Benchmark-script-performance-metrics

To benchmark the performance metrics of the optimized js_all_in_one_recon.sh script, we need to measure key aspects such as execution time, CPU usage, memory consumption, I/O operations, and regex efficiency. Below, I outline a comprehensive approach to benchmarking the script, including tools, methodologies, and specific metrics to collect, tailored to the script’s tasks (downloading JS files, prettifying, extracting data with regexes, probing URLs, and generating reports). Since I cannot execute the script directly, I’ll provide a detailed guide on how to benchmark it, including commands to run, metrics to track, and modifications to the script to enable profiling. I’ll also integrate the advanced optimization techniques (parallelism, regex optimization, I/O minimization) discussed previously to ensure we’re measuring the most efficient version.

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Benchmarking Approach

Objectives

• Measure total execution time and time per major step (download, prettify, extract, probe, report).
• Track CPU usage to identify bottlenecks in parallelized tasks or regex processing.
• Monitor memory usage to ensure in-memory processing (/dev/shm) doesn’t exhaust resources.
• Evaluate I/O performance (disk and network) to optimize file writes and downloads.
• Assess regex efficiency to confirm the optimized patterns (ABS_URL_RE, SECRETS_RE, etc.) perform well.
• Compare performance with and without optimizations (e.g., caching, parallel vs. sequential).

Tools

• GNU time: Measure execution time for the entire script and individual steps.
• htop or top: Monitor CPU and memory usage in real-time.
• iotop: Track disk I/O to identify bottlenecks in file writes/reads.
• perf: Profile CPU-intensive operations (e.g., regex processing with rg).
• strace: Count system calls (e.g., file operations, network requests).
• curl and httpx: Measure network performance for downloads and probes.
• ripgrep (rg): Profile regex performance with --stats or --trace.
• jq: Analyze JSON output size and parsing efficiency (if -o json is used).

Setup

• Test Environment: Run on a consistent system (e.g., Linux with 4 CPU cores, 8GB RAM, SSD) to ensure comparable results.
• Input Data: Use a representative js_list_file with 100–1000 URLs to balance realism and test duration. Example:

  echo -e "https://example.com/script1.js\nhttps://example.com/script2.js" > js_files.txt

• Output Directory: Use /dev/shm for in-memory processing, as per the optimized script.
• Dependencies: Ensure all dependencies (curl, rg, httpx, parallel, js-beautify/prettier, jq, timeout) are installed.

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Modifications to the Script for Benchmarking

To collect detailed performance metrics, we’ll modify the script to:

• Add timing for each major step using time.
• Log CPU and memory usage with ps or top.
• Enable rg --stats for regex performance.
• Track I/O operations with a counter for file writes/reads.
• Output a benchmark report in JSON or text format.

Benchmarking Metrics to Collect

1. Execution Time

• Total Time: Measure the entire script runtime.

  time ./js_all_in_one_recon.sh -f js_files.txt -b

• Per-Step Time: Extract from $OUTDIR/*_time.txt files (download, prettify, extract, mapping, probe, cookie_check, report).
  • Example output: download_time.txt shows real 1m23.456s.
  • Summarized in $OUTDIR/benchmark_summary.json (if -b is used).
• Metric: Seconds per step, total seconds.

2. CPU Usage

• Per-Step CPU: Captured via ps -eo %cpu in log_benchmark.
  • Example: cpu_percent: 75.2 for the download step.
• System-Wide CPU: Monitor with htop or:

  top -b -n 1 | head -n 5

• Metric: Average CPU % per step, peak CPU usage.

3. Memory Usage

• Per-Step Memory: Captured via ps -eo rss in log_benchmark.
  • Example: mem_mb: 512.3 for the extraction step.
• System-Wide Memory: Monitor with:

  free -m

• Metric: Peak memory (MB), average memory per step.

4. I/O Operations

• Disk I/O: Track file writes/reads with IO_COUNT in the script.
  • Example: io_ops: 150 in $OUTDIR/benchmark_summary.json.
  • Use iotop for real-time disk I/O:

    sudo iotop -o

• Network I/O: Measure download/probe bandwidth with iftop:

  sudo iftop -i eth0

• Metric: Number of file operations, bytes read/written, network bytes sent/received.

5. Regex Performance

• Matches and Time: Use rg --stats to get match counts and time:

  rg --stats -Pho "$ABS_URL_RE" "$OUTDIR/pretty" > /dev/null

  • Example output: 1000 matches in 0.234s.
• Per-Pattern Metrics: Extract from $OUTDIR/regex_stats.txt when -r or -b is used.
• Metric: Matches per second, total regex processing time.

6. System Calls

• Count System Calls: Use strace to count file and network operations:

  strace -c -o strace_summary.txt ./js_all_in_one_recon.sh -f js_files.txt

• Metric: Number of open, read, write, connect calls.

7. Output Size

• File Sizes: Measure output file sizes to ensure efficiency:

  du -sh "$OUTDIR"/*

• Metric: Size (MB) of absolute_urls.txt, suspected_secrets.txt, etc.

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Running the Benchmark

1. Prepare Input: Create a test file with 100–1000 JS URLs:

   for i in {1..100}; do echo "https://example.com/script$i.js"; done > js_files.txt

2. Run with Benchmarking:

   ./js_all_in_one_recon.sh -f js_files.txt -d example.com -s 200,404 -c 20 -o json -p -C -r -b

3. Monitor Real-Time:
   • CPU/Memory: htop or top -b -n 1.
   • Disk I/O: sudo iotop -o.
   • Network: sudo iftop -i eth0.
4. Collect Metrics:
   • Check $OUTDIR/benchmark_summary.json for a summary.
   • Review $OUTDIR/regex_stats.txt for regex performance.
   • Analyze strace_summary.txt for system calls.

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Expected Metrics (Hypothetical)

Based on a test with 100 URLs, 4-core CPU, 8GB RAM, SSD, and /dev/shm:

• Total Time: ~60–120s (depends on network speed).
• Download: 30–60s, 50–80% CPU, 200–500MB memory.
• Prettify: 10–20s, 60–90% CPU, 300–600MB memory.
• Extract: 5–15s, 70–100% CPU, 100–300MB memory (optimized regexes reduce this).
• Probe: 15–30s, 40–70% CPU, 200–400MB memory.
• I/O Ops: ~200–500 file operations, ~50–200MB written.
• Regex Matches: 1000–5000 matches, ~0.1–0.5s per pattern.

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Analysis and Optimization

• Bottlenecks:
  • Network: Download and probe steps are network-bound. Optimize with --retry and -timeout in curl/httpx.
  • Regex: Extraction is CPU-intensive. The pre-filtering (rg -l) and split patterns reduce this significantly.
  • I/O: Using /dev/shm minimizes disk I/O, but large URL lists may still hit memory limits.
• Improvements:
  • Increase CONCURRENCY if network latency is low.
  • Further split regexes if regex_stats.txt shows slow patterns.
  • Cache more aggressively with -C for repeated runs.

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Visualizing Metrics

To visualize the benchmark results, you can generate a chart from $OUTDIR/benchmark_summary.json. Since you didn’t explicitly request a chart, I’ll provide the command to generate one if desired:

jq -r '.steps[] | [.step, .time_s] | @csv' "$OUTDIR/benchmark_summary.json" > "$OUTDIR/benchmark_times.csv"

Then, use a tool like Python’s matplotlib or a spreadsheet to plot step times. If you want a Chart.js chart, run:

# Example Chart.js config (run in a JavaScript environment or use a tool to render)
{
  type: 'bar',
  data: {
    labels: ['download', 'prettify', 'extract', 'mapping', 'probe', 'cookie_check', 'report'],
    datasets: [{
      label: 'Time (seconds)',
      data: $(jq -r '.steps[] | .time_s' "$OUTDIR/benchmark_summary.json" | tr '\n' ','),
      backgroundColor: ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2']
    }]
  },
  options: { scales: { y: { beginAtZero: true, title: { display: true, text: 'Time (s)' } } } }
}

This script and benchmarking setup provide a comprehensive way to measure and optimize performance. Run it with -b and analyze $OUTDIR/benchmark_summary.json to identify bottlenecks. Let me know if you need help interpreting results or further optimizations!