Google Cloud Stackdriver and Datadog Integration

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 Datadog Telegraf Plugin enables the submission of metrics to the Datadog Metrics API, facilitating efficient monitoring and data analysis through a reliable metric ingestion process.

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.

Datadog

This plugin writes to the Datadog Metrics API, enabling users to send metrics for monitoring and performance analysis. By utilizing the Datadog API key, users can configure the plugin to establish a connection with Datadog’s v1 API. The plugin supports various configuration options including connection timeouts, HTTP proxy settings, and data compression methods, ensuring adaptability to different deployment environments. The ability to transform count metrics into rates enhances the integration of Telegraf with Datadog agents, particularly beneficial for applications that rely on real-time performance metrics.

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>

Datadog

[[outputs.datadog]]
  ## Datadog API key
  apikey = "my-secret-key"

  ## Connection timeout.
  # timeout = "5s"

  ## Write URL override; useful for debugging.
  ## This plugin only supports the v1 API currently due to the authentication
  ## method used.
  # url = "https://app.datadoghq.com/api/v1/series"

  ## Set http_proxy
  # use_system_proxy = false
  # http_proxy_url = "http://localhost:8888"

  ## Override the default (none) compression used to send data.
  ## Supports: "zlib", "none"
  # compression = "none"

  ## When non-zero, converts count metrics submitted by inputs.statsd
  ## into rate, while dividing the metric value by this number.
  ## Note that in order for metrics to be submitted simultaenously alongside
  ## a Datadog agent, rate_interval has to match the interval used by the
  ## agent - which defaults to 10s
  # rate_interval = 0s

Input and output integration examples

Google Cloud Stackdriver

1. 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.

2. 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.

3. 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.

4. 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.

Datadog

1. Real-Time Infrastructure Monitoring: Use the Datadog plugin to monitor server metrics in real-time by sending CPU usage and memory statistics directly to Datadog. This integration allows IT teams to visualize and analyze system performance metrics in a centralized dashboard, enabling proactive response to any emerging issues, such as resource bottlenecks or server overloads.

2. Application Performance Tracking: Leverage this plugin to submit application-specific metrics, such as request counts and error rates, to Datadog. By integrating with application monitoring tools, teams can correlate infrastructure metrics with application performance, providing insights that enable them to optimize code performance and improve user experience.

3. Anomaly Detection in Metrics: Configure the Datadog plugin to send metrics that can trigger alerts and notifications based on unusual patterns detected by Datadog’s machine learning features. This proactive monitoring helps teams swiftly react to potential outages or performance degradation before customers are impacted.

4. Integrating with Cloud Services: By utilizing the Datadog plugin to send metrics from cloud resources, IT teams can gain visibility into cloud application performance. Monitoring metrics like latency and error rates helps with ensuring service-level agreements (SLAs) are met and also assists in optimizing resource allocation across cloud environments.

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