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StatsD 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/statsd-influxdb/">StatsD and InfluxDB</a>.

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Infrastructure Monitoring with Telegraf

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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.

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Input and output integration overview

<p>The StatsD input plugin captures metrics from a StatsD server by running a listener service in the background, allowing for comprehensive performance monitoring and metric aggregation.</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

StatsD

<p>The StatsD input plugin is designed to gather metrics from a StatsD server by running a backgrounded StatsD listener service while Telegraf is active. This plugin leverages the format of the StatsD messages as established by the original Etsy implementation, which allows for various types of metrics including gauges, counters, sets, timings, histograms, and distributions. The capabilities of the StatsD plugin extend to parsing tags and extending the standard protocol with features that accommodate InfluxDB’s tagging system. It can handle messages sent via different protocols (UDP or TCP), manage multiple metric metrics effectively, and offers advanced configurations for optimal metric handling such as percentiles calculation and data transformation templates. This flexibility empowers users to track application performance comprehensively, making it an essential tool for robust monitoring setups.</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

StatsD

<pre><code class="language-toml">[[inputs.statsd]] ## Protocol, must be "tcp", "udp4", "udp6" or "udp" (default=udp) protocol = "udp" ## MaxTCPConnection - applicable when protocol is set to tcp (default=250) max_tcp_connections = 250 ## Enable TCP keep alive probes (default=false) tcp_keep_alive = false ## Specifies the keep-alive period for an active network connection. ## Only applies to TCP sockets and will be ignored if tcp_keep_alive is false. ## Defaults to the OS configuration. # tcp_keep_alive_period = "2h" ## Address and port to host UDP listener on service_address = ":8125" ## The following configuration options control when telegraf clears it's cache ## of previous values. If set to false, then telegraf will only clear it's ## cache when the daemon is restarted. ## Reset gauges every interval (default=true) delete_gauges = true ## Reset counters every interval (default=true) delete_counters = true ## Reset sets every interval (default=true) delete_sets = true ## Reset timings &amp; histograms every interval (default=true) delete_timings = true ## Enable aggregation temporality adds temporality=delta or temporality=commulative tag, and ## start_time field, which adds the start time of the metric accumulation. ## You should use this when using OpenTelemetry output. # enable_aggregation_temporality = false ## Percentiles to calculate for timing &amp; histogram stats. percentiles = [50.0, 90.0, 99.0, 99.9, 99.95, 100.0] ## separator to use between elements of a statsd metric metric_separator = "_" ## Parses tags in the datadog statsd format ## http://docs.datadoghq.com/guides/dogstatsd/ ## deprecated in 1.10; use datadog_extensions option instead parse_data_dog_tags = false ## Parses extensions to statsd in the datadog statsd format ## currently supports metrics and datadog tags. ## http://docs.datadoghq.com/guides/dogstatsd/ datadog_extensions = false ## Parses distributions metric as specified in the datadog statsd format ## https://docs.datadoghq.com/developers/metrics/types/?tab=distribution#definition datadog_distributions = false ## Keep or drop the container id as tag. Included as optional field ## in DogStatsD protocol v1.2 if source is running in Kubernetes ## https://docs.datadoghq.com/developers/dogstatsd/datagram_shell/?tab=metrics#dogstatsd-protocol-v12 datadog_keep_container_tag = false ## Statsd data translation templates, more info can be read here: ## https://github.com/influxdata/telegraf/blob/master/docs/TEMPLATE_PATTERN.md # templates = [ # "cpu.* measurement*" # ] ## Number of UDP messages allowed to queue up, once filled, ## the statsd server will start dropping packets allowed_pending_messages = 10000 ## Number of worker threads used to parse the incoming messages. # number_workers_threads = 5 ## Number of timing/histogram values to track per-measurement in the ## calculation of percentiles. Raising this limit increases the accuracy ## of percentiles but also increases the memory usage and cpu time. percentile_limit = 1000 ## Maximum socket buffer size in bytes, once the buffer fills up, metrics ## will start dropping. Defaults to the OS default. # read_buffer_size = 65535 ## Max duration (TTL) for each metric to stay cached/reported without being updated. # max_ttl = "10h" ## Sanitize name method ## By default, telegraf will pass names directly as they are received. ## However, upstream statsd now does sanitization of names which can be ## enabled by using the "upstream" method option. This option will a) replace ## white space with '_', replace '/' with '-', and remove characters not ## matching 'a-zA-Z_\-0-9\.;='. #sanitize_name_method = "" ## Replace dots (.) with underscore (_) and dashes (-) with ## double underscore (__) in metric names. # convert_names = false ## Convert all numeric counters to float ## Enabling this would ensure that both counters and guages are both emitted ## as floats. # float_counters = false </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

StatsD

<ol> <li> <p><strong>Real-time Application Performance Monitoring</strong>: Utilize the StatsD input plugin to monitor application performance metrics in real-time. By configuring your application to send various metrics to a StatsD server, teams can leverage this plugin to analyze performance bottlenecks, track user activity, and ensure resource optimization dynamically. The combination of historical and real-time metrics allows for proactive troubleshooting and enhances the responsiveness of issue resolution processes.</p> </li> <li> <p><strong>Tracking User Engagement Metrics in Web Applications</strong>: Use the StatsD plugin to gather user engagement statistics, such as page views, click events, and interaction times. By sending these metrics to the StatsD server, businesses can derive valuable insights into user behavior, enabling them to make data-driven decisions to improve user experience and interface design based on quantitative feedback. This can significantly enhance the effectiveness of marketing strategies and product development efforts.</p> </li> <li> <p><strong>Infrastructure Health Monitoring</strong>: Deploy the StatsD plugin to monitor the health of your server infrastructure by tracking metrics such as resource utilization, server response times, and network performance. With this setup, DevOps teams can gain detailed visibility into system performance, effectively anticipating issues before they escalate. This enables a proactive approach to infrastructure management, minimizing downtimes and ensuring optimal service delivery.</p> </li> <li> <p><strong>Creating Comprehensive Service Dashboards</strong>: Integrate StatsD with visualization tools to create comprehensive dashboards that reflect the status and health of services across an architecture. For instance, combining data from multiple services logged through StatsD can transform raw metrics into actionable insights, showcasing system performance trends over time. This capability empowers stakeholders to maintain oversight and drive decisions based on visualized data sets, enhancing overall operational transparency.</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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