ntpq and Cortex Integration

Input and output integration overview

The ntpq plugin collects standard metrics related to the Network Time Protocol (NTP) by executing the ntpq command. It gathers essential information about the synchronization state of the local machine with remote NTP servers, providing valuable insights into timekeeping accuracy and network performance.

This plugin enables Telegraf to send metrics to Cortex using the Prometheus remote write protocol, allowing seamless ingestion into Cortex’s scalable, multi-tenant time series storage.

Integration details

ntpq

The ntpq Telegraf plugin provides a way to gather metrics from the Network Time Protocol (NTP) by querying the NTP server using the ntpq executable. This plugin collects a variety of metrics related to the synchronization status with remote NTP servers, including delay, jitter, offset, polling frequency, and reachability. These metrics are crucial for understanding the performance and reliability of time synchronization efforts in systems that rely on accurate timekeeping. NTP plays a vital role in networked environments, enabling synchronized clocks across devices which is essential for logging, coordination of activities, and security protocols. Through this plugin, users can monitor the effectiveness of their time synchronization processes, making it easier to identify issues related to network delays or misconfigurations, thus ensuring that systems remain in sync and operate efficiently.

Cortex

With Telegraf’s HTTP output plugin and the prometheusremotewrite data format you can send metrics directly to Cortex, a horizontally scalable, long-term storage backend for Prometheus. Cortex supports multi-tenancy and accepts remote write requests using the Prometheus protobuf format. By using Telegraf as the collection agent and Remote Write as the transport mechanism, organizations can extend observability into sources not natively supported by Prometheus—such as Windows hosts, SNMP-enabled devices, or custom application metrics—while leveraging Cortex’s high-availability and long-retention capabilities.

Configuration

ntpq

[[inputs.ntpq]]
  ## Servers to query with ntpq.
  ## If no server is given, the local machine is queried.
  # servers = []

  ## If false, set the -n ntpq flag. Can reduce metric gather time.
  ## DEPRECATED since 1.24.0: add '-n' to 'options' instead to skip DNS lookup
  # dns_lookup = true

  ## Options to pass to the ntpq command.
  # options = "-p"

  ## Output format for the 'reach' field.
  ## Available values are
  ##   octal   --  output as is in octal representation e.g. 377 (default)
  ##   decimal --  convert value to decimal representation e.g. 371 -> 249
  ##   count   --  count the number of bits in the value. This represents
  ##               the number of successful reaches, e.g. 37 -> 5
  ##   ratio   --  output the ratio of successful attempts e.g. 37 -> 5/8 = 0.625
  # reach_format = "octal"

Cortex

[[outputs.http]]
  ## Cortex Remote Write endpoint
  url = "http://cortex.example.com/api/v1/push"

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

  ## Send metrics using Prometheus remote write format
  data_format = "prometheusremotewrite"

  ## Optional HTTP headers for authentication
  # [outputs.http.headers]
  #   X-Scope-OrgID = "your-tenant-id"
  #   Authorization = "Bearer YOUR_API_TOKEN"

  ## Optional TLS configuration
  # tls_ca = "/path/to/ca.pem"
  # tls_cert = "/path/to/cert.pem"
  # tls_key = "/path/to/key.pem"
  # insecure_skip_verify = false

  ## Request timeout
  timeout = "10s"

Input and output integration examples

ntpq

1. Network Time Monitoring Dashboard: Utilize the ntpq plugin to create a centralized monitoring dashboard for tracking the reliability and performance of network time synchronization across multiple servers. By visualizing metrics such as delay and jitter, system administrators can quickly identify which servers are providing accurate time versus those with significant latency issues, ensuring that all systems remain synchronized effectively.

2. Automated Alert System for Time Drift: Implement an automated alert system that leverages ntpq metrics to notify operations teams when time drift exceeds acceptable thresholds. By analyzing the offset and jitter values, the system can trigger alerts if any remote NTP server is out of sync, allowing for swift remediation actions to maintain time accuracy across critical infrastructure.

3. Comparative Analysis of Time Sources: Use the ntpq plugin to perform a comparative analysis of different NTP servers over time. By querying multiple NTP sources and monitoring their metrics, organizations can evaluate the performance and reliability of their time sources, making informed decisions about which NTP servers to configure as primary or secondary in their environments.

4. Historical Performance Tracking for NTP: Gather historical performance data on various NTP servers using the ntpq plugin, enabling long-term trend analysis for timekeeping accuracy. This can help organizations identify patterns or recurring issues related to specific servers, informing future decisions about infrastructure changes or adjustments related to time synchronization strategies.

Cortex

1. Unified Multi-Tenant Monitoring: Use Telegraf to collect metrics from different teams or environments and push them to Cortex with separate X-Scope-OrgID headers. This enables isolated data ingestion and querying per tenant, ideal for managed services and platform teams.

2. Extending Prometheus Coverage to Edge Devices: Deploy Telegraf on edge or IoT devices to collect system metrics and send them to a centralized Cortex cluster. This approach ensures consistent observability even for environments without local Prometheus scrapers.

3. Global Service Observability with Federated Tenants: Aggregate metrics from global infrastructure by configuring Telegraf agents to push data into regional Cortex clusters, each tagged with tenant identifiers. Cortex handles deduplication and centralized access across regions.

4. Custom App Telemetry Pipeline: Collect app-specific telemetry via Telegraf’s exec or http input plugins and forward it to Cortex. This allows DevOps teams to monitor app-specific KPIs in a scalable, query-efficient format while keeping metrics logically grouped by tenant or service.

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