Kafka and New Relic 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
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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>This plugin allows you to gather metrics from Kafka topics in real-time, enhancing data monitoring and collection capabilities within your Telegraf setup.</p>
<p>This plugin allows the sending of metrics to New Relic Insights using the Metrics API, enabling effective monitoring and analysis of application performance.</p>
Integration details
Kafka
<p>The Kafka Telegraf plugin is designed to read data from Kafka topics and create metrics using supported input data formats. As a service input plugin, it listens continuously for incoming metrics and events, differing from standard input plugins that operate at fixed intervals. This particular plugin can utilize features from various Kafka versions and is capable of consuming messages from specified topics, applying configurations such as security credentials using SASL, and managing message processing with options for message offsets and consumer groups. The flexibility of this plugin allows it to handle a wide array of message formats and use cases, making it a valuable asset for applications relying on Kafka for data ingestion.</p>
New Relic
<p>This plugin writes metrics to New Relic Insights utilizing the Metrics API, which provides a robust mechanism for sending time series data to the New Relic platform. Users must first obtain an Insights API Key to authenticate and authorize their data submissions. The plugin is designed to facilitate easy integration with New Relic’s monitoring and analytics capabilities, supporting a variety of metric types and allowing for efficient data handling. Core features include the ability to add prefixes to metrics for better identification, customizable timeouts for API requests, and support for proxy settings to enhance connectivity. It is essential for users to configure these options according to their requirements, enabling seamless data flow into New Relic for comprehensive real-time analytics and insights.</p>
Configuration
Kafka
New Relic
Input and output integration examples
Kafka
<ol> <li> <p><strong>Real-Time Data Processing</strong>: Use the Kafka plugin to feed live data from a Kafka topic into a monitoring system. This can be particularly useful for applications that require instant feedback on performance metrics or user activity, allowing businesses to react more swiftly to changing conditions in their environments.</p> </li> <li> <p><strong>Dynamic Metrics Collection</strong>: Leverage this plugin to dynamically adjust the metrics being captured based on events occurring within Kafka. For instance, by integrating with other services, users can have the plugin reconfigure itself on-the-fly, ensuring relevant metrics are always collected according to the needs of the business or application.</p> </li> <li> <p><strong>Centralized Logging and Monitoring</strong>: Implement a centralized logging system using the Kafka Consumer Plugin to aggregate logs from multiple services into a unified monitoring dashboard. This setup can help identify issues across different services and improve overall system observability and troubleshooting capabilities.</p> </li> <li> <p><strong>Anomaly Detection System</strong>: Combine Kafka with machine learning algorithms for real-time anomaly detection. By constantly analyzing streaming data, this setup can automatically identify unusual patterns, triggering alerts and mitigating potential issues more effectively.</p> </li> </ol>
New Relic
<ol> <li> <p><strong>Application Performance Monitoring</strong>: Use the New Relic Telegraf plugin to send application performance metrics from a web service to New Relic Insights. By integrating this plugin, developers can collect data such as response times, error rates, and throughput, enabling teams to monitor application health in real-time and resolve issues quickly before they impact users. This setup promotes proactive management of application performance and user experience.</p> </li> <li> <p><strong>Infrastructure Metrics Aggregation</strong>: Leverage this plugin to aggregate and send system-level metrics (CPU usage, memory consumption, etc.) from various servers to New Relic. This helps system administrators maintain an comprehensive view of infrastructure performance, facilitating capacity planning and identifying potential bottlenecks. By centralizing metrics in New Relic, teams can visualize trends over time and make informed decisions regarding resource allocation.</p> </li> <li> <p><strong>Dynamic Metric Naming for Multi-tenant Applications</strong>: Implement dynamic prefixing with the metric_prefix option to differentiate between different tenants in a multi-tenant application. By configuring the plugin to include a unique identifier per tenant in the metric names, teams can analyze usage patterns and performance metrics per tenant. This provides valuable insights into tenant behavior, supporting tailored optimizations and enhancing service quality across different customer segments.</p> </li> <li> <p><strong>Real-time Anomaly Detection</strong>: Combine the New Relic plugin with alerting mechanisms to trigger notifications based on unusual metric patterns. By sending metrics such as request counts and response times, teams can set thresholds in New Relic that, when breached, will automatically alert responsible parties. This user-driven approach supports immediate responses to potential issues before they escalate into larger incidents.</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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