IPVS and Google Cloud Monitoring 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.
See Ways to Get Started
Input and output integration overview
The IPVS plugin is designed to collect metrics related to IPVS virtual and real servers on Linux systems.
The Stackdriver plugin allows users to send metrics directly to a specified project in Google Cloud Monitoring, facilitating robust monitoring capabilities across their cloud resources.
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.
Google Cloud Monitoring
This plugin writes metrics to a project in Google Cloud Monitoring, which used to be known as Stackdriver. Authentication is a prerequisite and can be achieved via service accounts or user credentials. The plugin is designed to group metrics by a namespace
variable and metric key, facilitating organized data management. However, users are encouraged to use the official
naming format for enhanced query efficiency. The plugin supports additional configurations for managing metric representation and allows tags to be treated as resource labels. Notably, it imposes certain restrictions on the data it can accept, such as not allowing string values or points that are out of chronological order.
Configuration
IPVS
[[inputs.ipvs]]
# no configuration
Google Cloud Monitoring
[[outputs.stackdriver]]
## GCP Project
project = "project-id"
## Quota Project
## Specifies the Google Cloud project that should be billed for metric ingestion.
## If omitted, the quota is charged to the service account’s default project.
## This is useful when sending metrics to multiple projects using a single service account.
## The caller must have the `serviceusage.services.use` permission on the specified project.
# quota_project = ""
## The namespace for the metric descriptor
## This is optional and users are encouraged to set the namespace as a
## resource label instead. If omitted it is not included in the metric name.
namespace = "telegraf"
## Metric Type Prefix
## The DNS name used with the metric type as a prefix.
# metric_type_prefix = "custom.googleapis.com"
## Metric Name Format
## Specifies the layout of the metric name, choose from:
## * path: 'metric_type_prefix_namespace_name_key'
## * official: 'metric_type_prefix/namespace_name_key/kind'
# metric_name_format = "path"
## Metric Data Type
## By default, telegraf will use whatever type the metric comes in as.
## However, for some use cases, forcing int64, may be preferred for values:
## * source: use whatever was passed in
## * double: preferred datatype to allow queries by PromQL.
# metric_data_type = "source"
## Tags as resource labels
## Tags defined in this option, when they exist, are added as a resource
## label and not included as a metric label. The values from tags override
## the values defined under the resource_labels config options.
# tags_as_resource_label = []
## Custom resource type
# resource_type = "generic_node"
## Override metric type by metric name
## Metric names matching the values here, globbing supported, will have the
## metric type set to the corresponding type.
# metric_counter = []
# metric_gauge = []
# metric_histogram = []
## NOTE: Due to the way TOML is parsed, tables must be at the END of the
## plugin definition, otherwise additional config options are read as part of
## the table
## Additional resource labels
# [outputs.stackdriver.resource_labels]
# node_id = "$HOSTNAME"
# namespace = "myapp"
# location = "eu-north0"
Input and output integration examples
IPVS
-
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.
-
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.
-
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.
Google Cloud Monitoring
-
Multi-Project Metric Aggregation: Use this plugin to send aggregated metrics from various applications across different projects into a single Google Cloud Monitoring project. This use case helps centralize metrics for teams managing multiple applications, providing a unified view for performance monitoring and enhancing decision-making. By configuring different quota projects for billing, organizations can ensure proper cost management while benefiting from a consolidated monitoring strategy.
-
Anomaly Detection Setup: Integrate the plugin with a machine learning-based analytics tool that identifies anomalies in the collected metrics. Using the historical data provided by the plugin, the tool can learn normal baseline behavior and promptly alert the operations team when unusual patterns arise, enabling proactive troubleshooting and minimizing service disruptions.
-
Dynamic Resource Labeling: Implement dynamic tagging by utilizing the tags_as_resource_label option to adaptively attach resource labels based on runtime conditions. This setup allows metrics to provide context-sensitive information, such as varying environmental parameters or operational states, enhancing the granularity of monitoring and reporting without changing the fundamental metric structure.
-
Custom Metric Visualization Dashboards: Leverage the data collected by the Google Cloud Monitoring output plugin to feed a custom metrics visualization dashboard using a third-party framework. By visualizing metrics in real-time, teams can achieve better situational awareness, notably by correlating different metrics, improving operational decision-making, and streamlining performance management workflows.
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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