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Tail and Microsoft SQL Server 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>Telegraf’s SQL plugin facilitates the storage of metrics in SQL databases. When configured for Microsoft SQL Server, it supports the specific DSN format and schema requirements, allowing for seamless integration with SQL Server.</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>

Microsoft SQL Server

<p>Telegraf’s SQL output plugin for Microsoft SQL Server is designed to capture and store metric data by dynamically creating tables and columns that match the structure of incoming data. This integration leverages the go-mssqldb driver, which follows the SQL Server connection protocol through a DSN that includes server, port, and database details. Although the driver is considered experimental due to limited unit tests, it provides robust support for dynamic schema generation and data insertion, enabling detailed time-stamped records of system performance. This flexibility makes it a valuable tool for environments that demand reliable and granular metric logging, despite its experimental status.</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>

Microsoft SQL Server

<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 = "mssql" ## Data source name ## For Microsoft SQL Server, the DSN typically includes the server, port, username, password, and database name. ## Example DSN: "sqlserver://username:password@localhost:1433?database=telegraf" data_source_name = "sqlserver://username:password@localhost:1433?database=telegraf" ## 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} - table name as a quoted identifier table_exists_template = "SELECT 1 FROM {TABLE} LIMIT 1" ## Initialization SQL (optional) init_sql = "" ## 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 ## Metric type to SQL type conversion ## You can customize the mapping if needed. #[outputs.sql.convert] # integer = "INT" # real = "DOUBLE" # text = "TEXT" # timestamp = "TIMESTAMP" # defaultvalue = "TEXT" # unsigned = "UNSIGNED" # bool = "BOOL" </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>

Microsoft SQL Server

<ol> <li> <p><strong>Enterprise Application Monitoring</strong>: Leverage the plugin to capture detailed performance metrics from enterprise applications running on SQL Server. This setup allows IT teams to analyze system performance, track transaction times, and identify bottlenecks across complex, multi-tier environments.</p> </li> <li> <p><strong>Dynamic Infrastructure Auditing</strong>: Deploy the plugin to create a dynamic audit log of infrastructure changes and performance metrics in SQL Server. This use case is ideal for organizations that require real-time monitoring and historical analysis of system performance for compliance and optimization.</p> </li> <li> <p><strong>Automated Performance Benchmarking</strong>: Use the plugin to continuously record and analyze performance metrics of SQL Server databases. This enables automated benchmarking, where historical data is compared against current performance, helping to quickly identify anomalies or degradation in service.</p> </li> <li> <p><strong>Integrated DevOps Dashboards</strong>: Integrate the plugin with DevOps monitoring tools to feed real-time metrics from SQL Server into centralized dashboards. This provides a holistic view of application health, allowing teams to correlate SQL Server performance with application-level events for faster troubleshooting and proactive maintenance.</p> </li> </ol>

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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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