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Tail 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/tail-influxdb/">Tail and InfluxDB</a>.

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Input and output integration overview

<p>The Tail Telegraf plugin collects metrics by tailing specified log files, capturing new log entries in real-time for further analysis.</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

Tail

<p>The tail plugin is designed to continuously monitor and parse log files, making it ideal for real-time log analysis and monitoring. It mimics the functionality of the Unix <code>tail</code> command, allowing users to specify a file or pattern and begin reading new lines as they are added. Key features include the ability to follow log-rotated files, start reading from the end of a file, and support various parsing formats for the log messages. Users can customize the plugin through various configuration options, such as specifying file encoding, the method for watching file updates, and filter settings for processing log data. This plugin is particularly valuable in environments where log data is critical for monitoring application performance and diagnosing issues.</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

Tail

<pre><code class="language-toml">[[inputs.tail]] ## File names or a pattern to tail. ## These accept standard unix glob matching rules, but with the addition of ## ** as a "super asterisk". ie: ## "/var/log/**.log" -&gt; recursively find all .log files in /var/log ## "/var/log/*/*.log" -&gt; find all .log files with a parent dir in /var/log ## "/var/log/apache.log" -&gt; just tail the apache log file ## "/var/log/log[!1-2]* -&gt; tail files without 1-2 ## "/var/log/log[^1-2]* -&gt; identical behavior as above ## See https://github.com/gobwas/glob for more examples ## files = ["/var/mymetrics.out"] ## Read file from beginning. # from_beginning = false ## Whether file is a named pipe # pipe = false ## Method used to watch for file updates. Can be either "inotify" or "poll". ## inotify is supported on linux, *bsd, and macOS, while Windows requires ## using poll. Poll checks for changes every 250ms. # watch_method = "inotify" ## Maximum lines of the file to process that have not yet be written by the ## output. For best throughput set based on the number of metrics on each ## line and the size of the output's metric_batch_size. # max_undelivered_lines = 1000 ## Character encoding to use when interpreting the file contents. Invalid ## characters are replaced using the unicode replacement character. When set ## to the empty string the data is not decoded to text. ## ex: character_encoding = "utf-8" ## character_encoding = "utf-16le" ## character_encoding = "utf-16be" ## character_encoding = "" # character_encoding = "" ## 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" ## Set the tag that will contain the path of the tailed file. If you don't want this tag, set it to an empty string. # path_tag = "path" ## Filters to apply to files before generating metrics ## "ansi_color" removes ANSI colors # filters = [] ## multiline parser/codec ## https://www.elastic.co/guide/en/logstash/2.4/plugins-filters-multiline.html #[inputs.tail.multiline] ## The pattern should be a regexp which matches what you believe to be an indicator that the field is part of an event consisting of multiple lines of log data. #pattern = "^\s" ## The field's value must be previous or next and indicates the relation to the ## multi-line event. #match_which_line = "previous" ## The invert_match can be true or false (defaults to false). ## If true, a message not matching the pattern will constitute a match of the multiline filter and the what will be applied. (vice-versa is also true) #invert_match = false ## The handling method for quoted text (defaults to 'ignore'). ## The following methods are available: ## ignore -- do not consider quotation (default) ## single-quotes -- consider text quoted by single quotes (') ## double-quotes -- consider text quoted by double quotes (") ## backticks -- consider text quoted by backticks (`) ## When handling quotes, escaped quotes (e.g. \") are handled correctly. #quotation = "ignore" ## The preserve_newline option can be true or false (defaults to false). ## If true, the newline character is preserved for multiline elements, ## this is useful to preserve message-structure e.g. for logging outputs. #preserve_newline = false #After the specified timeout, this plugin sends the multiline event even if no new pattern is found to start a new event. The default is 5s. #timeout = 5s </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

Tail

<ol> <li> <p><strong>Real-Time Server Health Monitoring</strong>: Implement the Tail plugin to parse web server access logs in real-time, providing immediate visibility into user activity, error rates, and performance metrics. By visualizing this log data, operations teams can quickly identify and respond to spikes in traffic or errors, enhancing system reliability and user experience.</p> </li> <li> <p><strong>Centralized Log Management</strong>: Utilize the Tail plugin to aggregate logs from multiple sources across a distributed system. By configuring each service to send its logs to a centralized location via the Tail plugin, teams can simplify log analysis and ensure that all relevant data is accessible from a single interface, streamlining troubleshooting processes.</p> </li> <li> <p><strong>Security Incident Detection</strong>: Use this plugin to monitor authentication logs for unauthorized access attempts or suspicious activity. By setting up alerts on certain log messages, teams can leverage this plugin to enhance security postures and respond promptly to potential security threats, reducing the risk of breaches and increasing overall system integrity.</p> </li> <li> <p><strong>Dynamic Application Performance Insights</strong>: Integrate with analytics tools to create real-time dashboards that display application performance metrics based on log data. This setup not only helps developers diagnose bottlenecks and inefficiencies but also allows for proactive performance tuning and resource allocation, optimizing application behavior under varying loads.</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

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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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