All Integrations / Tail and MariaDB Integration

Tail and MariaDB Integration

Powerful performance with an easy integration, powered by Telegraf, the open source data connector built by InfluxData.

Get This Integration for Free
info

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>.

5B+

Telegraf downloads

#1

Time series database
Source: DB Engines

1B+

Downloads of InfluxDB

2,800+

Contributors

Table of Contents

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

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 plugin writes metrics from Telegraf directly into MariaDB using parameterized SQL INSERT statements, offering a flexible way to store metrics in structured, relational tables.</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>

MariaDB

<p>The SQL output plugin in Telegraf enables direct writing of metrics into SQL-compatible databases like MariaDB by executing parameterized SQL statements. With support for the MySQL driver, the plugin seamlessly integrates with MariaDB for reliable, structured metric storage. This setup is ideal for users who prefer SQL-based analytics or want to store metrics alongside business data for unified querying. MariaDB is a community-developed, enterprise-grade fork of MySQL that emphasizes performance, security, and openness. The plugin supports inserting time series metrics into custom schemas, enabling flexible analytics and integrations with BI tools like Metabase or Grafana using SQL connectors.</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>

MariaDB

<pre><code class="language-toml">[[outputs.sql]] ## Database driver ## Valid options: mssql (Microsoft SQL Server), mysql (MySQL), pgx (Postgres), ## sqlite (SQLite3), snowflake (snowflake.com) clickhouse (ClickHouse) driver = "mysql" ## Data source name ## The format of the data source name is different for each database driver. ## See the plugin readme for details. data_source_name = "username:password@tcp(host:port)/dbname" ## Timestamp column name timestamp_column = "timestamp" ## Table creation template ## Available template variables: ## {TABLE} - table name as a quoted identifier ## {TABLELITERAL} - table name as a quoted string literal ## {COLUMNS} - column definitions (list of quoted identifiers and types) table_template = "CREATE TABLE {TABLE}({COLUMNS})" ## SQL INSERT statement with placeholders. Telegraf will substitute values at runtime. ## table_template = "INSERT INTO metrics (timestamp, name, value, tags) VALUES (?, ?, ?, ?)" ## Table existence check template ## Available template variables: ## {TABLE} - tablename as a quoted identifier table_exists_template = "SELECT 1 FROM {TABLE} LIMIT 1" ## Initialization SQL init_sql = "SET sql_mode='ANSI_QUOTES';" ## Maximum amount of time a connection may be idle. "0s" means connections are ## never closed due to idle time. connection_max_idle_time = "0s" ## Maximum amount of time a connection may be reused. "0s" means connections ## are never closed due to age. connection_max_lifetime = "0s" ## Maximum number of connections in the idle connection pool. 0 means unlimited. connection_max_idle = 2 ## Maximum number of open connections to the database. 0 means unlimited. connection_max_open = 0 ## NOTE: Due to the way TOML is parsed, tables must be at the END of the ## plugin definition, otherwise additional config options are read as part of the ## table ## Metric type to SQL type conversion ## The values on the left are the data types Telegraf has and the values on ## the right are the data types Telegraf will use when sending to a database. ## ## The database values used must be data types the destination database ## understands. It is up to the user to ensure that the selected data type is ## available in the database they are using. Refer to your database ## documentation for what data types are available and supported. #[outputs.sql.convert] # integer = "INT" # real = "DOUBLE" # text = "TEXT" # timestamp = "TIMESTAMP" # defaultvalue = "TEXT" # unsigned = "UNSIGNED" # bool = "BOOL" # ## This setting controls the behavior of the unsigned value. By default the # ## setting will take the integer value and append the unsigned value to it. The other # ## option is "literal", which will use the actual value the user provides to # ## the unsigned option. This is useful for a database like ClickHouse where # ## the unsigned value should use a value like "uint64". # # conversion_style = "unsigned_suffix" </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>

MariaDB

<ol> <li> <p><strong>Business Intelligence Integration</strong>: Store application performance metrics directly into MariaDB and connect it to BI tools like Metabase or Apache Superset. This setup allows blending of operational data with business KPIs for unified dashboards, enhancing visibility across departments.</p> </li> <li> <p><strong>Compliance Reporting with Historical Metrics</strong>: Use this plugin to log metrics into MariaDB for audit and compliance use cases. The relational model enables precise querying of past performance indicators with timestamped entries, supporting regulatory documentation.</p> </li> <li> <p><strong>Custom Alerting Based on SQL Logic</strong>: Insert metrics into MariaDB and use custom SQL queries to define alert thresholds or conditions. Combined with cron jobs or scheduled scripts, this enables advanced alerting workflows not possible with traditional metric platforms.</p> </li> <li> <p><strong>IoT Sensor Metrics Storage</strong>: Collect sensor data from IoT devices via Telegraf and store it in MariaDB using a normalized schema. This approach is cost-effective and integrates well with existing SQL-based systems for real-time or historical analysis.</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

Related Integrations

HTTP and InfluxDB Integration

The HTTP plugin collects metrics from one or more HTTP(S) endpoints. It supports various authentication methods and configuration options for data formats.

View Integration

Kafka and InfluxDB Integration

This plugin reads messages from Kafka and allows the creation of metrics based on those messages. It supports various configurations including different Kafka settings and message processing options.

View Integration

Kinesis and InfluxDB Integration

The Kinesis plugin allows for reading metrics from AWS Kinesis streams. It supports multiple input data formats and offers checkpointing features with DynamoDB for reliable message processing.

View Integration

Start building now

Download Telegraf today or learn more about InfluxDB.

telegraf Get started with InfluxDB
Product & Solutions
Developers
Company

548 Market St, PMB 77953
San Francisco, California 94104

© 2025 InfluxData Inc. All Rights Reserved.

Legal Security Cookie Policy Comparison