Nginx and OSI PI Integration
Powerful performance with an easy integration, powered by Telegraf, the open source data connector built by InfluxData.
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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
Input and output integration overview
The Nginx plugin for Telegraf is designed to collect status metrics from Nginx web servers, providing real-time insights into server operation metrics.
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
Nginx
This plugin gathers status metrics from Nginx. It utilizes the ngx_http_stub_status_module to collect basic metrics related to the server’s performance. The plugin provides valuable insights into active connections, requests handled, and the current state of various metrics. This real-time data is essential for monitoring web server performance and ensuring optimal operations. The configuration allows users to specify the URL for the Nginx status endpoint, set timeouts, and configure TLS settings if necessary.
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
Nginx
[[inputs.nginx]]
## An array of Nginx stub_status URI to gather stats.
urls = ["http://localhost/server_status"]
## Optional TLS Config
# tls_ca = "/etc/telegraf/ca.pem"
# tls_cert = "/etc/telegraf/cert.pem"
# tls_key = "/etc/telegraf/key.pem"
## Use TLS but skip chain & host verification
# insecure_skip_verify = false
## HTTP response timeout (default: 5s)
response_timeout = "5s"
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
Nginx
-
Web Performance Monitoring: Use the Nginx plugin to gather performance metrics from various Nginx servers across your infrastructure. By visualizing these metrics in real-time dashboards, teams can track performance trends, identify bottlenecks, and enhance the user experience on their web applications. Implementing such monitoring allows businesses to proactively address performance issues before they impact end-users.
-
Load Balancer Monitoring: Integrate this plugin with your load balancers to track the performance of backend Nginx servers. By collecting statistics like ‘active connections’ and ‘requests handled’, your operations team can ensure that traffic is flowing optimally and that no single server is overwhelmed. This proactive approach to load balancing prevents service downtime and enhances user experience.
-
Automated Alerting Systems: Combine the Nginx plugin with alerting services to automatically notify your team when a server’s metrics exceed predefined thresholds. For instance, if the number of active connections is too high, the system can trigger alerts so that corrective actions can be taken immediately, thus maintaining service quality and reliability.
-
Historical Data Analysis: Store the metrics collected by the Nginx plugin in a time-series database to analyze historical performance trends. This analysis can uncover periods of high traffic or poor performance, allowing for data-driven decisions about infrastructure scaling and optimization. By understanding past trends, organizations can better prepare for future demands.
OSI PI
-
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.
-
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.
-
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. -
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.
Feedback
Thank you for being part of our community! If you have any general feedback or found any bugs on these pages, we welcome and encourage your input. Please submit your feedback in the InfluxDB community Slack.
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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