics from various sources into a data lake. By writing metrics in parquet format, you establish a standardized and efficient way to manage time-series data, enabling faster querying capabilities and seamless integration with analytics tools like Apache Spark or AWS Athena. This setup can significantly improve data retrieval times and analysis workflows.</p> </li> <li> <p><strong>Long-term Storage of Metrics</strong>: Implement the Parquet plugin in a monitoring setup where metrics are collected over time from multiple applications. This allows for long-term storage of performance data in a compact format, making it cost-effective to store vast amounts of historical data while preserving the ability for quick retrieval and analysis later on. By archiving metrics in parquet files, organizations can maintain compliance and create detailed reports from historical performance trends.</p> </li> <li> <p><strong>Analytics and Reporting</strong>: After writing metrics to parquet files, leverage tools like Apache Arrow or PyArrow to perform complex analytical queries directly on the files without needing to load all the data into memory. This can enhance reporting capabilities, allowing teams to generate insights and visualization from large datasets efficiently, thereby improving decision-making processes based on accurate, up-to-date performance metrics.</p> </li> <li> <p><strong>Integrating with Data Warehouses</strong>: Use the Parquet plugin as part of a data integration pipeline that feeds into a modern data warehouse. By converting metrics to parquet format, the data can be easily ingested by systems like Snowflake or Google BigQuery, enabling powerful analytics and business intelligence capabilities that drive actionable insights from the collected metrics.</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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