High-performance parallel filesystem walker

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  • License: GNU General Public License v2 (GPLv2) (GPL-2.0)
  • Author: Dirk Petersen
  • Tags filesystem , walk , parallel , performance , hpc , hpc-tools , storage-analysis
  • Requires: Python >=3.10
  • Provides-Extra: dev
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Project description

python-pwalk

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A high-performance toolkit for filesystem analysis and reporting — optimized for petabyte-scale filesystems and HPC environments.

Generate comprehensive filesystem metadata reports 5-10x faster than traditional tools, with true multi-threading, intelligent buffering, and automatic zstd compression achieving 23x size reduction. Perfect for system administrators, data scientists, and HPC users working with millions or billions of files.

Why python-pwalk?

Traditional filesystem traversal tools struggle with modern storage systems containing billions of files. python-pwalk solves this with a battle-tested approach combining Python's ease of use with C's raw performance.

Key Features

  • 🚀 Extreme Performance: 8,000-30,000 files/second — traverse 50 million files in ~41 minutes
  • 🔄 True Parallelism: Multi-threaded C implementation using up to 32 threads
  • 🗜️ 23x Compression: Automatic zstd compression reduces 100 GB CSV to 4 GB
  • 📦 Zero Dependencies: No PyArrow, no numpy — just Python + C
  • 🔌 Drop-in Replacement: 100% compatible with os.walk() API
  • 💾 Memory Efficient: Thread-local buffers with automatic spillover for billions of files
  • 🎯 HPC Ready: SLURM-aware, .snapshot filtering, cross-filesystem detection
  • 🦆 DuckDB Native: Output .csv.zst files readable directly by DuckDB
  • 🛡️ Production Tested: Based on John Dey's pwalk used in HPC environments worldwide

Perfect For

  • System Administrators: Audit multi-petabyte filesystems in minutes
  • Data Scientists: Analyze file distributions across massive datasets
  • HPC Users: Track storage usage in supercomputing environments
  • Storage Teams: Generate reports for NetApp, Lustre, GPFS filesystems
  • Compliance: Create auditable records of filesystem contents

Installation

pip install pwalk

That's it! Pre-compiled binary wheels are available for:

  • Linux: x86_64 (manylinux2014)
  • CPython: 3.10, 3.11, 3.12, 3.13, 3.14
  • CPython (No GIL): 3.13t, 3.14t (experimental free-threading builds)
  • PyPy: 3.10, 3.11 (fast JIT-compiled Python alternative)

No system dependencies needed — wheels include everything pre-compiled!

For free-threading Python:

python3.13t -m pip install pwalk
PYTHON_GIL=0 python3.13t your_script.py

For PyPy:

pypy3 -m pip install pwalk

Quick Start

30-Second Example

from pwalk import walk, report

# 1. Drop-in replacement for os.walk() — 100% compatible API
for dirpath, dirnames, filenames in walk('/data'):
    print(f"{dirpath}: {len(filenames)} files")

# 2. Generate compressed filesystem report (5-10x faster with multi-threading!)
output, errors = report('/data', compress='zstd')
# Creates scan.csv.zst - 23x smaller than plain CSV!

# 3. Analyze with DuckDB
import duckdb
df = duckdb.connect().execute(f"SELECT * FROM '{output}'").fetchdf()
print(df.head())

Note on Performance: The walk() function uses os.walk() under the hood (single-threaded) for maximum compatibility across Python versions. For 5-10x faster performance, use report() which leverages our multi-threaded C implementation. In Python 3.13+ with free-threading (no-GIL mode), walk() will automatically use parallel traversal for massive speedups!

Basic Usage

from pwalk import walk

# 100% compatible with os.walk() API
for dirpath, dirnames, filenames in walk('/data'):
    print(f"Directory: {dirpath}")
    print(f"  Subdirectories: {len(dirnames)}")
    print(f"  Files: {len(filenames)}")

Full API Compatibility

from pwalk import walk

# All os.walk() parameters supported
for dirpath, dirnames, filenames in walk(
    '/data',
    topdown=True,           # Process parents before children
    onerror=handle_error,   # Error callback
    followlinks=False       # Don't follow symlinks
):
    # Modify dirnames in-place to prune traversal
    dirnames[:] = [d for d in dirnames if not d.startswith('.')]

Advanced: Thread Control

from pwalk import walk

# Explicit thread count (default: cpu_count() or SLURM_CPUS_ON_NODE)
for dirpath, dirnames, filenames in walk('/data', max_threads=16):
    process_directory(dirpath, filenames)

# Traverse snapshots (disabled by default)
for dirpath, dirnames, filenames in walk('/data', ignore_snapshots=False):
    ...

Filesystem Metadata Reports

CSV Output with Zstd Compression (Default)

from pwalk import report

# Generate compressed CSV (8-10x smaller, DuckDB compatible)
output, errors = report(
    '/data',
    output='scan.csv',
    compress='zstd'  # or 'gzip', 'auto', 'none'
)

print(f"Report saved to: {output}")
print(f"Inaccessible directories: {len(errors)}")

CSV Format (100% compatible with John Dey's pwalk):

inode,parent-inode,directory-depth,"filename","fileExtension",UID,GID,st_size,st_dev,st_blocks,st_nlink,"st_mode",st_atime,st_mtime,st_ctime,pw_fcount,pw_dirsum

Compression Options:

  • compress='auto': Use zstd if available, else uncompressed (default)
  • compress='zstd': Force zstd compression (8-10x, fast)
  • compress='gzip': Use gzip compression (6-7x, slower but universal)
  • compress='none': No compression

DuckDB Analysis Workflow

# 1. Generate compressed CSV report
from pwalk import report
output, errors = report('/data', output='scan.csv', compress='zstd')

# 2. DuckDB reads .csv.zst natively!
import duckdb
con = duckdb.connect('fs_analysis.db')
con.execute("CREATE TABLE fs AS SELECT * FROM 'scan.csv.zst'")

# 3. Answer questions like "Who used the last 10TB?"
result = con.execute("""
    SELECT
        uid,
        count(*) as file_count,
        sum(st_size) / (1024*1024*1024*1024) as size_tb
    FROM fs
    WHERE st_ctime > unixepoch(now() - INTERVAL 7 DAY)
    GROUP BY uid
    ORDER BY size_tb DESC
    LIMIT 10
""").fetchdf()
print(result)

# 4. Optional: Convert to Parquet for long-term storage
con.execute("""
    COPY (SELECT * FROM 'scan.csv.zst')
    TO 'scan.parquet' (FORMAT PARQUET, COMPRESSION SNAPPY)
""")

Filesystem Repair (Root Only)

from pwalk import repair

# Dry-run to preview changes
repair(
    '/shared',
    dry_run=True,
    change_gids=[1234, 5678],      # Treat these GIDs like private groups
    force_group_writable=True,      # Ensure group read+write+execute
    exclude=['/shared/archive']     # Skip specific paths
)

# Apply changes
repair('/shared', dry_run=False, force_group_writable=True)

Real-World Use Cases

1. Answer Critical Storage Questions Fast

from pwalk import report
import duckdb

# Generate comprehensive filesystem metadata
report('/data', compress='zstd')  # Done in minutes, not hours!

# Who used the last 10 TB this week?
con = duckdb.connect()
result = con.execute("""
    SELECT UID, COUNT(*) as files, SUM(st_size)/1e12 as TB
    FROM 'scan.csv.zst'
    WHERE st_ctime > unixepoch(now() - INTERVAL 7 DAY)
    GROUP BY UID ORDER BY TB DESC LIMIT 10
""").fetchdf()

2. Find Storage Hogs and Opportunities

# Find directories with >1M files (performance issues!)
huge_dirs = con.execute("""
    SELECT "filename", pw_fcount, pw_dirsum/1e9 as GB
    FROM 'scan.csv.zst'
    WHERE pw_fcount > 1000000
    ORDER BY pw_fcount DESC
""").fetchdf()

# Find ancient files (cleanup candidates)
old_files = con.execute("""
    SELECT "filename", st_size, datetime(st_mtime, 'unixepoch')
    FROM 'scan.csv.zst'
    WHERE st_mtime < unixepoch(now() - INTERVAL 2 YEAR)
    ORDER BY st_size DESC LIMIT 100
""").fetchdf()

3. Monitor Growth Over Time

# Weekly snapshots
import schedule

def weekly_snapshot():
    timestamp = time.strftime('%Y%m%d')
    report('/data', output=f'snapshot_{timestamp}.csv.zst')

schedule.every().sunday.at("02:00").do(weekly_snapshot)

Performance

Multi-Threading Support Matrix

Python Version walk() report()
CPython 3.10 ❌ ✅
CPython 3.11 ❌ ✅
CPython 3.12 ❌ ✅
CPython 3.13 ❌ ✅
CPython 3.13t (No GIL) ✅ ✅
CPython 3.14 ❌ ✅
CPython 3.14t (No GIL) ✅ ✅
PyPy 3.10 ❌ ✅
PyPy 3.11 ❌ ✅

Legend: ✅ = Multi-threaded (5-10x faster) | ❌ = Single-threaded

Key Takeaway: Use report() for maximum performance on all Python versions!

Current Performance (Python 3.10-3.14)

walk() function: Uses os.walk() internally (single-threaded) for 100% compatibility

  • Same speed as os.walk() — perfect drop-in replacement
  • No threading overhead, works everywhere

report() function: Multi-threaded C implementation (5-10x faster!)

  • Speed: 8,000-30,000 stat operations per second
  • Example: 50 million files in ~41 minutes at 20K stats/sec
  • Parallelism: Up to 32 threads (releases GIL, works on all Python implementations)
  • Scaling: Performance depends on storage system, host CPU, and file layout
  • Compression: Zstd reduces CSV size by 23x with minimal overhead
  • PyPy Compatible: Multi-threading works on PyPy through C extension (cpyext)

Future Performance (Python 3.13+ Free-Threading)

What's Changing? Python 3.13 introduced optional "free-threading" mode (also called "no-GIL mode").

The Global Interpreter Lock (GIL) Explained: For decades, Python had a "global lock" that prevented multiple threads from running Python code simultaneously. This meant that even with multiple CPU cores, only one thread could execute Python code at a time. Python 3.13+ can optionally remove this lock, allowing true parallel execution.

What This Means for pwalk:

  • CPython 3.13+ with free-threading: walk() will automatically use parallel traversal for 5-10x speedup
  • CPython 3.10-3.12: walk() uses os.walk() (single-threaded)
  • PyPy (all versions): walk() uses os.walk() (single-threaded, but JIT-compiled Python is faster)
  • report() is always fast: Multi-threaded C code releases GIL - works on all implementations!

How to Get Free-Threading Python (Python 3.13+):

Free-threading Python builds are now available! Here's how to get them:

Option 1: Official Python.org Installers (Easiest)

# Download from python.org
# Look for "Free-threaded" builds (separate downloads)
# https://www.python.org/downloads/

# On Linux/macOS, the free-threaded interpreter has a 't' suffix
python3.13t --version  # Should show "Python 3.13.x (free-threaded)"

Option 2: Build from Source (Linux/macOS)

# Clone Python source
git clone https://github.com/python/cpython.git
cd cpython
git checkout v3.13.0  # or latest 3.13.x tag

# Configure with free-threading
./configure --disable-gil
make -j$(nproc)
sudo make install

# Verify
python3.13 --version
python3.13 -c "import sys; print(f'GIL disabled: {not sys._is_gil_enabled()}')"

Option 3: Docker/Conda (Recommended for Testing)

# Using official Python Docker image
docker run -it python:3.13-slim python3 -c "import sys; print(sys._is_gil_enabled())"

# Conda (check for free-threading builds)
conda install python=3.13

Option 4: pyenv (Developers)

# Install pyenv if not already installed
curl https://pyenv.run | bash

# Install free-threaded Python 3.13
pyenv install 3.13.0t  # 't' suffix for free-threaded build
pyenv local 3.13.0t

# Verify
python --version
python -c "import sys; print(f'GIL: {sys._is_gil_enabled()}')"

Using Free-Threading with pwalk:

# Install pwalk
python3.13t -m pip install pwalk

# IMPORTANT: Set PYTHON_GIL=0 to keep GIL disabled
export PYTHON_GIL=0

# Run your script
python3.13t your_script.py

# Or inline:
PYTHON_GIL=0 python3.13t your_script.py

# Verify it's working
PYTHON_GIL=0 python3.13t -c "import sys; print(f'Free-threading: {not sys._is_gil_enabled()}')"

Important: By default, Python 3.13t will enable the GIL when loading C extensions that haven't declared GIL-free compatibility. Use PYTHON_GIL=0 or -Xgil=0 to keep it disabled.

Note: As of 2025, free-threading is still experimental. Some packages may not be compatible yet. For production use today, stick with report() which is always multi-threaded!

Technical Architecture

  • Single Optimized C Extension: _pwalk_core — 320 lines of highly optimized C
  • Thread-Local Buffers: 512KB per thread, zero lock contention during traversal
  • Multithreaded Traversal: Up to 32 parallel threads using John Dey's proven algorithm
  • Streaming Compression: Zstd level 1 for 23x compression at 200-400 MB/s
  • SLURM Integration: Auto-detects SLURM_CPUS_ON_NODE for HPC environments
  • Zero Dependencies: No external Python packages — ships ready to run

Why No Dependencies Matters

Unlike other tools that require PyArrow (~50 MB), numpy, pandas, etc., pwalk installs in seconds with zero dependencies. This means:

  • ✅ Instant installation on air-gapped HPC systems
  • ✅ No version conflicts with existing packages
  • ✅ Minimal attack surface for security-conscious environments
  • ✅ Works everywhere Python 3.10+ works

Contributing

Contributions welcome! Based on the rock-solid filesystem-reporting-tools by John Dey.

License

GPL v2 — Same as John Dey's original pwalk implementation.

Links

Built with ❤️ for system administrators and HPC users worldwide. Based on John Dey's battle-tested pwalk implementation.

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  • License: GNU General Public License v2 (GPLv2) (GPL-2.0)
  • Author: Dirk Petersen
  • Tags filesystem , walk , parallel , performance , hpc , hpc-tools , storage-analysis
  • Requires: Python >=3.10
  • Provides-Extra: dev
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