Amazon CloudWatch 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
This plugin will pull Metric Statistics from Amazon CloudWatch, streamlining the process of monitoring and analyzing AWS resources.
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
Amazon CloudWatch
The Amazon CloudWatch Plugin allows users to pull detailed metric statistics from Amazon’s CloudWatch service. As a monitoring solution, CloudWatch enables users to track various metrics related to AWS resources and applications, facilitating improved operational and performance insights. The plugin uses a structured authentication method that prioritizes security and flexibility through a combination of STS (Security Token Service), shared credentials, environment variables, and EC2 instance profiles, ensuring robust access control to AWS resources. Key features include the ability to define specific metric namespaces, aggregated periods for metrics, and optional inclusion of linked accounts for cross-account monitoring. A significant aspect of this plugin is its capacity to handle both sparse and dense metric formats, allowing for varied output structures depending on user preference. Thus, it supports versatile use cases in cloud monitoring and analytics by providing comprehensive, timely data directly from CloudWatch.
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
Amazon CloudWatch
[[inputs.cloudwatch]]
region = "us-east-1"
# access_key = ""
# secret_key = ""
# token = ""
# role_arn = ""
# web_identity_token_file = ""
# role_session_name = ""
# profile = ""
# shared_credential_file = ""
# include_linked_accounts = false
# endpoint_url = ""
# use_system_proxy = false
# http_proxy_url = "http://localhost:8888"
period = "5m"
delay = "5m"
interval = "5m"
#recently_active = "PT3H"
# cache_ttl = "1h"
namespaces = ["AWS/ELB"]
# metric_format = "sparse"
# ratelimit = 25
# timeout = "5s"
# batch_size = 500
# statistic_include = ["average", "sum", "minimum", "maximum", sample_count]
# statistic_exclude = []
# [[inputs.cloudwatch.metrics]]
# names = ["Latency", "RequestCount"]
# [[inputs.cloudwatch.metrics.dimensions]]
# name = "LoadBalancerName"
# value = "p-example"
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
Amazon CloudWatch
-
Cross-Account Monitoring: Utilize this plugin to monitor resources across multiple AWS accounts by enabling the
include_linked_accounts
option. This scenario allows companies managing multiple AWS accounts to aggregate metrics into a central monitoring dashboard, providing a unified view of all metrics while ensuring secure data access and compliance through proper role management. -
Dynamic Alerting System: Integrate this plugin with alerting tools to create an automated system that triggers alerts based on defined thresholds for CloudWatch metrics. For instance, if latency metrics exceed specified limits, alerts can be sent to relevant teams, enabling proactive responses to performance issues and reducing downtime.
-
Cost Management Dashboard: Use the metrics gathered from the plugin to build a cost management dashboard that visualizes AWS service usage metrics over time. By correlating these metrics with billing data, organizations can identify high-cost services and take informed actions to optimize their resource usage and spending.
-
Performance Benchmarking for Applications: Leverage the metrics collected from applications running on AWS to perform performance benchmarks. For example, by tracking latency and request count metrics for an ELB, developers can assess the impact of application changes on its performance, making data-driven decisions for optimization.
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
Thank you for being part of our community! If you have any general feedback or found any bugs on these pages, we welcome and encourage your input. Please submit your feedback in the InfluxDB community Slack.
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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