Tail and Parquet Integration

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.

This plugin writes metrics to parquet files, utilizing a schema based on the metrics grouped by name. It supports file rotation and buffered writing for optimal performance.

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.

Parquet

The Parquet output plugin for Telegraf writes metrics to parquet files, which are columnar storage formats optimized for analytics. By default, this plugin groups metrics by their name, writing them to a single file. If a metric’s schema does not align with existing schemas, those metrics are dropped. The plugin generates an Apache Arrow schema based on all grouped metrics, ensuring that the schema reflects the union of all fields and tags. It operates in a buffered manner, meaning it temporarily holds metrics in memory before writing them to disk for efficiency. Parquet files require proper closure to ensure readability, and this is crucial when using the plugin, as improper closure can lead to unreadable files. Additionally, the plugin supports file rotation after specific time intervals, preventing overwrites of existing files and schema conflicts when a file with the same name already exists.

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

Parquet

[[outputs.parquet]]
  ## Directory to write parquet files in. If a file already exists the output
  ## will attempt to continue using the existing file.
  # directory = "."
  
  ## Files are rotated after the time interval specified. When set to 0 no time
  ## based rotation is performed.
  # rotation_interval = "0h"
  
  ## Timestamp field name
  ## Field name to use to store the timestamp. If set to an empty string, then
  ## the timestamp is omitted.
  # timestamp_field_name = "timestamp"

Input and output integration examples

Tail

1. 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.

2. 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.

3. 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.

4. 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.

Parquet

1. Data Lake Ingestion: Utilize the Parquet plugin to store metrics from various sources into a data lake. By writing metrics in parquet format, you establish a standardized and efficient way to manage time-series data, enabling faster querying capabilities and seamless integration with analytics tools like Apache Spark or AWS Athena. This setup can significantly improve data retrieval times and analysis workflows.

2. Long-term Storage of Metrics: Implement the Parquet plugin in a monitoring setup where metrics are collected over time from multiple applications. This allows for long-term storage of performance data in a compact format, making it cost-effective to store vast amounts of historical data while preserving the ability for quick retrieval and analysis later on. By archiving metrics in parquet files, organizations can maintain compliance and create detailed reports from historical performance trends.

3. Analytics and Reporting: After writing metrics to parquet files, leverage tools like Apache Arrow or PyArrow to perform complex analytical queries directly on the files without needing to load all the data into memory. This can enhance reporting capabilities, allowing teams to generate insights and visualization from large datasets efficiently, thereby improving decision-making processes based on accurate, up-to-date performance metrics.

4. Integrating with Data Warehouses: Use the Parquet plugin as part of a data integration pipeline that feeds into a modern data warehouse. By converting metrics to parquet format, the data can be easily ingested by systems like Snowflake or Google BigQuery, enabling powerful analytics and business intelligence capabilities that drive actionable insights from the collected metrics.

Feedback

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