Kafka and SigNoz Integration
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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 configuration turns any Telegraf agent into a Remote Write publisher for SigNoz, streaming rich metrics straight into the SigNoz backend with a single URL change.</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>
SigNoz
<p>SigNoz is an open source observability platform that stores metrics, traces, and logs. When you deploy SigNoz, its <strong>signoz-otel-collector-metrics</strong> service exposes a Prometheus Remote Write receiver (default <strong>:13133/api/v1/write</strong>). By configuring Telegraf’s Prometheus plugin to point at this endpoint, you can push any Telegraf collected metrics, SNMP counters, cloud services, or business KPIs—directly into SigNoz. The plugin natively serializes metrics in the Remote Write protobuf format, supports external labels, metadata export, retries, and TLS or bearer-token auth, so it fits zero-trust and multi-tenant SigNoz clusters. Inside SigNoz, the data lands in ClickHouse tables that back Metrics Explorer, alert rules, and unified dashboards. This approach lets organizations unify Prometheus and OTLP pipelines, enables long-term retention powered by ClickHouse compression, and avoids vendor lock-in while retaining PromQL-style queries.</p>
Configuration
Kafka
SigNoz
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>
SigNoz
<ol> <li> <p><strong>Multi-Cluster Federated Monitoring</strong>: Drop a Telegraf DaemonSet into each Kubernetes cluster, tag metrics with <code>cluster=<name></code>, and Remote Write them to a central SigNoz instance. Ops teams get a single PromQL window across prod, staging, and edge clusters without running Thanos sidecars.</p> </li> <li> <p><strong>Factory-Floor Edge Gateway</strong>: A rugged Intel NUC on the shop floor runs Telegraf to scrape Modbus PLCs and environmental sensors. It batches readings every 5 seconds and pushes them over an intermittent 4G link to SigNoz SaaS. ClickHouse compression keeps costs low while AI-based outlier detection in SigNoz flags overheating motors before failure.</p> </li> <li> <p><strong>SaaS Usage Metering</strong>: Telegraf runs alongside each micro-service, exporting per-tenant counters (<code>api_calls</code>, <code>gigabytes_processed</code>). Remote Write streams the data to SigNoz where a scheduled ClickHouse materialized view aggregates usage for monthly billing—no separate metering stack required.</p> </li> <li> <p><strong>Autoscaling Feedback Loop</strong>: Combine Telegraf’s Kubernetes input with the Remote Write output to publish granular pod CPU and queue-length metrics into SigNoz. A custom SigNoz alert fires when P95 latency breaches 200 ms and a GitOps controller reads that alert to trigger a HorizontalPodAutoscaler tweak—closing the loop between observability and automation.</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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