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

<p>This output plugin delivers a reliable and efficient mechanism for routing Telegraf collected metrics directly into TimescaleDB. By leveraging PostgreSQL’s robust ecosystem combined with TimescaleDB’s time series optimizations, it supports high-performance data ingestion and advanced querying capabilities.</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>

TimescaleDB

<p>TimescaleDB is an open source time series database built as an extension to PostgreSQL, designed to handle large scale, time-oriented data efficiently. Launched in 2017, TimescaleDB emerged in response to the growing need for a robust, scalable solution that could manage vast volumes of data with high insert rates and complex queries. By leveraging PostgreSQL’s familiar SQL interface and enhancing it with specialized time series capabilities, TimescaleDB quickly gained popularity among developers looking to integrate time series functionality into existing relational databases. Its hybrid approach allows users to benefit from PostgreSQL’s flexibility, reliability, and ecosystem while providing optimized performance for time series data.</p> <p>The database is particularly effective in environments that demand fast ingestion of data points combined with sophisticated analytical queries over historical periods. TimescaleDB has a number of innovative features like hypertables which transparently partition data into manageable chunks and built-in continuous aggregation. These allow for significantly improved query speed and resource efficiency.</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>

TimescaleDB

<pre><code class="language-toml"># Publishes metrics to a TimescaleDB database [[outputs.postgresql]] ## Specify connection address via the standard libpq connection string: ## host=... user=... password=... sslmode=... dbname=... ## Or a URL: ## postgres://[user[:password]]@localhost[/dbname]?sslmode=[disable|verify-ca|verify-full] ## See https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING ## ## All connection parameters are optional. Environment vars are also supported. ## e.g. PGPASSWORD, PGHOST, PGUSER, PGDATABASE ## All supported vars can be found here: ## https://www.postgresql.org/docs/current/libpq-envars.html ## ## Non-standard parameters: ## pool_max_conns (default: 1) - Maximum size of connection pool for parallel (per-batch per-table) inserts. ## pool_min_conns (default: 0) - Minimum size of connection pool. ## pool_max_conn_lifetime (default: 0s) - Maximum connection age before closing. ## pool_max_conn_idle_time (default: 0s) - Maximum idle time of a connection before closing. ## pool_health_check_period (default: 0s) - Duration between health checks on idle connections. # connection = "" ## Postgres schema to use. # schema = "public" ## Store tags as foreign keys in the metrics table. Default is false. # tags_as_foreign_keys = false ## Suffix to append to table name (measurement name) for the foreign tag table. # tag_table_suffix = "_tag" ## Deny inserting metrics if the foreign tag can't be inserted. # foreign_tag_constraint = false ## Store all tags as a JSONB object in a single 'tags' column. # tags_as_jsonb = false ## Store all fields as a JSONB object in a single 'fields' column. # fields_as_jsonb = false ## Name of the timestamp column ## NOTE: Some tools (e.g. Grafana) require the default name so be careful! # timestamp_column_name = "time" ## Type of the timestamp column ## Currently, "timestamp without time zone" and "timestamp with time zone" ## are supported # timestamp_column_type = "timestamp without time zone" ## Templated statements to execute when creating a new table. # create_templates = [ # '''CREATE TABLE {{ .table }} ({{ .columns }})''', # ] ## Templated statements to execute when adding columns to a table. ## Set to an empty list to disable. Points containing tags for which there is ## no column will be skipped. Points containing fields for which there is no ## column will have the field omitted. # add_column_templates = [ # '''ALTER TABLE {{ .table }} ADD COLUMN IF NOT EXISTS {{ .columns|join ", ADD COLUMN IF NOT EXISTS " }}''', # ] ## Templated statements to execute when creating a new tag table. # tag_table_create_templates = [ # '''CREATE TABLE {{ .table }} ({{ .columns }}, PRIMARY KEY (tag_id))''', # ] ## Templated statements to execute when adding columns to a tag table. ## Set to an empty list to disable. Points containing tags for which there is ## no column will be skipped. # tag_table_add_column_templates = [ # '''ALTER TABLE {{ .table }} ADD COLUMN IF NOT EXISTS {{ .columns|join ", ADD COLUMN IF NOT EXISTS " }}''', # ] ## The postgres data type to use for storing unsigned 64-bit integer values ## (Postgres does not have a native unsigned 64-bit integer type). ## The value can be one of: ## numeric - Uses the PostgreSQL "numeric" data type. ## uint8 - Requires pguint extension (https://github.com/petere/pguint) # uint64_type = "numeric" ## When using pool_max_conns &gt; 1, and a temporary error occurs, the query is ## retried with an incremental backoff. This controls the maximum duration. # retry_max_backoff = "15s" ## Approximate number of tag IDs to store in in-memory cache (when using ## tags_as_foreign_keys). This is an optimization to skip inserting known ## tag IDs. Each entry consumes approximately 34 bytes of memory. # tag_cache_size = 100000 ## Cut column names at the given length to not exceed PostgreSQL's ## 'identifier length' limit (default: no limit) ## (see https://www.postgresql.org/docs/current/limits.html) ## Be careful to not create duplicate column names! # column_name_length_limit = 0 ## Enable &amp; set the log level for the Postgres driver. # log_level = "warn" # trace, debug, info, warn, error, none </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>

TimescaleDB

<ol> <li> <p><strong>Real-Time IoT Data Ingestion</strong>: Use the plugin to collect and store sensor data from thousands of IoT devices in real time. This setup facilitates immediate analysis, helping organizations monitor operational efficiency and respond quickly to changing conditions.</p> </li> <li> <p><strong>Cloud Application Performance Monitoring</strong>: Leverage the plugin to feed detailed performance metrics from distributed cloud applications into TimescaleDB. This integration supports real-time dashboards and alerts, enabling teams to swiftly identify and mitigate performance bottlenecks.</p> </li> <li> <p><strong>Historical Data Analysis and Reporting</strong>: Implement a system where long-term metrics are stored in TimescaleDB for comprehensive historical analysis. This approach allows businesses to perform trend analysis, generate detailed reports, and make data-driven decisions based on archived time-series data.</p> </li> <li> <p><strong>Adaptive Alerting and Anomaly Detection</strong>: Integrate the plugin with automated anomaly detection workflows. By continuously streaming metrics to TimescaleDB, machine learning models can analyze data patterns and trigger alerts when anomalies occur, enhancing system reliability and proactive maintenance.</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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