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Google Cloud Stackdriver and AWS Timestream Integration

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This is not the recommended configuration for real-time query at scale. For query and compression optimization, high-speed ingest, and high availability, you may want to consider <a href="/integrations/stackdriver-influxdb/">Stackdriver and InfluxDB</a>.

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Infrastructure Monitoring with Telegraf

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Input and output integration overview

<p>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.</p>

<p>The AWS Timestream Telegraf plugin enables users to send metrics directly to Amazon’s Timestream service, which is designed for time series data management. This plugin offers a variety of configuration options for authentication, data organization, and retention settings.</p> <p>With the coming End of Life of AWS Timestream for LiveAnalytics, you can easily switch to AWS Timestream for InfluxDB or other verions of InfluxDB hosted on AWS by using the <a href="https://www.influxdata.com/integrations/influxdb/">InfluxDB Telegraf plugin</a>. Learn more about <a href="https://www.influxdata.com/influxdb-cloud-on-aws/">AWS and InfluxDB</a></p>

Integration details

Google Cloud Stackdriver

<p>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.</p>

AWS Timestream

<p>This plugin is designed to efficiently write metrics to Amazon’s Timestream for LiveAnalytics service. With AWS no longer accepting new users for their LiveAnalytics service, consider using the <a href="https://www.influxdata.com/integrations/influxdb/">InfluxDB plugin</a> with AWS Timestream for InfluxDB or other <a href="https://www.influxdata.com/influxdb-cloud-on-aws/">InfluxDB options available on AWS</a>. This plugin Telegraf can send data collected from various sources and supports a flexible configuration for authentication, data organization, and retention management. It utilizes a credential chain for authentication, allowing various methods such as web identity, assumed roles, and shared profiles. Users can define how metrics are organized in Timestream—whether to use a single table or multiple tables, alongside control over aspect such as retention periods for both magnetic and memory stores. A key feature is its ability to handle multi-measure records, enabling efficient data ingestion and helping to reduce the overhead of multiple writes. In terms of error handling, the plugin includes mechanisms for addressing common issues related to AWS errors during data writes, such as retry logic for throttling and the ability to create tables as needed.</p>

Configuration

Google Cloud Stackdriver

<pre><code class="language-toml">[[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 ::= <resource_labels> {AND <metric_labels> AND <user_labels> AND <system_labels>} ## resource_labels ::= <resource_labels> {OR <resource_label>} ## metric_labels ::= <metric_labels> {OR <metric_label>} ## user_labels ::= <user_labels> {OR <user_label>} ## system_labels ::= <system_labels> {OR <system_label>} ## ## 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.<key> = <value> # [[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.<key> = <value> # [[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."<key>" = <value> # [[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."<key>" = <value> # [[inputs.stackdriver.filter.system_labels]] # key = "machine_type" # value = 'starts_with("e2-")' &lt;/code&gt;&lt;/pre&gt; </value></key></value></key></value></key></value></key></system_label></system_labels></user_label></user_labels></metric_label></metric_labels></resource_label></resource_labels></system_labels></user_labels></metric_labels></resource_labels></code></pre>

AWS Timestream

<pre><code class="language-toml">[[outputs.timestream]] ## Amazon Region region = "us-east-1" ## Amazon Credentials ## Credentials are loaded in the following order: ## 1) Web identity provider credentials via STS if role_arn and web_identity_token_file are specified ## 2) Assumed credentials via STS if role_arn is specified ## 3) explicit credentials from 'access_key' and 'secret_key' ## 4) shared profile from 'profile' ## 5) environment variables ## 6) shared credentials file ## 7) EC2 Instance Profile #access_key = "" #secret_key = "" #token = "" #role_arn = "" #web_identity_token_file = "" #role_session_name = "" #profile = "" #shared_credential_file = "" ## Endpoint to make request against, the correct endpoint is automatically ## determined and this option should only be set if you wish to override the ## default. ## ex: endpoint_url = "http://localhost:8000" # endpoint_url = "" ## Timestream database where the metrics will be inserted. ## The database must exist prior to starting Telegraf. database_name = "yourDatabaseNameHere" ## Specifies if the plugin should describe the Timestream database upon starting ## to validate if it has access necessary permissions, connection, etc., as a safety check. ## If the describe operation fails, the plugin will not start ## and therefore the Telegraf agent will not start. describe_database_on_start = false ## Specifies how the data is organized in Timestream. ## Valid values are: single-table, multi-table. ## When mapping_mode is set to single-table, all of the data is stored in a single table. ## When mapping_mode is set to multi-table, the data is organized and stored in multiple tables. ## The default is multi-table. mapping_mode = "multi-table" ## Specifies if the plugin should create the table, if the table does not exist. create_table_if_not_exists = true ## Specifies the Timestream table magnetic store retention period in days. ## Check Timestream documentation for more details. ## NOTE: This property is valid when create_table_if_not_exists = true. create_table_magnetic_store_retention_period_in_days = 365 ## Specifies the Timestream table memory store retention period in hours. ## Check Timestream documentation for more details. ## NOTE: This property is valid when create_table_if_not_exists = true. create_table_memory_store_retention_period_in_hours = 24 ## Specifies how the data is written into Timestream. ## Valid values are: true, false ## When use_multi_measure_records is set to true, all of the tags and fields are stored ## as a single row in a Timestream table. ## When use_multi_measure_record is set to false, Timestream stores each field in a ## separate table row, thereby storing the tags multiple times (once for each field). ## The recommended setting is true. ## The default is false. use_multi_measure_records = "false" ## Specifies the measure_name to use when sending multi-measure records. ## NOTE: This property is valid when use_multi_measure_records=true and mapping_mode=multi-table measure_name_for_multi_measure_records = "telegraf_measure" ## Specifies the name of the table to write data into ## NOTE: This property is valid when mapping_mode=single-table. # single_table_name = "" ## Specifies the name of dimension when all of the data is being stored in a single table ## and the measurement name is transformed into the dimension value ## (see Mapping data from Influx to Timestream for details) ## NOTE: This property is valid when mapping_mode=single-table. # single_table_dimension_name_for_telegraf_measurement_name = "namespace" ## Only valid and optional if create_table_if_not_exists = true ## Specifies the Timestream table tags. ## Check Timestream documentation for more details # create_table_tags = { "foo" = "bar", "environment" = "dev"} ## Specify the maximum number of parallel go routines to ingest/write data ## If not specified, defaulted to 1 go routines max_write_go_routines = 25 ## Please see README.md to know how line protocol data is mapped to Timestream ## </code></pre>

Input and output integration examples

Google Cloud Stackdriver

<ol> <li> <p><strong>Integrating Cloud Metrics into Custom Dashboards</strong>: 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.</p> </li> <li> <p><strong>Automated Alerts and Analysis</strong>: 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.</p> </li> <li> <p><strong>Cross-Platform Resource Comparison</strong>: 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.</p> </li> <li> <p><strong>Historical Data Analysis for Capacity Planning</strong>: 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.</p> </li> </ol>

AWS Timestream

<ol> <li> <p><strong>IoT Data Metrics</strong>: Use the Timestream plugin to send real-time metrics from IoT devices to Timestream, allowing for quick analysis and visualization of sensor data. By organizing device readings into a time series format, users can track trends, identify anomalies, and streamline operational decisions based on device performance.</p> </li> <li> <p><strong>Application Performance Monitoring</strong>: Leverage Timestream alongside application monitoring tools to send metrics about service performance over time. This integration enables engineers to perform historical analysis of application performance, correlate it with business metrics, and optimize resource allocation based on usage patterns viewed over time.</p> </li> <li> <p><strong>Automated Data Archiving</strong>: Configure the Timestream plugin to write data to Timestream while simultaneously managing retention periods. This setup can automate archiving strategies, ensuring that older data is preserved according to predefined criteria. This is especially useful for compliance and historical analysis, allowing businesses to maintain their data lifecycle with minimal manual intervention.</p> </li> <li> <p><strong>Multi-Application Metrics Aggregation</strong>: Utilize the Timestream plugin to aggregate metrics from multiple applications into Timestream. By creating a unified database of performance metrics, organizations can gain holistic insights across various services, improving visibility into system-wide performance and facilitating cross-application troubleshooting.</p> </li> </ol>

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Useful Links

Documentation

Telegraf Quickstart Training

Infrastructure Monitoring with Telegraf

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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