Python os.fdatasync Function

Last modified April 11, 2025

This comprehensive guide explores Python’s os.fdatasync function, which forces write of file data to disk. We’ll cover synchronization methods, performance implications, and practical file I/O examples.

Basic Definitions

The os.fdatasync function forces write of file data to disk without necessarily writing metadata. It’s similar to fsync but more efficient.

Key differences: fdatasync only flushes data, not metadata (like file size). This makes it faster than fsync for cases where metadata doesn’t change.

Basic File Synchronization

This example shows basic usage of fdatasync to ensure data is written to disk after file operations. This is crucial for critical data integrity.

basic_sync.py

import os

Open file for writing

with open(“important.log”, “w”) as f: f.write(“Critical transaction data\n”) # Force data to disk os.fdatasync(f.fileno()) print(“Data safely written to disk”)

Verify the write

with open(“important.log”, “r”) as f: print(f.read())

The example writes data to a file and immediately forces it to disk using fdatasync. This ensures data persists even if the system crashes.

Note we use fileno() to get the file descriptor from the file object before passing it to fdatasync.

Comparing fsync and fdatasync

This example demonstrates the performance difference between fsync and fdatasync by timing multiple write operations with each synchronization method.

sync_comparison.py

import os import time

def test_sync(method, filename): start = time.time() with open(filename, “w”) as f: for i in range(1000): f.write(f"Line {i}\n") if method == “fsync”: os.fsync(f.fileno()) else: os.fdatasync(f.fileno()) return time.time() - start

Test both methods

fsync_time = test_sync(“fsync”, “fsync_test.log”) fdatasync_time = test_sync(“fdatasync”, “fdatasync_test.log”)

print(f"fsync time: {fsync_time:.3f} seconds") print(f"fdatasync time: {fdatasync_time:.3f} seconds") print(f"Difference: {(fsync_time-fdatasync_time):.3f} seconds")

The test writes 1000 lines to files using each sync method. fdatasync is typically faster as it doesn’t flush metadata to disk.

The performance difference depends on filesystem and hardware, but fdatasync generally has less overhead than fsync.

Database Transaction Safety

This example shows how fdatasync can be used to ensure database transaction durability without the full overhead of fsync.

db_transaction.py

import os import sqlite3

def safe_transaction(db_file): conn = sqlite3.connect(db_file) cursor = conn.cursor()

# Create table if not exists
cursor.execute("CREATE TABLE IF NOT EXISTS data (id INTEGER PRIMARY KEY, value TEXT)")

# Perform transaction
cursor.execute("INSERT INTO data (value) VALUES ('important')")
conn.commit()

# Force data to disk
db_fd = os.open(db_file, os.O_RDWR)
os.fdatasync(db_fd)
os.close(db_fd)

print("Transaction committed and synced")
conn.close()

safe_transaction(“test.db”)

The example performs a SQLite transaction and ensures the data is flushed to disk using fdatasync. This provides durability with better performance.

Note we directly open the file descriptor for the database file to sync it, as SQLite’s Python interface doesn’t expose the file descriptor.

Log File Rotation with Sync

This example demonstrates proper log file rotation with data synchronization to prevent data loss during the rotation process.

log_rotation.py

import os import time from datetime import datetime

LOG_FILE = “app.log” MAX_SIZE = 1024 # 1KB for demonstration

def write_log(message): with open(LOG_FILE, “a”) as f: f.write(f"{datetime.now()}: {message}\n") os.fdatasync(f.fileno())

def rotate_logs(): if os.path.exists(LOG_FILE) and os.path.getsize(LOG_FILE) > MAX_SIZE: timestamp = int(time.time()) os.rename(LOG_FILE, f"app_{timestamp}.log") # Create new empty log file with open(LOG_FILE, “w”) as f: os.fdatasync(f.fileno())

Test log writing and rotation

for i in range(50): write_log(f"Test message {i}") rotate_logs() time.sleep(0.1)

The example writes log messages and rotates the log file when it exceeds size. Each write is synchronized with fdatasync to ensure no data loss.

The rotation process also syncs the new empty file to ensure filesystem metadata is consistent after rotation.

Configuring File Buffering

This example shows how to combine file buffering settings with fdatasync to optimize performance while maintaining data integrity.

buffering.py

import os

def write_with_buffering(filename, buffer_size): # Open with specific buffer size with open(filename, “w”, buffering=buffer_size) as f: for i in range(100): f.write(f"Data chunk {i}\n") # Sync at specific intervals if i % 10 == 0: os.fdatasync(f.fileno()) # Final sync os.fdatasync(f.fileno())

Test different buffer sizes

for size in [0, 1, 1024, 4096]: write_with_buffering(f"output_{size}.txt", size) print(f"Written with buffer size {size}")

The example tests different buffer sizes with periodic synchronization. Smaller buffers reduce data loss risk but may impact performance.

The optimal buffer size depends on your specific use case and performance requirements versus data integrity needs.

Error Handling

This example demonstrates proper error handling when using fdatasync, as the operation can fail for various reasons like disk full or I/O errors.

error_handling.py

import os import errno

def safe_write(filename, data): try: with open(filename, “w”) as f: f.write(data) try: os.fdatasync(f.fileno()) print(“Data successfully written and synced”) except OSError as e: if e.errno == errno.EIO: print(“I/O error during sync - data may be corrupted”) elif e.errno == errno.ENOSPC: print(“No space left on device”) else: print(f"Sync failed: {e}") except IOError as e: print(f"File operation failed: {e}")

Test with good and bad scenarios

safe_write(“good.txt”, “This should work”) safe_write("/full/device.txt", “This should fail if device is full”)

The example shows comprehensive error handling for both file operations and synchronization. Different error types are handled specifically.

Proper error handling is crucial when dealing with disk synchronization as failures can indicate serious storage problems.

Platform Considerations

This example demonstrates platform-specific behavior of fdatasync and provides a cross-platform wrapper function.

cross_platform.py

import os import sys

def safe_sync(fd): “““Cross-platform file synchronization””” if hasattr(os, ‘fdatasync’): # Unix-like systems os.fdatasync(fd) else: # Windows or other platforms os.fsync(fd)

def write_data(filename): with open(filename, “w”) as f: f.write(“Cross-platform data\n”) safe_sync(f.fileno()) print(“Data written and synced”)

write_data(“data.txt”) print(f"Running on: {sys.platform}")

The example provides a cross-platform solution that uses fdatasync where available and falls back to fsync otherwise.

On Windows, fdatasync is not available, so fsync is used instead. The behavior is similar but not identical.

Performance Implications

• Disk I/O: fdatasync forces physical write operations

• Metadata: Doesn’t flush metadata like fsync does

• Battery life: Can impact mobile device battery life

• Throughput: Reduces write throughput significantly

• Latency: Increases write latency due to disk sync

Best Practices

• Use sparingly: Only for critical data that must persist

• Batch operations: Group writes and sync once if possible

• Error handling: Always handle potential sync failures

• Testing: Test behavior on your specific filesystem

• Alternatives: Consider write-through modes if available

Source References

• Python os.fdatasync Documentation

• Linux fdatasync(2) man page

Author

My name is Jan Bodnar, and I am a passionate programmer with extensive programming experience. I have been writing programming articles since 2007. To date, I have authored over 1,400 articles and 8 e-books. I possess more than ten years of experience in teaching programming.

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