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Performance Profiling: perf, Flamegraphs, py-spy, pprof

#technology #devtools #productivity

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Performance Profiling: perf, Flamegraphs, py-spy, pprof

Introduction

Performance profiling identifies where your application spends its time — CPU, memory, I/O, or blocking. Without profiling, optimization is guesswork. This article covers four profiling approaches: perf for system-level Linux profiling, flamegraphs for visualization, py-spy for Python without code changes, and pprof for Go applications.

perf (Linux Profiler)

The built-in Linux profiler for CPU, hardware events, and tracepoints:

# CPU profiling

perf record -F 99 -g ./myapp          # Sample at 99Hz with call graphs

perf record -F 99 -p PID -g --sleep 30  # Profile running process for 30s

perf report --stdio                   # Text report

perf report -g graph                  # Call graph report

# Common events

perf stat ./myapp                     # Execution statistics

perf stat -e cache-misses ./myapp     # Cache miss analysis

perf stat -e branch-misses ./myapp    # Branch prediction

perf stat -e context-switches -p PID  # Context switch monitoring

# Hardware event sampling

perf record -e cycles -F 99 -a -g --sleep 10  # System-wide CPU sampling

# Tracepoints

perf record -e sched:sched_switch -a -g  # Context switch tracing

perf record -e syscalls:sys_enter_write -a  # Write syscall tracing

# Top-like live view

perf top -p PID

perf top -e cache-misses

# Generate flamegraph data

perf script > out.perf
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Key metrics: cycles for CPU time, cache-misses for memory bottleneck detection, context-switches for contention issues.

Flamegraphs

Brendan Gregg's visualization for profiler output:

# Install FlameGraph tools

git clone https://github.com/brendangregg/FlameGraph

# Generate flamegraph from perf data

perf script | ./FlameGraph/stackcollapse-perf.pl > out.folded

./FlameGraph/flamegraph.pl out.folded > flamegraph.svg

# Generate differential flamegraph (before/after)

# After optimization:

perf script | ./FlameGraph/stackcollapse-perf.pl > optimized.folded

./FlameGraph/difffolded.pl before.folded optimized.folded | ./FlameGraph/flamegraph.pl > diff.svg
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Reading flamegraphs: The x-axis shows stack profile population (not time). Each rectangle is a function call; wider rectangles mean more CPU time. The y-axis is stack depth. Look for wide top rectangles — those are the hot functions.

For other languages:

# JavaScript (Node.js)

node --perf-basic-prof app.js

perf script | ./FlameGraph/stackcollapse-perf.pl > out.folded

# Python with py-spy

py-spy record -o profile.svg --pid $PID

# Go with pprof

go tool pprof -http=:8080 http://localhost:6060/debug/pprof/profile?seconds=30
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py-spy

Sampling profiler for Python without modifying code:

# Installation

pip install py-spy

# Profile a running process

py-spy record -o profile.svg --pid 12345

py-spy record -o profile.svg -- python myapp.py

# Top-like live view

py-spy top --pid 12345

# Dump current stack traces

py-spy dump --pid 12345

# Profile specific duration

py-spy record -o profile.svg --pid 12345 --duration 30

# With subprocesses

py-spy record -o profile.svg -- python myapp.py --subprocesses

# Native frames

py-spy record --native -o profile.svg --pid 12345

# Save raw data for later analysis

py-spy record -o profile.raw --pid 12345 --format raw
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Key advantages: No code changes required, works with running processes, safe for production (read-only), native code frame support.

pprof (Go)

Go's built-in profiling tool:

package main

import (

    "net/http"

    _ "net/http/pprof"

)

func main() {

    // Start pprof HTTP server

    go func() {

        http.ListenAndServe("localhost:6060", nil)

    }()

    // Your application code...

}

# Collect profiles

go tool pprof http://localhost:6060/debug/pprof/profile?seconds=30  # CPU

go tool pprof http://localhost:6060/debug/pprof/heap               # Memory

go tool pprof http://localhost:6060/debug/pprof/goroutine          # Goroutines

go tool pprof http://localhost:6060/debug/pprof/block              # Blocking

go tool pprof http://localhost:6060/debug/pprof/mutex              # Mutex contention

# Interactive exploration

go tool pprof cpu.pprof

(pprof) top10           # Top 10 functions

(pprof) list myFunc     # Source with line-level timing

(pprof) web             # Open in browser (requires graphviz)

(pprof) pdf             # Generate PDF

(pprof) peek myFunc     # Caller/callee view

# Web interface

go tool pprof -http=:8080 cpu.pprof

# Allocations profiling

go tool pprof -http=:8080 http://localhost:6060/debug/pprof/allocs

# Compare profiles

go tool pprof -http=:8080 -diff_base=before.pprof after.pprof
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