# How to Profile Telegraf Performance Using pprof > Profile Telegraf performance effectively using pprof. Learn to capture and analyze CPU, memory, and goroutine profiles with simple Go tooling and the --pprof-addr flag. - Repository: [InfluxData/telegraf](https://github.com/influxdata/telegraf) - Tags: performance - Published: 2026-05-14 --- **Launch Telegraf with the `--pprof-addr` flag to expose Go's `net/http/pprof` endpoints, then use standard Go tooling to capture and analyze CPU, memory, and goroutine profiles.** Telegraf, the open-source server agent from InfluxData for collecting and reporting metrics, ships with built-in support for Go's standard profiling package. Enabling **pprof** allows you to diagnose performance bottlenecks, investigate memory leaks, and analyze goroutine dumps without modifying source code. This guide explains how to activate and use pprof for profiling Telegraf performance based on the actual implementation in the `influxdata/telegraf` repository. ## Enabling the pprof HTTP Server To activate profiling, append the **`--pprof-addr`** flag when starting Telegraf: ```bash telegraf --config /etc/telegraf/telegraf.conf --pprof-addr localhost:6060 ``` This command instructs Telegraf to start an auxiliary HTTP server bound to the specified address. The implementation defines this flag in **[`cmd/telegraf/main.go`](https://github.com/influxdata/telegraf/blob/main/cmd/telegraf/main.go)** (lines 317‑322), where the address string is passed to the agent. If a value is present, the profiler initializes via `pprof.Start(address)` at lines 231‑233 in the same file. The actual server setup resides in **[`cmd/telegraf/pprof.go`](https://github.com/influxdata/telegraf/blob/main/cmd/telegraf/pprof.go)** (lines 6‑35). This file imports the standard library package using `_ "net/http/pprof"`, which registers the profiling handlers with `http.DefaultServeMux` as an import side-effect. The code then constructs the listen address and logs the accessible URL on startup. ## How the Profiler Operates Internally The **pprof** integration runs independently of Telegraf's data collection pipeline. When `pprof.Start(addr)` executes, it creates a new `http.Server` instance and runs it in a dedicated goroutine. This architecture ensures that profiling requests do not block metric ingestion or output operations. Error handling occurs through a dedicated error channel returned by `pprof.ErrChan()`. The main Telegraf agent monitors this channel, as seen in **[`cmd/telegraf/telegraf.go`](https://github.com/influxdata/telegraf/blob/main/cmd/telegraf/telegraf.go)** (lines 200‑201), logging any startup failures immediately while continuing to run the primary application. ## Accessing Profiling Endpoints Once Telegraf starts with the `--pprof-addr` flag, navigate to the following HTTP endpoints: - **`/debug/pprof/`** – Index page listing all available profiles. - **`/debug/pprof/heap`** – Current memory heap snapshot for analyzing allocations. - **`/debug/pprof/goroutine`** – Stack traces for all running goroutines. - **`/debug/pprof/profile?seconds=30`** – 30-second CPU profile sample. - **`/debug/pprof/trace?seconds=10`** – Execution trace for analyzing latency and concurrency. These endpoints are served by Go's standard `net/http/pprof` handlers, which read runtime statistics directly from the same process. ## Capturing Profiles to File Use `curl` or any HTTP client to download profiles for local analysis. The following commands capture common profile types: ```bash # Capture a 30-second CPU profile curl -s http://localhost:6060/debug/pprof/profile?seconds=30 > cpu.prof # Dump current heap usage curl -s http://localhost:6060/debug/pprof/heap > mem.prof # Export full execution trace curl -s http://localhost:6060/debug/pprof/trace?seconds=10 > trace.bin ``` For quick ad-hoc analysis, you can also open the endpoints directly in a browser to view the raw profile data or index links. ## Analyzing Profiles with Go Tooling Process captured profiles using the standard Go toolchain. The `go tool pprof` command provides an interactive terminal for exploring CPU and memory data: ```bash # Launch interactive mode for CPU analysis go tool pprof cpu.prof ``` Within the interactive shell, common commands include: - **`top5`** – Display the five functions consuming the most resources. - **`list `** – Show annotated source code for a specific function. - **`peek `** – View callers and callees matching a pattern. To generate visualizations, use the `-png` or `-svg` flags: ```bash # Create a flame graph visualization (requires Graphviz) go tool pprof -png cpu.prof > cpu.png # Analyze heap directly from running instance go tool pprof -png http://localhost:6060/debug/pprof/heap > heap.png ``` For execution traces, use the dedicated trace viewer: ```bash go tool trace trace.bin ``` This opens a browser-based interface for analyzing latency, goroutine scheduling, and blocking operations. ## Best Practices for Production Profiling When running **pprof** in production environments, consider these recommendations: - **Bind to localhost only**: Use `--pprof-addr 127.0.0.1:6060` to prevent external network exposure. - **Firewall the port**: If remote access is necessary, restrict the port via firewall rules or VPN access rather than binding to `0.0.0.0`. - **Adjust sampling duration**: Increase the `seconds` parameter in CPU profile URLs for long-running bottlenecks, or decrease for quick snapshots. - **Monitor over time**: For memory leak investigations, capture `/heap` and `/goroutine` profiles at regular intervals to compare growth patterns. - **Separate concerns**: Since the profiler runs in its own goroutine, it does not interfere with Telegraf's metric pipeline, but always verify resource usage on constrained systems. Additional guidance is available in the official **[`docs/PROFILING.md`](https://github.com/influxdata/telegraf/blob/main/docs/PROFILING.md)** file within the repository, which provides complementary examples and links to Go profiling documentation. ## Summary - Telegraf exposes **pprof** endpoints via the `--pprof-addr` CLI flag, implemented in [`cmd/telegraf/main.go`](https://github.com/influxdata/telegraf/blob/main/cmd/telegraf/main.go) and [`cmd/telegraf/pprof.go`](https://github.com/influxdata/telegraf/blob/main/cmd/telegraf/pprof.go). - The profiler uses Go's `_ "net/http/pprof"` import to register handlers and runs in a separate goroutine isolated from data collection. - Access **CPU**, **heap**, **goroutine**, and **trace** profiles via standard HTTP endpoints under `/debug/pprof/`. - Capture profiles using `curl` and analyze them locally with `go tool pprof` or visualize them with Graphviz. - Always bind the profiler to localhost or restrict network access in production environments to maintain security. ## Frequently Asked Questions ### Does enabling pprof impact Telegraf's metric collection performance? No. According to the source code in [`cmd/telegraf/pprof.go`](https://github.com/influxdata/telegraf/blob/main/cmd/telegraf/pprof.go), the profiler runs in a dedicated HTTP server within a separate goroutine. It reads runtime statistics from the same process but does not interfere with Telegraf's data collection pipeline, ensuring minimal overhead during normal operations. ### Where is the official documentation for Telegraf profiling located? The comprehensive user guide resides in **[`docs/PROFILING.md`](https://github.com/influxdata/telegraf/blob/main/docs/PROFILING.md)** in the `influxdata/telegraf` repository. This document provides additional examples, command references, and links to the underlying `net/http/pprof` package documentation. ### Can I enable pprof on a Telegraf instance that is already running? No. The `--pprof-addr` flag must be supplied at startup. The flag is parsed in [`cmd/telegraf/main.go`](https://github.com/influxdata/telegraf/blob/main/cmd/telegraf/main.go) and triggers the `pprof.Start(address)` call during initialization. To enable profiling, you must restart the Telegraf process with the flag included. ### How do I capture a profile if I cannot install `go` on the production server? You can download the binary profile files directly using `curl` or `wget` from the HTTP endpoints (e.g., `/debug/pprof/heap` or `/debug/pprof/profile`), then transfer the `.prof` files to a development machine with Go installed for analysis using `go tool pprof`.