Tail and Apache Hudi Integration

Input and output integration overview

<p>The Tail Telegraf plugin collects metrics by tailing specified log files, capturing new log entries in real-time for further analysis.</p>

<p>Writes metrics to Parquet files via Telegraf’s Parquet output plugin, preparing them for ingestion into Apache Hudi’s lakehouse architecture.</p>

Integration details

Tail

<p>The tail plugin is designed to continuously monitor and parse log files, making it ideal for real-time log analysis and monitoring. It mimics the functionality of the Unix <code>tail</code> command, allowing users to specify a file or pattern and begin reading new lines as they are added. Key features include the ability to follow log-rotated files, start reading from the end of a file, and support various parsing formats for the log messages. Users can customize the plugin through various configuration options, such as specifying file encoding, the method for watching file updates, and filter settings for processing log data. This plugin is particularly valuable in environments where log data is critical for monitoring application performance and diagnosing issues.</p>

Apache Hudi

<p>This configuration leverages Telegraf’s Parquet plugin to serialize metrics into columnar Parquet files suitable for downstream ingestion by Apache Hudi. The plugin writes metrics grouped by metric name into files in a specified directory, buffering writes for efficiency and optionally rotating files on timers. It considers schema compatibility—metrics with incompatible schemas are dropped—ensuring consistency. Apache Hudi can then consume these Parquet files via tools like DeltaStreamer or Spark jobs, enabling transactional ingestion, time-travel queries, and upserts on your time series data.</p>

Configuration

Tail

Apache Hudi

Input and output integration examples

Tail

<ol> <li> <p><strong>Real-Time Server Health Monitoring</strong>: Implement the Tail plugin to parse web server access logs in real-time, providing immediate visibility into user activity, error rates, and performance metrics. By visualizing this log data, operations teams can quickly identify and respond to spikes in traffic or errors, enhancing system reliability and user experience.</p> </li> <li> <p><strong>Centralized Log Management</strong>: Utilize the Tail plugin to aggregate logs from multiple sources across a distributed system. By configuring each service to send its logs to a centralized location via the Tail plugin, teams can simplify log analysis and ensure that all relevant data is accessible from a single interface, streamlining troubleshooting processes.</p> </li> <li> <p><strong>Security Incident Detection</strong>: Use this plugin to monitor authentication logs for unauthorized access attempts or suspicious activity. By setting up alerts on certain log messages, teams can leverage this plugin to enhance security postures and respond promptly to potential security threats, reducing the risk of breaches and increasing overall system integrity.</p> </li> <li> <p><strong>Dynamic Application Performance Insights</strong>: Integrate with analytics tools to create real-time dashboards that display application performance metrics based on log data. This setup not only helps developers diagnose bottlenecks and inefficiencies but also allows for proactive performance tuning and resource allocation, optimizing application behavior under varying loads.</p> </li> </ol>

Apache Hudi

<ol> <li> <p><strong>Transactional Lakehouse Metrics</strong>: Buffer and write Web service metrics as Parquet files for DeltaStreamer to ingest into Hudi, enabling upserts, ACID compliance, and time-travel on historical performance data.</p> </li> <li> <p><strong>Edge Device Batch Analytics</strong>: Telegraf running on IoT gateways writes metrics to Parquet locally, where periodic Spark jobs ingest them into Hudi for long-term analytics and traceability.</p> </li> <li> <p><strong>Schema-Enforced Abnormal Metric Handling</strong>: Use Parquet plugin’s strict schema-dropping behavior to prevent malformed or unexpected metric changes. Hudi ingestion then guarantees consistent schema and data quality in downstream datasets.</p> </li> <li> <p><strong>Data Platform Integration</strong>: Store Telegraf metrics as Parquet files in an S3/ADLS landing zone. Hudi’s Spark-based ingestion pipeline then loads them into a unified, queryable lakehouse with business events and logs.</p> </li> </ol>

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.