Netflow and OSI PI Integration

Input and output integration overview

The Netflow plugin is designed to collect traffic flow data from devices using the Netflow v5, v9 and IPFIX protocols. By capturing detailed flow information, this plugin supports network observability and analysis, enabling administrators to monitor traffic patterns and performance metrics effectively.

This setup converts Telegraf into a lightweight PI Web API publisher, letting you push any Telegraf metric into the OSI PI System with a simple HTTP POST.

Integration details

Netflow

The Netflow plugin serves as a collector for flow data using protocols such as Netflow v5, Netflow v9, and IPFIX. This plugin allows users to gather important flow metrics from devices that support these protocols, including a variety of operational insights about traffic patterns, source/destination information, and protocol usage. The plugin leverages templates sent by flow devices to decode incoming data correctly, and it supports private enterprise number mappings for vendor-specific information. With features like adjustable service addresses and buffer sizes, the plugin provides flexibility in how it can be deployed within various network architectures, making it an essential tool for network monitoring and analysis.

OSI PI

OSI PI is an data management and analytics platform used in energy, manufacturing, and critical infrastructure. The PI Web API is its REST interface, exposing endpoints such as /piwebapi/streams/{WebId}/value that accept JSON payloads containing a Timestamp and Value. By pairing Telegraf’s flexible HTTP output with this endpoint, any metric Telegraf collects—SNMP counters, Modbus readings, Kubernetes stats—can be written directly into PI without installing proprietary interfaces. The configuration above authenticates with Basic or Kerberos, serializes each batch to JSON, and renders a minimal body template that aligns with PI Web API’s single-value write contract. Because Telegraf already supports batching, TLS, proxies, and custom headers, this approach scales from edge gateways to cloud VMs, allowing organizations to back-fill historical data, stream live telemetry, or mirror non-PI sources (e.g., Prometheus) into the PI data archive. It also sidesteps older SDK dependencies and enables hybrid architectures where PI remains on-prem while Telegraf agents run in containers or IIoT devices.

Configuration

Netflow

[[inputs.netflow]]
  ## Address to listen for netflow,ipfix or sflow packets.
  ##   example: service_address = "udp://:2055"
  ##            service_address = "udp4://:2055"
  ##            service_address = "udp6://:2055"
  service_address = "udp://:2055"

  ## Set the size of the operating system's receive buffer.
  ##   example: read_buffer_size = "64KiB"
  ## Uses the system's default if not set.
  # read_buffer_size = ""

  ## Protocol version to use for decoding.
  ## Available options are
  ##   "ipfix"      -- IPFIX / Netflow v10 protocol (also works for Netflow v9)
  ##   "netflow v5" -- Netflow v5 protocol
  ##   "netflow v9" -- Netflow v9 protocol (also works for IPFIX)
  ##   "sflow v5"   -- sFlow v5 protocol
  # protocol = "ipfix"

  ## Private Enterprise Numbers (PEN) mappings for decoding
  ## This option allows to specify vendor-specific mapping files to use during
  ## decoding.
  # private_enterprise_number_files = []

  ## Log incoming packets for tracing issues
  # log_level = "trace"

OSI PI

[[outputs.http]]
  ## PI Web API endpoint for writing a single value to a PI Point by Web ID
  url = "https://${PI_HOST}/piwebapi/streams/${WEB_ID}/value"

  ## Use POST for each batch
  method       = "POST"
  content_type = "application/json"

  ## Basic-auth header (base64-encoded "DOMAIN\\user:password")
  headers = { Authorization = "Basic ${BASIC_AUTH}" }

  ## Serialize Telegraf metrics as JSON
  data_format           = "json"
  json_timestamp_units  = "1ms"

  ## Render the JSON body that PI Web API expects
  body_template = """
  {{ range .Metrics -}}
  { "Timestamp": "{{ .timestamp | formatDate \"2006-01-02T15:04:05Z07:00\" }}", "Value": {{ index .fields 0 }} }
  {{ end -}}
  """

  ## Tune networking / batching if needed
  # timeout     = "10s"
  # batch_size  = 1

Input and output integration examples

Netflow

1. Traffic Analysis and Visualization: Use the Netflow plugin to collect traffic flow data and visualize it in real-time using an analytics platform. Administrators can create dashboards that display traffic patterns and anomalies, helping them understand bandwidth usage and user behavior.

2. Network Performance Optimization: Integrate the Netflow plugin with performance monitoring tools to identify bottlenecks and optimize the network. Analyze collected metrics to pinpoint areas where network resources can be improved, enhancing overall system performance.

3. Anomaly Detection for Security: Leverage the Netflow data for security analysis by feeding it into an anomaly detection system. This can help identify unusual traffic patterns that may indicate potential security threats, enabling quicker responses to prevent breaches.

4. Customized Alerts for Network Events: Configure threshold-based alerts using the Netflow plugin metrics to notify network administrators of unusual spikes or drops in traffic. This proactive monitoring can help in quickly addressing potential issues before they escalate.

OSI PI

1. Remote Pump Stations Telemetry Bridge: Install Telegraf on edge gateways at oil-field pump stations, gather flow-meter and vibration readings over Modbus, and POST them to the PI Web API. Operations teams view real-time data in PI Vision without deploying heavyweight PI interfaces, while bandwidth-friendly batching keeps satellite links economical.

2. Green-Energy Micro-Grid Dashboard: Export inverter, battery, and weather metrics from MQTT into Telegraf, which relays them to PI. PI AF analytics can calculate real-time power balance and feed a campus dashboard; historical deltas inform sustainability reports.

3. Brownfield SCADA Modernization: Legacy PLCs logged to CSV are ingested by Telegraf’s tail input; each row is parsed and immediately sent to PI via HTTP, creating a live data stream that co-exists with archival files while the SCADA upgrade proceeds incrementally.

4. Synthetic Data Generator for Training: Telegraf’s exec input can run a script that emits simulated sensor patterns. Posting those metrics to a non-production PI server through the Web API supplies realistic datasets for PI Vision training sessions without risking production tags.

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