SNMP and Parquet Integration

Input and output integration overview

The SNMP plugin allows you to collect a variety of metrics from SNMP (Simple Network Management Protocol) agents. It provides flexibility in how data is retrieved, whether collecting single metrics or entire tables.

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

SNMP

This plugin uses polling to gather metrics from SNMP agents, supporting retrieval of individual OIDs and complete SNMP tables. It can be configured to handle multiple SNMP versions, authentication, and other features.

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

SNMP

[[inputs.snmp]]
  agents = ["udp://127.0.0.1:161"]

  [[inputs.snmp.field]]
    oid = "RFC1213-MIB::sysUpTime.0"
    name = "sysUptime"
    conversion = "float(2)"

  [[inputs.snmp.field]]
    oid = "RFC1213-MIB::sysName.0"
    name = "sysName"
    is_tag = true

  [[inputs.snmp.table]]
    oid = "IF-MIB::ifTable"
    name = "interface"
    inherit_tags = ["sysName"]

    [[inputs.snmp.table.field]]
      oid = "IF-MIB::ifDescr"
      name = "ifDescr"
      is_tag = true

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

SNMP

1. Basic SNMP Configuration: Collect metrics from a local SNMP agent using typical SNMP community string settings. This setup is ideal for local monitoring of device performance.
2. Advanced SNMPv3 Setup: Securely collect metrics using SNMPv3 with authentication and encryption to enhance security. This configuration is recommended for production environments.
3. Collect Interface Metrics: Configure the plugin to collect interface metrics from the device’s SNMP table. Utilize fields to capture specific data points for traffic analysis.
4. Join Two SNMP Tables: By using translation fields, join data from two SNMP tables for a comprehensive view of correlated performance metrics.

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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