Syslog and Parquet Integration

Input and output integration overview

The Syslog plugin enables the collection of syslog messages from various sources using standard networking protocols. This functionality is critical for environments where systems need to be monitored and logged efficiently.

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

Syslog

The Syslog plugin for Telegraf captures syslog messages transmitted over various protocols such as TCP, UDP, and TLS. It supports both RFC 5424 (the newer syslog protocol) and the older RFC 3164 (BSD syslog protocol). This plugin operates as a service input, effectively starting a service that listens for incoming syslog messages. Unlike traditional plugins, service inputs may not function with standard interval settings or CLI options like --once. It includes options for setting network configurations, socket permissions, message handling, and connection handling. Furthermore, the integration with Rsyslog allows forwarding of logging messages, making it a powerful tool for collecting and relaying system logs in real-time, thus seamlessly integrating into monitoring and logging systems.

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

Syslog

[[inputs.syslog]]
  ## Protocol, address and port to host the syslog receiver.
  ## If no host is specified, then localhost is used.
  ## If no port is specified, 6514 is used (RFC5425#section-4.1).
  ##   ex: server = "tcp://localhost:6514"
  ##       server = "udp://:6514"
  ##       server = "unix:///var/run/telegraf-syslog.sock"
  ## When using tcp, consider using 'tcp4' or 'tcp6' to force the usage of IPv4
  ## or IPV6 respectively. There are cases, where when not specified, a system
  ## may force an IPv4 mapped IPv6 address.
  server = "tcp://127.0.0.1:6514"

  ## Permission for unix sockets (only available on unix sockets)
  ## This setting may not be respected by some platforms. To safely restrict
  ## permissions it is recommended to place the socket into a previously
  ## created directory with the desired permissions.
  ##   ex: socket_mode = "777"
  # socket_mode = ""

  ## Maximum number of concurrent connections (only available on stream sockets like TCP)
  ## Zero means unlimited.
  # max_connections = 0

  ## Read timeout (only available on stream sockets like TCP)
  ## Zero means unlimited.
  # read_timeout = "0s"

  ## Optional TLS configuration (only available on stream sockets like TCP)
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key  = "/etc/telegraf/key.pem"
  ## Enables client authentication if set.
  # tls_allowed_cacerts = ["/etc/telegraf/clientca.pem"]

  ## Maximum socket buffer size (in bytes when no unit specified)
  ## For stream sockets, once the buffer fills up, the sender will start
  ## backing up. For datagram sockets, once the buffer fills up, metrics will
  ## start dropping. Defaults to the OS default.
  # read_buffer_size = "64KiB"

  ## Period between keep alive probes (only applies to TCP sockets)
  ## Zero disables keep alive probes. Defaults to the OS configuration.
  # keep_alive_period = "5m"

  ## Content encoding for message payloads
  ## Can be set to "gzip" for compressed payloads or "identity" for no encoding.
  # content_encoding = "identity"

  ## Maximum size of decoded packet (in bytes when no unit specified)
  # max_decompression_size = "500MB"

  ## Framing technique used for messages transport
  ## Available settings are:
  ##   octet-counting  -- see RFC5425#section-4.3.1 and RFC6587#section-3.4.1
  ##   non-transparent -- see RFC6587#section-3.4.2
  # framing = "octet-counting"

  ## The trailer to be expected in case of non-transparent framing (default = "LF").
  ## Must be one of "LF", or "NUL".
  # trailer = "LF"

  ## Whether to parse in best effort mode or not (default = false).
  ## By default best effort parsing is off.
  # best_effort = false

  ## The RFC standard to use for message parsing
  ## By default RFC5424 is used. RFC3164 only supports UDP transport (no streaming support)
  ## Must be one of "RFC5424", or "RFC3164".
  # syslog_standard = "RFC5424"

  ## Character to prepend to SD-PARAMs (default = "_").
  ## A syslog message can contain multiple parameters and multiple identifiers within structured data section.
  ## Eg., [id1 name1="val1" name2="val2"][id2 name1="val1" nameA="valA"]
  ## For each combination a field is created.
  ## Its name is created concatenating identifier, sdparam_separator, and parameter name.
  # sdparam_separator = "_"

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

Syslog

1. Centralized Log Management: Use the Syslog plugin to aggregate log messages from multiple servers into a central logging system. This setup can help in monitoring overall system health, troubleshooting issues effectively, and maintaining audit trails by collecting syslog data from different sources.

2. Real-Time Alerting: Integrate the Syslog plugin with alerting tools to trigger real-time notifications when specific log patterns or errors are detected. For example, if a critical system error appears in the logs, an alert can be sent to the operations team, minimizing downtime and performing proactive maintenance.

3. Security Monitoring: Leverage the Syslog plugin for security monitoring by capturing logs from firewalls, intrusion detection systems, and other security devices. This logging capability enhances security visibility and helps in investigating potentially malicious activities by analyzing the captured syslog data.

4. Application Performance Tracking: Utilize the Syslog plugin to monitor application performance by collecting logs from various applications. This integration helps in analyzing the application’s behavior and performance trends, thus aiding in optimizing application processes and ensuring smoother operation.

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.

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