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NATS 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/nats_consumer-influxdb/">NATS and InfluxDB</a>.

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Input and output integration overview

<p>The NATS Consumer Input Plugin enables real-time data consumption from NATS messaging subjects, integrating seamlessly into the Telegraf data pipeline for monitoring and metrics gathering.</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

NATS

<p>The NATS Consumer Plugin allows Telegraf to read metrics from specified NATS subjects and create metrics based on supported input data formats. Utilizing a Queue Group allows multiple instances of Telegraf to read from a NATS cluster in parallel, enhancing throughput and reliability. This plugin also supports various authentication methods, including username/password, NATS credentials files, and nkey seed files, ensuring secure communication with the NATS servers. It is particularly useful in environments where data persistence and message reliability are critical, thanks to features such as JetStream that facilitate the consumption of historical messages. Additionally, the ability to configure various operational parameters makes this plugin suitable for high-throughput scenarios while maintaining performance integrity.</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

NATS

<pre><code class="language-toml">[[inputs.nats_consumer]] ## urls of NATS servers servers = ["nats://localhost:4222"] ## subject(s) to consume ## If you use jetstream you need to set the subjects ## in jetstream_subjects subjects = ["telegraf"] ## jetstream subjects ## jetstream is a streaming technology inside of nats. ## With jetstream the nats-server persists messages and ## a consumer can consume historical messages. This is ## useful when telegraf needs to restart it don't miss a ## message. You need to configure the nats-server. ## https://docs.nats.io/nats-concepts/jetstream. jetstream_subjects = ["js_telegraf"] ## name a queue group queue_group = "telegraf_consumers" ## Optional authentication with username and password credentials # username = "" # password = "" ## Optional authentication with NATS credentials file (NATS 2.0) # credentials = "/etc/telegraf/nats.creds" ## Optional authentication with nkey seed file (NATS 2.0) # nkey_seed = "/etc/telegraf/seed.txt" ## Use Transport Layer Security # secure = false ## Optional TLS Config # tls_ca = "/etc/telegraf/ca.pem" # tls_cert = "/etc/telegraf/cert.pem" # tls_key = "/etc/telegraf/key.pem" ## Use TLS but skip chain &amp; host verification # insecure_skip_verify = false ## Sets the limits for pending msgs and bytes for each subscription ## These shouldn't need to be adjusted except in very high throughput scenarios # pending_message_limit = 65536 # pending_bytes_limit = 67108864 ## Max undelivered messages ## This plugin uses tracking metrics, which ensure messages are read to ## outputs before acknowledging them to the original broker to ensure data ## is not lost. This option sets the maximum messages to read from the ## broker that have not been written by an output. ## ## This value needs to be picked with awareness of the agent's ## metric_batch_size value as well. Setting max undelivered messages too high ## can result in a constant stream of data batches to the output. While ## setting it too low may never flush the broker's messages. # max_undelivered_messages = 1000 ## Data format to consume. ## Each data format has its own unique set of configuration options, read ## more about them here: ## https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md data_format = "influx" </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

NATS

<ol> <li> <p><strong>Real-Time Analytics Dashboard</strong>: Utilize the NATS plugin to gather metrics from various NATS subjects in real time and feed them into a centralized analytics dashboard. This setup allows for immediate visibility into live application performance, enabling teams to react swiftly to operational issues or performance degradation.</p> </li> <li> <p><strong>Distributed System Monitoring</strong>: Deploy multiple instances of Telegraf configured with the NATS plugin across a distributed architecture. This approach allows teams to aggregate metrics from various microservices efficiently, providing a holistic view of system health and performance while ensuring no messages are dropped during transmission.</p> </li> <li> <p><strong>Historical Message Recovery</strong>: Leverage the capabilities of NATS JetStream along with this plugin to recover and process historical messages after Telegraf has been restarted. This feature is particularly beneficial for applications that require high reliability, ensuring that no critical metrics are lost even in case of service disruptions.</p> </li> <li> <p><strong>Dynamic Load Balancing</strong>: Implement a dynamic load balancing scenario where Telegraf instances consume messages from a NATS cluster based on load. Adjust the queue group settings to control the number of active consumers, allowing for better resource utilization and performance scaling as demand fluctuations occur.</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>

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.

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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