ntpq and Zabbix Integration
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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.
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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 sends metrics to Zabbix via traps, allowing for efficient monitoring of systems and applications. It supports automated configuration and data sending based on dynamic metrics collected by Telegraf.
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.
Zabbix
The Telegraf Zabbix plugin is designed to send metrics to Zabbix, an open-source monitoring solution, using the trap protocol. It supports various versions from 3.0 to 6.0, ensuring compatibility with recent updates. The plugin facilitates easy integration with the Zabbix ecosystem, allowing users to send collected metrics and monitor system performance seamlessly. Key functionalities include the ability to define the address and port of the Zabbix server, options for prefixing keys, determining the type of data sent (active vs. trapper), and features for low-level discovery (LLD) enabling dynamic item creation based on the metrics observed. Configuration options also allow for autoregistration and resending intervals for LLD data, ensuring that the metrics are up-to-date and relevant. Additionally, the trap format used for sending metrics is structured to facilitate efficient data transfer and processing in Zabbix.
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"
Zabbix
[[outputs.zabbix]]
## Address and (optional) port of the Zabbix server
address = "zabbix.example.com:10051"
## Send metrics as type "Zabbix agent (active)"
# agent_active = false
## Add prefix to all keys sent to Zabbix.
# key_prefix = "telegraf."
## Name of the tag that contains the host name. Used to set the host in Zabbix.
## If the tag is not found, use the hostname of the system running Telegraf.
# host_tag = "host"
## Skip measurement prefix to all keys sent to Zabbix.
# skip_measurement_prefix = false
## This field will be sent as HostMetadata to Zabbix Server to autoregister the host.
## To enable this feature, this option must be set to a value other than "".
# autoregister = ""
## Interval to resend auto-registration data to Zabbix.
## Only applies if autoregister feature is enabled.
## This value is a lower limit, the actual resend should be triggered by the next flush interval.
# autoregister_resend_interval = "30m"
## Interval to send LLD data to Zabbix.
## This value is a lower limit, the actual resend should be triggered by the next flush interval.
# lld_send_interval = "10m"
## Interval to delete stored LLD known data and start capturing it again.
## This value is a lower limit, the actual resend should be triggered by the next flush interval.
# lld_clear_interval = "1h"
Input and output integration examples
ntpq
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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.
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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.
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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.
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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.
Zabbix
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Dynamic Monitoring of Containerized Applications: Integration of the Zabbix plugin can be leveraged to monitor Docker containers dynamically. As containers are created and removed, the plugin can automatically update Zabbix with the appropriate metrics, ensuring that monitoring stays current without manual configuration. This enhances visibility into resource usage and performance metrics for microservices orchestrated with Kubernetes or Docker Swarm.
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Real-Time Performance Monitoring with Auto-registration: By enabling the autoregister feature, the plugin can automatically register hosts in Zabbix based on the metrics received. This scenario provides a streamlined approach to add new hosts to monitoring without manual setup, which is particularly useful in environments where hosts may frequently spin up and down, such as serverless architectures or cloud-based deployments.
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Leveraging Low-level Discovery for Flexible Metric Capture: Using low-level discovery, this plugin allows Zabbix to adaptively create items for metrics that are not predefined. In a scenario involving multiple network devices reporting different performance metrics, the plugin can dynamically inform Zabbix about new metrics as they appear, thus ensuring comprehensive monitoring capabilities that evolve with the monitored systems.
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Centralized Monitoring of Distributed Systems: The Zabbix plugin can be utilized in a centralized monitoring setup for distributed systems where multiple Telegraf instances are running across different geographical locations. By sending all metrics to a central Zabbix server, organizations can achieve a holistic view of their infrastructure’s performance and make informed operational decisions.
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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