Pros of Speedscope

• Speedscope provides a more visually appealing and interactive flame graph visualization compared to what-studio/profiling.
• Speedscope supports a wider range of input formats, including Chrome Trace, Firefox Profiler, and more.
• Speedscope has a more user-friendly interface with features like zooming, panning, and filtering.

Cons of Speedscope

• Speedscope may have a steeper learning curve compared to the more straightforward what-studio/profiling.
• Speedscope is a standalone application, while what-studio/profiling is integrated into the What Studio IDE.
• Speedscope may not provide the same level of integration and workflow as what-studio/profiling within the What Studio ecosystem.

Code Comparison

Speedscope:

function renderFlameGraph(data) {
  const flamegraph = new Flamegraph(data, {
    onClick: (node) => {
      console.log(`Clicked on ${node.name}`);
    },
    onHover: (node) => {
      console.log(`Hovered over ${node.name}`);
    },
  });

  flamegraph.render(document.getElementById('flame-graph'));
}

what-studio/profiling:

def profile_function(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        with profiler.profile() as prof:
            result = func(*args, **kwargs)
        prof.export_chrome_trace("profile.json")
        return result
    return wrapper

Pros of FlameGraph

• FlameGraph is a widely-used and well-established tool for visualizing performance data, with a large user community and extensive documentation.
• The tool supports a variety of input formats, including perf, DTrace, and LTTng, making it versatile for different profiling use cases.
• FlameGraph provides a clear and intuitive visualization of performance data, making it easier to identify performance bottlenecks and optimize code.

Cons of FlameGraph

• FlameGraph is primarily a visualization tool and does not provide the same level of profiling capabilities as what-studio/profiling, which includes features like call graph analysis and CPU/memory profiling.
• The tool may have a steeper learning curve for users who are not familiar with the underlying profiling tools and data formats.

Code Comparison

FlameGraph:

#!/usr/bin/env perl

use strict;
use warnings;

my $in = shift || die "Usage: $0 input.folded\n";
my %seen;
while (<>) {
    chomp;
    my @lines = split /;/, $_;
    my $frame = pop @lines;
    my $count = $frame =~ s/ +(\d+)$//r;
    my $func = join ";", @lines;
    $seen{$func} += $count;
}

for my $func (sort { $seen{$b} <=> $seen{$a} } keys %seen) {
    print "$func $seen{$func}\n";
}

what-studio/profiling:

import os
import sys
import time
import psutil
import signal
import subprocess
from collections import defaultdict

def profile(cmd, output_file):
    start_time = time.time()
    process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)

    while process.poll() is None:
        time.sleep(0.1)
        if time.time() - start_time > 60:
            os.kill(process.pid, signal.SIGINT)
            break

    with open(output_file, 'w') as f:
        f.write(process.stdout.read().decode())
        f.write(process.stderr.read().decode())

Pros of Flamescope

• Flamescope provides a web-based interface for visualizing and analyzing CPU profiles, making it easier to share and collaborate on performance analysis.
• The tool supports a wide range of profiling formats, including Linux perf, DTrace, and JFR, allowing it to be used with a variety of programming languages and environments.
• Flamescope's visualization features, such as the flame graph, provide a clear and intuitive way to identify performance bottlenecks in complex systems.

Cons of Flamescope

• Flamescope is a standalone tool, which means it requires additional setup and configuration to integrate with existing development workflows.
• The tool's web-based interface may not be as familiar or accessible to developers who prefer command-line tools or desktop applications.
• Flamescope's focus on CPU profiling may not be as useful for developers working on applications with different performance characteristics, such as I/O-bound or memory-intensive workloads.

Code Comparison

Profiling:

import profiling

with profiling.Profile():
    # Your code here
    pass

Flamescope:

import flamescope

with flamescope.Profile():
    # Your code here
    pass

The code snippets above demonstrate the basic usage of Profiling and Flamescope, respectively. Both tools provide a context manager-based API for capturing and analyzing performance data, but the underlying implementation and features may differ.

Pros of Pyflame

• Pyflame is a Python profiler that can trace a running Python process without any code instrumentation, making it a non-invasive profiling tool.
• Pyflame supports a wide range of Python versions, from 2.7 to 3.9, making it a versatile tool for different Python environments.
• Pyflame provides detailed information about the running Python process, including function call stacks and CPU usage.

Cons of Pyflame

• Pyflame requires the ptrace system call, which may not be available on all platforms or may require additional permissions.
• Pyflame may have performance overhead when profiling long-running or high-concurrency Python applications.
• Pyflame's output can be complex and may require some expertise to interpret, especially for large or complex Python applications.

Code Comparison

Pyflame:

import pyflame

pid = 12345
flame_graph = pyflame.Flamegraph(pid)
flame_graph.render('profile.svg')

Profiling:

from profiling import Profiler

profiler = Profiler()
profiler.start()
# Run your code here
profiler.stop()
profiler.save('profile.html')

The key difference is that Pyflame uses the ptrace system call to trace the running Python process, while Profiling uses code instrumentation to collect profiling data. Pyflame's approach is more non-invasive, but may have some platform-specific limitations.

Pros of google/pprof

• Supports a wide range of programming languages, including Go, C, C++, and Rust.
• Provides a comprehensive set of tools for profiling and analyzing performance data.
• Integrates well with the Go standard library's runtime/pprof package.

Cons of google/pprof

• Requires manual instrumentation of the code to collect profiling data.
• May have a steeper learning curve for developers unfamiliar with profiling tools.
• Limited support for visualizing and interpreting profiling data.

Code Comparison

what-studio/profiling

import profiling

@profiling.profile
def my_function(arg1, arg2):
    # Function implementation
    pass

google/pprof

import "runtime/pprof"

func main() {
    // Start CPU profiling
    pprof.StartCPUProfile(os.Stdout)
    defer pprof.StopCPUProfile()

    // Call the function to be profiled
    myFunction(arg1, arg2)
}