IPVS and Mimir Integration

Input and output integration overview

The IPVS plugin is designed to collect metrics related to IPVS virtual and real servers on Linux systems.

This plugin sends Telegraf metrics directly to Grafana’s Mimir database using HTTP, providing scalable and efficient long-term storage and analysis for Prometheus-compatible metrics.

Integration details

IPVS

The IPVS plugin gathers metrics about IPVS virtual and real servers using the Linux kernel netlink socket interface. As a component specifically designed for Linux, it tracks performance related to IP virtual servers, allowing users to monitor various attributes such as active connections, packet statistics, and byte counts. Key metrics include those for both virtual and real servers, facilitating a comprehensive view of server performance. The plugin also requires the Telegraf process to run with appropriate permissions, typically as root or a user with specific capabilities for proper operation.

Mimir

Grafana Mimir supports the Prometheus Remote Write protocol, enabling Telegraf collected metrics to be efficiently ingested into Mimir clusters for large-scale, long-term storage. This integration leverages Prometheus’s well-established standards, allowing users to combine Telegraf’s extensive data collection capabilities with Mimir’s advanced features, such as query federation, multi-tenancy, high availability, and cost-efficient storage. Grafana Mimir’s architecture is optimized for handling high volumes of metric data and delivering fast query responses, making it ideal for complex monitoring environments and distributed systems.

Configuration

IPVS

[[inputs.ipvs]]
  # no configuration

Mimir

[[outputs.http]]
  url = "http://data-load-balancer-backend-1:9009/api/v1/push"
  data_format = "prometheusremotewrite"
  username = "*****"
  password = "******"
  [outputs.http.headers]
     Content-Type = "application/x-protobuf"
     Content-Encoding = "snappy"
     X-Scope-OrgID = "****"

Input and output integration examples

IPVS

1. Load Balancing Performance Monitoring: Use the IPVS plugin to monitor the performance of a load balancing setup in a Linux environment where IPVS is implemented. By collecting metrics such as byte counts, packet rates, and active connections, administrators can gain real-time insights into server performance, allowing for proactive adjustments to load distribution strategies and ensuring that no individual server becomes a bottleneck.

2. Automated Alerting for Connection Thresholds: Integrate the metrics collected by the IPVS plugin with an alerting system to automatically notify administrators when active connections exceed or fall below specified thresholds. This use case enables dynamic scaling of backend resources, optimizing application performance and resource utilization, while minimizing the risk of sudden service disruptions.

3. Historical Performance Trend Analysis: Store the metrics gathered by the IPVS plugin in a time-series database for historical analysis. By analyzing trends over time, organizations can identify patterns in server performance, correlate them with application usage spikes, and make informed decisions regarding infrastructure upgrades or maintenance schedules to better handle peak loads.

Mimir

1. Enterprise-Scale Kubernetes Monitoring: Integrate Telegraf with Grafana Mimir to stream metrics from Kubernetes clusters at enterprise scale. This enables comprehensive visibility, improved resource allocation, and proactive troubleshooting across hundreds of clusters, leveraging Mimir’s horizontal scalability and high availability.

2. Multi-tenant SaaS Application Observability: Use this plugin to centralize metrics from diverse SaaS tenants into Grafana Mimir, enabling tenant isolation and accurate billing based on resource usage. This approach provides reliable observability, efficient cost management, and secure multi-tenancy support.

3. Global Edge Network Performance Tracking: Stream latency and availability metrics from globally distributed edge servers into Grafana Mimir. Organizations can quickly identify performance degradation or outages, leveraging Mimir’s fast querying capabilities to ensure optimal service reliability and user experience.

4. Real-Time Analytics for High-Volume Microservices: Implement Telegraf metrics collection in high-volume microservices architectures, feeding data into Grafana Mimir for real-time analytics and anomaly detection. Mimir’s powerful querying enables teams to detect anomalies and quickly respond, maintaining high service availability and performance.

Feedback

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