Kernel and Apache Druid Integration

Input and output integration overview

The Kernel plugin collects various statistics about the Linux kernel, including context switches, page usage, and entropy availability.

This plugin allows Telegraf to send JSON-formatted metrics to Apache Druid over HTTP, enabling real-time ingestion for analytical queries on high-volume time-series data.

Integration details

Kernel

The Kernel plugin is designed exclusively for Linux systems and gathers essential kernel statistics that are not covered by other plugins. It primarily focuses on the metrics available in /proc/stat, as well as the entropy available from /proc/sys/kernel/random/entropy_avail. Additional functionalities include the capture of Kernel Samepage Merging (KSM) data and Pressure Stall Information (PSI), requiring Linux kernel version 4.20 or later. This plugin provides a comprehensive look into system behaviors, enabling better understanding and optimization of resource management and usage. The metrics it collects are critical for monitoring system health and performance.

Apache Druid

This configuration uses Telegraf’s HTTP output plugin with json data format to send metrics directly to Apache Druid, a real-time analytics database designed for fast, ad hoc queries on high-ingest time-series data. Druid supports ingestion via HTTP POST to various components like the Tranquility service or native ingestion endpoints. The JSON format is ideal for structuring Telegraf metrics into event-style records for Druid’s columnar and time-partitioned storage engine. Druid excels at powering interactive dashboards and exploratory queries across massive datasets, making it an excellent choice for real-time observability and monitoring analytics when integrated with Telegraf.

Configuration

Kernel

[[inputs.kernel]]
  ## Additional gather options
  ## Possible options include:
  ## * ksm - kernel same-page merging
  ## * psi - pressure stall information
  # collect = []

Apache Druid

[[outputs.http]]
  ## Druid ingestion endpoint (e.g., Tranquility, HTTP Ingest, or Kafka REST Proxy)
  url = "http://druid-ingest.example.com/v1/post"

  ## Use POST method to send events
  method = "POST"

  ## Data format for Druid ingestion (expects JSON format)
  data_format = "json"

  ## Optional headers (may vary depending on Druid setup)
  # [outputs.http.headers]
  #   Content-Type = "application/json"
  #   Authorization = "Bearer YOUR_API_TOKEN"

  ## Optional timeout and TLS settings
  timeout = "10s"
  # tls_ca = "/path/to/ca.pem"
  # tls_cert = "/path/to/cert.pem"
  # tls_key = "/path/to/key.pem"
  # insecure_skip_verify = false

Input and output integration examples

Kernel

1. Memory Optimization through KSM: Utilize the KSM capabilities of this plugin to monitor memory usage patterns in your applications and dynamically adjust the memory allocation strategy based on shared page usage metrics. By analyzing the data collected, you can identify opportunities for consolidating memory and optimizing performance without manual intervention.

2. Real-time System Health Monitoring: Integrate the metrics collected by the Kernel plugin into a real-time dashboard that visualizes key kernel statistics including context switches, interrupts, and entropy availability. This setup allows system administrators to proactively respond to performance issues before they escalate into critical failures, ensuring smooth operation of Linux servers.

3. Enhanced Anomaly Detection: Combine the data from this plugin with machine learning models to predict and detect anomalies in kernel behavior. By continuously monitoring metrics like process forking rates and entropy levels, you can implement an adaptive alerting system that triggers on performance anomalies, allowing for quick responses to potential issues.

4. Resource Usage Patterns Analysis: Use the Pressure Stall Information collected by the plugin to analyze resource usage patterns over time and identify potential bottlenecks under load conditions. By adjusting application performance based on the PSI metrics, you can improve overall resource management and maintain service reliability under varying workloads.

Apache Druid

1. Real-Time Application Monitoring Dashboard: Use Telegraf to collect metrics from application servers and send them to Druid for immediate analysis and visualization in dashboards. Druid’s low-latency querying allows users to interactively explore system behavior in near real-time.

2. Security Event Aggregation: Aggregate and forward security-related metrics such as failed logins, port scans, or process anomalies to Druid. Analysts can build dashboards to monitor threat patterns and investigate incidents with millisecond-level granularity.

3. IoT Device Analytics: Collect telemetry from edge devices via Telegraf and send it to Druid for fast, scalable processing. Druid’s time-partitioned storage and roll-up capabilities are ideal for handling billions of small JSON events from sensors or gateways.

4. Web Traffic Behavior Exploration: Use Telegraf to capture web server metrics (e.g., requests per second, latency, error rates) and forward them to Druid. This enables teams to drill down into user behavior by region, device, or request type with subsecond query performance.

Feedback

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