Tail and MySQL Integration
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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
The Tail Telegraf plugin collects metrics by tailing specified log files, capturing new log entries in real-time for further analysis.
The Telegraf SQL plugin allows you to store metrics from Telegraf directly into a MySQL database, making it easier to analyze and visualize the collected metrics.
Integration details
Tail
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 tail
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.
MySQL
Telegraf’s SQL output plugin is designed to seamlessly write metric data to a SQL database by dynamically creating tables and columns based on the incoming metrics. When configured for MySQL, the plugin leverages the go-sql-driver/mysql, which requires enabling the ANSI_QUOTES SQL mode to ensure proper handling of quoted identifiers. This dynamic schema creation approach ensures that each metric is stored in its own table with a structure derived from its fields and tags, providing a detailed, timestamped record of system performance. The flexibility of the plugin allows it to handle high-throughput environments, making it ideal for scenarios that demand robust, granular metric logging and historical data analysis.
Configuration
Tail
[[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" -> recursively find all .log files in /var/log
## "/var/log/*/*.log" -> find all .log files with a parent dir in /var/log
## "/var/log/apache.log" -> just tail the apache log file
## "/var/log/log[!1-2]* -> tail files without 1-2
## "/var/log/log[^1-2]* -> 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
MySQL
[[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})"
## 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"
Input and output integration examples
Tail
-
Real-Time Server Health Monitoring: 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.
-
Centralized Log Management: 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.
-
Security Incident Detection: 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.
-
Dynamic Application Performance Insights: 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.
MySQL
-
Real-Time Web Analytics Storage: Leverage the plugin to capture website performance metrics and store them in MySQL. This setup enables teams to monitor user interactions, analyze traffic patterns, and dynamically adjust site features based on real-time data insights.
-
IoT Device Monitoring: Utilize the plugin to collect metrics from a network of IoT sensors and log them into a MySQL database. This use case supports continuous monitoring of device health and performance, allowing for predictive maintenance and immediate response to anomalies.
-
Financial Transaction Logging: Record high-frequency financial transaction data with precise timestamps. This approach supports robust audit trails, real-time fraud detection, and comprehensive historical analysis for compliance and reporting purposes.
-
Application Performance Benchmarking: Integrate the plugin with application performance monitoring systems to log metrics into MySQL. This facilitates detailed benchmarking and trend analysis over time, enabling organizations to identify performance bottlenecks and optimize resource allocation effectively.
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.
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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