Google Cloud Stackdriver and Google BigQuery 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
This plugin enables the collection of monitoring data from Google Cloud services through the Stackdriver Monitoring API. It is designed to help users monitor their cloud infrastructure’s performance and health by gathering relevant metrics.
The Google BigQuery plugin allows Telegraf to write metrics to Google Cloud BigQuery, enabling robust data analytics capabilities for telemetry data.
Integration details
Google Cloud Stackdriver
The Stackdriver Telegraf plugin allows users to query timeseries data from Google Cloud Monitoring using the Cloud Monitoring API v3. With this plugin, users can easily integrate Google Cloud monitoring metrics into their monitoring stacks. This API provides a wealth of insights about resources and applications running in Google Cloud, including performance, uptime, and operational metrics. The plugin supports various configuration options to filter and refine the data retrieved, enabling users to customize their monitoring setup according to their specific needs. This integration facilitates a smoother experience in maintaining the health and performance of cloud resources and assists teams in making data-driven decisions based on historical and current performance statistics.
Google BigQuery
The Google BigQuery plugin for Telegraf enables seamless integration with Google Cloud’s BigQuery service, a popular data warehousing and analytics platform. This plugin facilitates the transfer of metrics collected by Telegraf into BigQuery datasets, making it easier for users to perform analyses and generate insights from their telemetry data. It requires authentication through a service account or user credentials and is designed to handle various data types, ensuring that users can maintain the integrity and accuracy of their metrics as they are stored in BigQuery tables. The configuration options allow for customization around dataset specifications and handling metrics, including the management of hyphens in metric names, which are not supported by BigQuery for streaming inserts. This plugin is particularly useful for organizations leveraging the scalability and powerful query capabilities of BigQuery to analyze large volumes of monitoring data.
Configuration
Google Cloud Stackdriver
[[inputs.stackdriver]]
## GCP Project
project = "erudite-bloom-151019"
## Include timeseries that start with the given metric type.
metric_type_prefix_include = [
"compute.googleapis.com/",
]
## Exclude timeseries that start with the given metric type.
# metric_type_prefix_exclude = []
## Most metrics are updated no more than once per minute; it is recommended
## to override the agent level interval with a value of 1m or greater.
interval = "1m"
## Maximum number of API calls to make per second. The quota for accounts
## varies, it can be viewed on the API dashboard:
## https://cloud.google.com/monitoring/quotas#quotas_and_limits
# rate_limit = 14
## The delay and window options control the number of points selected on
## each gather. When set, metrics are gathered between:
## start: now() - delay - window
## end: now() - delay
#
## Collection delay; if set too low metrics may not yet be available.
# delay = "5m"
#
## If unset, the window will start at 1m and be updated dynamically to span
## the time between calls (approximately the length of the plugin interval).
# window = "1m"
## TTL for cached list of metric types. This is the maximum amount of time
## it may take to discover new metrics.
# cache_ttl = "1h"
## If true, raw bucket counts are collected for distribution value types.
## For a more lightweight collection, you may wish to disable and use
## distribution_aggregation_aligners instead.
# gather_raw_distribution_buckets = true
## Aggregate functions to be used for metrics whose value type is
## distribution. These aggregate values are recorded in in addition to raw
## bucket counts; if they are enabled.
##
## For a list of aligner strings see:
## https://cloud.google.com/monitoring/api/ref_v3/rpc/google.monitoring.v3#aligner
# distribution_aggregation_aligners = [
# "ALIGN_PERCENTILE_99",
# "ALIGN_PERCENTILE_95",
# "ALIGN_PERCENTILE_50",
# ]
## Filters can be added to reduce the number of time series matched. All
## functions are supported: starts_with, ends_with, has_substring, and
## one_of. Only the '=' operator is supported.
##
## The logical operators when combining filters are defined statically using
## the following values:
## filter ::= {AND AND AND }
## resource_labels ::= {OR }
## metric_labels ::= {OR }
## user_labels ::= {OR }
## system_labels ::= {OR }
##
## For more details, see https://cloud.google.com/monitoring/api/v3/filters
#
## Resource labels refine the time series selection with the following expression:
## resource.labels. =
# [[inputs.stackdriver.filter.resource_labels]]
# key = "instance_name"
# value = 'starts_with("localhost")'
#
## Metric labels refine the time series selection with the following expression:
## metric.labels. =
# [[inputs.stackdriver.filter.metric_labels]]
# key = "device_name"
# value = 'one_of("sda", "sdb")'
#
## User labels refine the time series selection with the following expression:
## metadata.user_labels."" =
# [[inputs.stackdriver.filter.user_labels]]
# key = "environment"
# value = 'one_of("prod", "staging")'
#
## System labels refine the time series selection with the following expression:
## metadata.system_labels."" =
# [[inputs.stackdriver.filter.system_labels]]
# key = "machine_type"
# value = 'starts_with("e2-")'
</code></pre>
Google BigQuery
# Configuration for Google Cloud BigQuery to send entries
[[outputs.bigquery]]
## Credentials File
credentials_file = "/path/to/service/account/key.json"
## Google Cloud Platform Project
# project = ""
## The namespace for the metric descriptor
dataset = "telegraf"
## Timeout for BigQuery operations.
# timeout = "5s"
## Character to replace hyphens on Metric name
# replace_hyphen_to = "_"
## Write all metrics in a single compact table
# compact_table = ""
Input and output integration examples
Google Cloud Stackdriver
-
Integrating Cloud Metrics into Custom Dashboards: With this plugin, teams can funnel metrics from Google Cloud into personalized dashboards, allowing for real-time monitoring of application performance and resource utilization. By customizing the visual representation of cloud metrics, operations teams can easily identify trends and anomalies, enabling proactive management before issues escalate.
-
Automated Alerts and Analysis: Users can set up automated alerting mechanisms leveraging the plugin’s metrics to track resource thresholds. This capability allows teams to act swiftly in response to performance degradation or outages by providing immediate notifications, thus reducing the mean time to recovery and ensuring continued operational efficiency.
-
Cross-Platform Resource Comparison: The plugin can be used to draw metrics from various Google Cloud services and compare them with on-premise resources. This cross-platform visibility helps organizations make informed decisions about resource allocation and scaling strategies, as well as optimize cloud spending versus on-premise infrastructure.
-
Historical Data Analysis for Capacity Planning: By collecting historical metrics over time, the plugin empowers teams to conduct thorough capacity planning. Understanding past performance trends facilitates accurate forecasting for resource needs, leading to better budgeting and investment strategies.
Google BigQuery
-
Real-Time Analytics Dashboard: Leverage the Google BigQuery plugin to feed live metrics into a custom analytics dashboard hosted on Google Cloud. This setup would allow teams to visualize performance data in real-time, providing insights into system health and usage patterns. By using BigQuery’s querying capabilities, users can easily create tailored reports and dashboards to meet their specific needs, thus enhancing decision-making processes.
-
Cost Management and Optimization Analysis: Utilize the plugin to automatically send cost-related metrics from various services into BigQuery. Analyzing this data can help businesses identify unnecessary expenses and optimize resource usage. By performing aggregation and transformation queries in BigQuery, organizations can create accurate forecasts and manage their cloud spending efficiently.
-
Cross-Team Collaboration on Monitoring Data: Enable different teams within an organization to share their monitoring data using BigQuery. With the help of this Telegraf plugin, teams can push their metrics to a central BigQuery instance, fostering collaboration. This data-sharing approach encourages best practices and cross-functional awareness, leading to collective improvements in system performance and reliability.
-
Historical Analysis for Capacity Planning: By using the BigQuery plugin, companies can collect and store historical metrics data essential for capacity planning. Analyzing trends over time can help anticipate system needs and scale infrastructure proactively. Organizations can create time-series analyses and identify patterns that inform their long-term strategic 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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